diff --git a/configs-npb-gapbs/restore_both.py b/configs-npb-gapbs/restore_both.py
index 3d1e4fd7d9..225031e072 100755
--- a/configs-npb-gapbs/restore_both.py
+++ b/configs-npb-gapbs/restore_both.py
@@ -185,7 +185,7 @@ def run():
     for interval_number in range(100):
         print("Interval number: {}".format(interval_number))
         exit_event = m5.simulate(10_000_000_000)  # 10 ms
-        # m5.stats.dump()
+        #m5.stats.dump()
 
         if exit_event.getCause() != "simulate() limit reached":
             if (
diff --git a/data-plots.ipynb b/data-plots.ipynb
deleted file mode 100644
index 84a5a7818e..0000000000
--- a/data-plots.ipynb
+++ /dev/null
@@ -1,2874 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import sys\n",
-    "from matplotlib import pyplot as plt\n",
-    "import os\n",
-    "import statistics\n",
-    "\n",
-    "cmap = plt.get_cmap('Set1')\n",
-    "\n",
-    "Stats = ['simSeconds ',\n",
-    "'hostSeconds ',\n",
-    "'system.mem_ctrl.readReqs ',\n",
-    "'system.mem_ctrl.writeReqs ',\n",
-    "'system.mem_ctrl.servicedByWrQ ',\n",
-    "'system.mem_ctrl.mergedWrBursts ',\n",
-    "'system.mem_ctrl.numTotHits ',\n",
-    "'system.mem_ctrl.numTotMisses ',\n",
-    "'system.mem_ctrl.numColdMisses ',\n",
-    "'system.mem_ctrl.numHotMisses ',\n",
-    "'system.mem_ctrl.numRdMissClean ',\n",
-    "'system.mem_ctrl.numRdMissDirty ',\n",
-    "'system.mem_ctrl.numRdHit ',\n",
-    "'system.mem_ctrl.numWrMissClean ',\n",
-    "'system.mem_ctrl.numWrMissDirty ',\n",
-    "'system.mem_ctrl.numWrHit ',\n",
-    "'system.mem_ctrl.numRdHitDirty ',\n",
-    "'system.mem_ctrl.numRdHitClean ',\n",
-    "'system.mem_ctrl.numWrHitDirty ',\n",
-    "'system.mem_ctrl.numWrHitClean ',\n",
-    "'system.o3Cpu0.thread_0.numInsts ',\n",
-    "'system.o3Cpu1.thread_0.numInsts ',\n",
-    "'system.o3Cpu2.thread_0.numInsts ',\n",
-    "'system.o3Cpu3.thread_0.numInsts ',\n",
-    "'system.o3Cpu4.thread_0.numInsts ',\n",
-    "'system.o3Cpu5.thread_0.numInsts ',\n",
-    "'system.o3Cpu6.thread_0.numInsts ',\n",
-    "'system.o3Cpu7.thread_0.numInsts ',\n",
-    "'system.mem_ctrl.avgRdBWSys ',\n",
-    "'system.mem_ctrl.avgWrBWSys ',\n",
-    "'system.mem_ctrl.avgORBLen ',\n",
-    "'system.far_mem_ctrl.avgRdBWSys ',\n",
-    "'system.far_mem_ctrl.avgWrBWSys ',\n",
-    "'system.loc_mem_ctrl.avgRdBWSys ',\n",
-    "'system.loc_mem_ctrl.avgWrBWSys ',\n",
-    "'system.loc_mem_ctrl.dram.readBursts ',\n",
-    "'system.loc_mem_ctrl.dram.writeBursts ',\n",
-    "'system.loc_mem_ctrl.dram_2.readBursts ',\n",
-    "'system.loc_mem_ctrl.dram_2.writeBursts ',\n",
-    "'system.far_mem_ctrl.dram.readBursts ',\n",
-    "'system.far_mem_ctrl.dram.writeBursts ',\n",
-    "'system.loc_mem_ctrl.dram.avgRdBW ',\n",
-    "'system.loc_mem_ctrl.dram.avgWrBW ',\n",
-    "'system.loc_mem_ctrl.dram_2.avgRdBW ',\n",
-    "'system.loc_mem_ctrl.dram_2.avgWrBW ',\n",
-    "'system.far_mem_ctrl.dram.avgRdBW ',\n",
-    "'system.far_mem_ctrl.dram.avgWrBW ',\n",
-    "'system.loc_mem_ctrl.dram.busUtil ',\n",
-    "'system.loc_mem_ctrl.dram.busUtilRead ',\n",
-    "'system.loc_mem_ctrl.dram.busUtilWrite ',\n",
-    "'system.loc_mem_ctrl.dram_2.busUtil ',\n",
-    "'system.loc_mem_ctrl.dram_2.busUtilRead ',\n",
-    "'system.loc_mem_ctrl.dram_2.busUtilWrite ',\n",
-    "'system.far_mem_ctrl.dram.busUtil ',\n",
-    "'system.far_mem_ctrl.dram.busUtilRead ',\n",
-    "'system.far_mem_ctrl.dram.busUtilWrite ',\n",
-    "'system.far_mem_ctrl.dram.bytesRead  ',\n",
-    "'system.far_mem_ctrl.dram.bytesWritten  ',\n",
-    "'system.loc_mem_ctrl.dram.bytesRead  ',\n",
-    "'system.loc_mem_ctrl.dram.bytesWritten  ',\n",
-    "'system.loc_mem_ctrl.dram_2.bytesRead  ',\n",
-    "'system.loc_mem_ctrl.dram_2.bytesWritten  ',\n",
-    "'system.mem_ctrl.avgTimeTagCheckRes ',\n",
-    "'system.mem_ctrl.avgTimeTagCheckResRd ',\n",
-    "'system.mem_ctrl.avgTimeTagCheckResWr ',\n",
-    "'system.mem_ctrl.avgPktRespTimeRd ',\n",
-    "'system.mem_ctrl.avgPktRespTimeWr ',\n",
-    "'system.mem_ctrl.avgPktORBTime ',\n",
-    "'system.mem_ctrl.avgPktORBTimeRd ',\n",
-    "'system.mem_ctrl.avgPktORBTimeWr ',\n",
-    "'system.mem_ctrl.avgTimeInLocRead ',\n",
-    "'system.mem_ctrl.avgTimeInLocWrite ',\n",
-    "'system.mem_ctrl.avgTimeInFarRead ',\n",
-    "'system.mem_ctrl.missRatio ',\n",
-    "'system.loc_mem_ctrl.dram.actDelayedDueToTagAct ',\n",
-    "'system.loc_mem_ctrl.noCandidBSlot ',\n",
-    "'system.loc_mem_ctrl.foundCandidBSlot ',\n",
-    "'system.loc_mem_ctrl.foundCandidBSlotRH ',\n",
-    "'system.loc_mem_ctrl.foundCandidBSlotRMC ',\n",
-    "'system.loc_mem_ctrl.foundCandidBSlotRMD ',\n",
-    "'system.loc_mem_ctrl.dram.readMC',\n",
-    "'system.loc_mem_ctrl.dram.writeBurstsTC'\n",
-    "          ]\n",
-    "\n",
-    "dfCols = [\n",
-    "         'app',\n",
-    "         'simSeconds',\n",
-    "         'hostSeconds',\n",
-    "         'readReqs',\n",
-    "         'writeReqs',\n",
-    "         'servicedByWrQ',\n",
-    "         'mergedWrBursts',\n",
-    "         'numTotHits',\n",
-    "         'numTotMisses',\n",
-    "         'numColdMisses',\n",
-    "         'numHotMisses',\n",
-    "         'numRdMissClean',\n",
-    "         'numRdMissDirty',\n",
-    "         'numRdHit',\n",
-    "         'numWrMissClean',\n",
-    "         'numWrMissDirty',\n",
-    "         'numWrHit',\n",
-    "         'numRdHitDirty',\n",
-    "         'numRdHitClean',\n",
-    "         'numWrHitDirty',\n",
-    "         'numWrHitClean',\n",
-    "         'numInsts0',\n",
-    "         'numInsts1',\n",
-    "         'numInsts2',\n",
-    "         'numInsts3',\n",
-    "         'numInsts4',\n",
-    "         'numInsts5',\n",
-    "         'numInsts6',\n",
-    "         'numInsts7',\n",
-    "         'avgRdBWSys',\n",
-    "         'avgWrBWSys',\n",
-    "         'avgORBLen',\n",
-    "         'farAvgRdBWSys',\n",
-    "         'farAvgWrBWSys',\n",
-    "         'locAvgRdBWSys',\n",
-    "         'locAvgWrBWSys',\n",
-    "         'readBursts1',\n",
-    "         'writeBursts1',\n",
-    "         'readBursts2',\n",
-    "         'writeBursts2',\n",
-    "         'readBursts3',\n",
-    "         'writeBursts3',\n",
-    "         'loc1AvgRdBW',\n",
-    "         'loc1AvgWrBW',\n",
-    "         'loc2AvgRdBW',\n",
-    "         'loc2AvgWrBW',\n",
-    "         'farAvgRdBW',\n",
-    "         'farAvgWrBW',\n",
-    "         'loc1BusUtil',\n",
-    "         'loc1BusUtilRead',\n",
-    "         'loc1BusUtilWrite',\n",
-    "         'loc2BusUtil',\n",
-    "         'loc2BusUtilRead',\n",
-    "         'loc2BusUtilWrite',\n",
-    "         'farBusUtil',\n",
-    "         'farBusUtilRead',\n",
-    "         'farBusUtilWrite',\n",
-    "         'farBytesRead',\n",
-    "         'farBytesWritten',\n",
-    "         'loc1BytesRead',\n",
-    "         'loc1BytesWritten',\n",
-    "         'loc2BytesRead',\n",
-    "         'loc2BytesWritten',\n",
-    "         'avgTimeTagCheckRes',\n",
-    "         'avgTimeTagCheckResRd',\n",
-    "         'avgTimeTagCheckResWr',\n",
-    "         'avgPktRespTimeRd',\n",
-    "         'avgPktRespTimeWr',\n",
-    "         'avgPktORBTime',\n",
-    "         'avgPktORBTimeRd',\n",
-    "         'avgPktORBTimeWr',\n",
-    "         'avgTimeInLocRead',\n",
-    "         'avgTimeInLocWrite',\n",
-    "         'avgTimeInFarRead',\n",
-    "         'missRatio',\n",
-    "         'actDelayedDueToTagAct',\n",
-    "         'noCandidBSlot',\n",
-    "         'foundCandidBSlot',\n",
-    "         'foundCandidBSlotRH',\n",
-    "         'foundCandidBSlotRMC',\n",
-    "         'foundCandidBSlotRMD',\n",
-    "         'readBurstsMC',\n",
-    "         'writeBurstsTC'\n",
-    "\n",
-    "         ]\n",
-    "##########################################################\n",
-    "\n",
-    "def getStat(filename, stat, index):\n",
-    "    filename = os.path.join(filename).replace('\\\\','/')\n",
-    "    #print(stat)\n",
-    "    #print(filename)\n",
-    "    try:\n",
-    "        x = 0\n",
-    "        with open(filename) as f:\n",
-    "            readlines = f.readlines()\n",
-    "            for l in readlines:\n",
-    "                if stat in l and x < (index-1):\n",
-    "                    x = x+1\n",
-    "                elif stat in l and x == (index-1):\n",
-    "                    return l\n",
-    "            return 0.0 #for cases where stat was not found\n",
-    "    except: #for cases where the file was not found\n",
-    "        return 0.0\n",
-    "\n",
-    "##########################################################\n",
-    "\n",
-    "def creatDataFrame(dataDir, suite, index):\n",
-    "    app = []\n",
-    "    if suite == \"GAPBS\":\n",
-    "        app = ['bc', 'bfs', 'cc', 'pr', 'sssp', 'tc']\n",
-    "    if suite == \"NPB\":\n",
-    "        app = ['bt', 'cg', 'ft', 'is', 'lu', 'mg', 'sp', 'ua']\n",
-    "    rows = []\n",
-    "    i = 0\n",
-    "    for a in app:\n",
-    "        stats = [a]\n",
-    "        for stat in Stats:\n",
-    "            time_file_path = '{}/{}/stats.txt'.format(dataDir, a)\n",
-    "            ret_line = getStat(time_file_path, stat, index[i])\n",
-    "\n",
-    "            if ret_line != 0:\n",
-    "                #if ret_line=='nan' :\n",
-    "                #    stat_val = 0\n",
-    "                #else:\n",
-    "                stat_val = ret_line.split()[1]\n",
-    "            else:\n",
-    "                stat_val = 0\n",
-    "            stats.append(stat_val)\n",
-    "\n",
-    "        rows.append(stats)\n",
-    "        i = i+1\n",
-    "    df = pd.DataFrame(rows, columns= dfCols)\n",
-    "    df['totNumInsts'] = df['numInsts0'].astype(int)+df['numInsts1'].astype(int)+df['numInsts2'].astype(int)+df['numInsts3'].astype(int)+df['numInsts4'].astype(int)+df['numInsts5'].astype(int)+df['numInsts6'].astype(int)+df['numInsts7'].astype(int)\n",
-    "    df['totBW'] = (df['avgRdBWSys'].astype(float)+df['avgWrBWSys'].astype(float))/1000000000\n",
-    "    df['coldRate'] = (df['numColdMisses'].astype(float) / (df['numTotMisses'].astype(float)+df['numTotHits'].astype(float))) *100\n",
-    "    df['injRate'] = (df['readReqs'].astype(float) + df['writeReqs'].astype(float))*64/1000000000 / df['simSeconds'].astype(float)\n",
-    "    df['BIPS'] = (df['totNumInsts'].astype(float)/1000000000)/df['simSeconds'].astype(float)\n",
-    "    \n",
-    "    df['accAmp'] =  (df['farBytesRead'].astype(float) + df['farBytesWritten'].astype(float) +\n",
-    "                     df['loc1BytesRead'].astype(float) + df['loc1BytesWritten'].astype(float) + \n",
-    "                     df['loc2BytesRead'].astype(float) + df['loc2BytesWritten'].astype(float)) / (df['readReqs'].astype(float) * 64 + df['writeReqs'].astype(float) * 64)\n",
-    "    \n",
-    "    df['BWBloat'] =  (df['loc1AvgRdBW'].astype(float) + df['loc1AvgWrBW'].astype(float) +\n",
-    "                      df['loc2AvgRdBW'].astype(float) + df['loc2AvgWrBW'].astype(float) +\n",
-    "                      df['farAvgRdBW'].astype(float) + df['farAvgWrBW'].astype(float)) / ((df['avgRdBWSys'].astype(float) + df['avgWrBWSys'].astype(float)) / 1000000)\n",
-    "    \n",
-    "\n",
-    "    df['locMemReadBursts'] =  df['readBursts1'].astype(float) + df['readBurstsMC'].astype(float)\n",
-    "    df['locMemWriteBursts'] = df['writeBursts1'].astype(float) + df['writeBurstsTC'].astype(float)\n",
-    "    \n",
-    "    \n",
-    "    return df"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_cas = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_cas = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_cas = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_cas = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ram = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ram = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_ram = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ram = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "\n",
-    "df_gap22_orc = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_orc = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/oracle/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_orc = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_orc = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/oracle/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_noDC = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_noDC = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/noDC/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_noDC = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_noDC = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/noDC/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/tagProbOptRealImplOnSetAssoBranch/dramCacheController/newResults/rambusTagPr/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [7,5,7,6,5,15])\n",
-    "df_npbC_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/tagProbOptRealImplOnSetAssoBranch/dramCacheController/newResults/rambusTagPr/1GB_8GB_g22_nC/NPB\", \"NPB\",[3,6,3,6,11,8,8,7])\n",
-    "\n",
-    "df_gap25_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/tagProbOptRealImplOnSetAssoBranch/dramCacheController/newResults/rambusTagPr/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [4,4,5,4,4,7])\n",
-    "df_npbD_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/tagProbOptRealImplOnSetAssoBranch/dramCacheController/newResults/rambusTagPr/1GB_85GB_g25_nD/NPB\", \"NPB\",[7,4,2,3,9,5,7,1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['coldRate'].astype(float)\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['coldRate'].astype(float)\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['coldRate'].astype(float)\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['coldRate'].astype(float)\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,100])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*2,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "\n",
-    "offset = i*2+1\n",
-    "for i,app in enumerate(app_npb):\n",
-    "    plt.bar(offset+i*2+1,   npb_C_cas[i],     width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*2, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=10, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Cold Miss Rate\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,100])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*2,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "\n",
-    "offset = i*2+1\n",
-    "for i,app in enumerate(app_npb):\n",
-    "    plt.bar(offset+i*2+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*2, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=10, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Cold Miss Rate\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['missRatio'].astype(float)-df_gap22_cas['coldRate'].astype(float)\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['missRatio'].astype(float)-df_gap25_cas['coldRate'].astype(float)\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['missRatio'].astype(float)-df_npbC_cas['coldRate'].astype(float)\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['missRatio'].astype(float)-df_npbD_cas['coldRate'].astype(float)\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,100])\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*2,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "\n",
-    "offset = i*2+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*2+1,   npb_C_cas[i],     width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*2, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Hot Miss Rate (%)\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "#plt.ylim([0,55])\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*2,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "\n",
-    "offset = i*2+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*2+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*2, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Hot Miss Rate (%)\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['missRatio'].astype(float)\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['missRatio'].astype(float)\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['missRatio'].astype(float)\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['missRatio'].astype(float)\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,100])\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*2,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "\n",
-    "offset = i*2+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*2+1,   npb_C_cas[i],     width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*2, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Total Miss Rate (%)\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "#plt.ylim([0,55])\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*2,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "\n",
-    "offset = i*2+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*2+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*2, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Total Miss Rate (%)\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_ram['app']\n",
-    "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)*1000\n",
-    "gap_22_orc = df_gap22_orc['simSeconds'].astype(float)*1000\n",
-    "gap_22_noDC = df_gap22_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)*1000\n",
-    "gap_25_orc = df_gap25_orc['simSeconds'].astype(float)*1000\n",
-    "gap_25_noDC = df_gap25_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "app_npb = df_npbC_ram['app']\n",
-    "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)*1000\n",
-    "npb_C_orc = df_npbC_orc['simSeconds'].astype(float)*1000\n",
-    "npb_C_noDC = df_npbC_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)*1000\n",
-    "npb_D_orc = df_npbD_orc['simSeconds'].astype(float)*1000\n",
-    "npb_D_noDC = df_npbD_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_ram[i]/gap_22_noDC[i],   width=1, color=cmap(1), label='TDRAM' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_orc[i]/gap_22_noDC[i],   width=1, color=cmap(2), label='Oracle' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_ram[i]/npb_C_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_orc[i]/npb_C_noDC[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_ram[i]/gap_25_noDC[i],   width=1, color=cmap(1), label='TDRAM' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_orc[i]/gap_25_noDC[i],   width=1, color=cmap(2), label='Oracle' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_ram[i]/npb_D_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_orc[i]/npb_D_noDC[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=9, ncol=2)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['simSeconds'].astype(float)*1000\n",
-    "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)*1000\n",
-    "gap_22_noDC = df_gap22_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['simSeconds'].astype(float)*1000\n",
-    "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)*1000\n",
-    "gap_25_noDC = df_gap25_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['simSeconds'].astype(float)*1000\n",
-    "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)*1000\n",
-    "npb_C_noDC = df_npbC_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['simSeconds'].astype(float)*1000\n",
-    "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)*1000\n",
-    "npb_D_noDC = df_npbD_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i]/gap_22_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i]/gap_22_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i]/npb_C_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i]/npb_C_noDC[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i]/gap_25_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i]/gap_25_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i]/npb_D_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i]/npb_D_noDC[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['simSeconds'].astype(float)*1000\n",
-    "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)*1000\n",
-    "gap_22_orc = df_gap22_orc['simSeconds'].astype(float)*1000\n",
-    "gap_22_noDC = df_gap22_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['simSeconds'].astype(float)*1000\n",
-    "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)*1000\n",
-    "gap_25_orc = df_gap25_orc['simSeconds'].astype(float)*1000\n",
-    "gap_25_noDC = df_gap25_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['simSeconds'].astype(float)*1000\n",
-    "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)*1000\n",
-    "npb_C_orc = df_npbC_orc['simSeconds'].astype(float)*1000\n",
-    "npb_C_noDC = df_npbC_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['simSeconds'].astype(float)*1000\n",
-    "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)*1000\n",
-    "npb_D_orc = df_npbD_orc['simSeconds'].astype(float)*1000\n",
-    "npb_D_noDC = df_npbD_noDC['simSeconds'].astype(float)*1000\n",
-    "\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*4,   gap_22_cas[i]/gap_22_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*4+1, gap_22_ram[i]/gap_22_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "    plt.bar(i*4+2, gap_22_orc[i]/gap_22_noDC[i],   width=1, color=cmap(3), label='Oracle' if i==0 else None)\n",
-    "\n",
-    "offset = i*4+3\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*4+1,   npb_C_cas[i]/npb_C_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*4+2,   npb_C_ram[i]/npb_C_noDC[i],   width=1, color=cmap(2))\n",
-    "    plt.bar(offset+i*4+3,   npb_C_orc[i]/npb_C_noDC[i],   width=1, color=cmap(3))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4+1, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=8, ncol=3)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*4,   gap_25_cas[i]/gap_25_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*4+1, gap_25_ram[i]/gap_25_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "    plt.bar(i*4+2, gap_25_orc[i]/gap_25_noDC[i],   width=1, color=cmap(3), label='Oracle' if i==0 else None)\n",
-    "\n",
-    "offset = i*4+3\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*4+1,   npb_D_cas[i]/npb_D_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*4+2,   npb_D_ram[i]/npb_D_noDC[i],   width=1, color=cmap(2))\n",
-    "    plt.bar(offset+i*4+3,   npb_D_orc[i]/npb_D_noDC[i],   width=1, color=cmap(3))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4+1, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=8, ncol=3)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['totBW'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['totBW'].astype(float)\n",
-    "gap_22_noDC = (df_gap22_noDC['farAvgRdBWSys'].astype(float)+df_gap22_noDC['farAvgWrBWSys'].astype(float))/1000000000\n",
-    "\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['totBW'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['totBW'].astype(float)\n",
-    "gap_25_noDC = (df_gap25_noDC['farAvgRdBWSys'].astype(float)+df_gap25_noDC['farAvgWrBWSys'].astype(float))/1000000000\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['totBW'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['totBW'].astype(float)\n",
-    "npb_C_noDC = (df_npbC_noDC['farAvgRdBWSys'].astype(float)+df_npbC_noDC['farAvgWrBWSys'].astype(float))/1000000000\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['totBW'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['totBW'].astype(float)\n",
-    "npb_D_noDC = (df_npbD_noDC['farAvgRdBWSys'].astype(float)+df_npbD_noDC['farAvgWrBWSys'].astype(float))/1000000000\n",
-    "\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "#plt.ylim([0,2.5])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "#plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Observed BW at LLC (GB/s)\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "#plt.ylim([0,2.5])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "#plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Observed BW at LLC (GB/s)\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x1 = df_gap22_cas['app']\n",
-    "y1 = 100 * df_gap22_cas['numRdMissClean'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_gap22_cas['numRdMissDirty'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_gap22_cas['numWrMissClean'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_gap22_cas['numWrMissDirty'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,110])\n",
-    "\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*4, y1[i], width=3, color=cmap(1), label='Read/Clean' if i==0 else None)\n",
-    "    plt.bar(i*4, y2[i], bottom = y1[i], width=3, color=cmap(2), label='Read/Dirty' if i==0 else None)\n",
-    "    plt.bar(i*4, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3), label='Write/Clean' if i==0 else None)\n",
-    "    plt.bar(i*4, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4), label='Write/Dirty' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*4\n",
-    "x2 = df_npbC_cas['app']\n",
-    "y1 = 100 * df_npbC_cas['numRdMissClean'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_npbC_cas['numRdMissDirty'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_npbC_cas['numWrMissClean'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_npbC_cas['numWrMissDirty'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*4+offset, y1[i], width=3, color=cmap(1))\n",
-    "    plt.bar(i*4+offset, y2[i], bottom = y1[i], width=3, color=cmap(2))\n",
-    "    plt.bar(i*4+offset, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3))\n",
-    "    plt.bar(i*4+offset, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4, list(x1)+list(x2))\n",
-    "plt.axvline(x=offset-2, color='black')\n",
-    "\n",
-    "plt.ylabel(\"DRAM Cache Miss Rate (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=1)\n",
-    "plt.tight_layout()\n",
-    "#plt.savefig(\"../figures/cs1_all_mpki.pdf\")\n",
-    "\n",
-    "x1 = df_gap25_cas['app']\n",
-    "y1 = 100 * df_gap25_cas['numRdMissClean'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_gap25_cas['numRdMissDirty'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_gap25_cas['numWrMissClean'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_gap25_cas['numWrMissDirty'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,110])\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*4, y1[i], width=3, color=cmap(1), label='Read/Clean' if i==0 else None)\n",
-    "    plt.bar(i*4, y2[i], bottom = y1[i], width=3, color=cmap(2), label='Read/Dirty' if i==0 else None)\n",
-    "    plt.bar(i*4, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3), label='Write/Clean' if i==0 else None)\n",
-    "    plt.bar(i*4, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4), label='Write/Dirty' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*4\n",
-    "x2 = df_npbD_cas['app']\n",
-    "y1 = 100 * df_npbD_cas['numRdMissClean'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_npbD_cas['numRdMissDirty'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_npbD_cas['numWrMissClean'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_npbD_cas['numWrMissDirty'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*4+offset, y1[i], width=3, color=cmap(1))\n",
-    "    plt.bar(i*4+offset, y2[i], bottom = y1[i], width=3, color=cmap(2))\n",
-    "    plt.bar(i*4+offset, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3))\n",
-    "    plt.bar(i*4+offset, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4, list(x1)+list(x2))\n",
-    "plt.axvline(x=offset-2, color='black')\n",
-    "\n",
-    "plt.ylabel(\"DRAM Cache Miss Rate (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=1)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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/TCOxsnn2zerg58GssopN+b5renO6pIuAPwPv59ZHxNyCdzQzM2uhYkdQv22wnD/JYADDKxuOmZlZotiU78NaKxAzM7N8pY7F9ytJ3fKWN5Z0fmZRmZlZzSv1h7r7R8RbuYWIeBP4RiYRmZmZUXqC6iTp87kFSV2BzzexvZmZWYuU2s18IvCApBvS5eOAm7IJyczMrPShjn4taR6wT1r0i4i4L7uwzKwtjLzzgIrUM+XgqRWpx2pbqUdQpD/UrdiPdSV1AmYDr0TEgZL6ArcAm5CMWHF0/hQfZmZWW0odi29P4DLgi0BnoBPwbkRs2IK2TwUWAbk6fg1cEhG3SLoKOB6Y0IL6zcwqrlJHmeAjzWJK7SRxOTAaeA7oCpwAXFFuo5J6AQcA16bLIvnR7+3pJjfhAV/MzGpaqQmKiFgCdIqIjyLiBmC/FrT7O+BHwMfp8ibAWxGxJl1eBmxV6I6SxkqaLWn2ihUrWhCCmZlVs1IT1HuSOgNPSrpQ0rhm3PdTJB0IvBYRc8q5f0RcHRGDImJQz549y6nCzMzagVKTzNHptt8D3gW2Br5ZZpuDgZGSlpJ0ihgOXAp0k5S7JtYLeKXM+s3MrANoMkFJ6ilpx4h4KSJWR8Q7EXEeyfxQb5fTYET8OCJ6RUQf4AhgWkQcCUwHRqWbHQNMLqd+MzPrGIodQV0G9ChQ3p3kqKeSzgROl7SE5JrUdRWu38zM2pFi3cz7RcSDDQsj4iFJLe4CHhH1QH16+wVg95bWaWZmHUOxI6gNmli3biUDMTMzy1csQS2R9JlRyyXtD7yQTUhmZmbFT/GdBkyVdDjJ8EOQzKq7F3BghnGZmVmNa/IIKiKeA3YGZgB90r8ZwJci4tmsgzMzs9pVdCy+iHgfuKHYdmZmZpVU1mgQZmZmWSt5ug2z9uy1J06pXGUHV64qM2ucj6DMzKwqNXkElc6iW3AVEBHxpcqHZGZmVvwU38dAAH8E7gJWZR6RmZkZxbuZf5lkosIvkCSpXwI7kUzT/lLm0ZmZWc0qeg0qIp6JiPERsSvJUdTvgXGZR2ZmZjWtaC8+SVuRTItxCPAmSXK6I+O4zMysxhXrJDGDZMDY24DjgNfTVZ0ldY+INzKOz8zMalSxI6htSDpJnAiMzStXWr5tRnGZmVmNazJBpbPempmZtbpm/1BX0naSzpa0MIuAzMzMoMQEJWlLSeMkPQ4sTO93RKaRmZlZTSvWSWIsye+gtiLpKHE8MDkizmuF2MzMqo7HdWw9xTpJXA78Dfh2RMwGkBSZR2VmZjWvWILaAjgM+K2kzUmOotbNPCozM6t5xYY6ej0iroqIIcA+wFvAq5IWSfpVawRoZma1qeRefBGxLCJ+GxGDgJF44FgzM8tQ0QQlaS9JoyRtmi5/CTiPT/9wt2SStpY0XdLTkhZKOjUt7y7pfknPpf83Lqd+MzPrGJpMUJIuAq4HvglMlXQ+8BfgMWD7MttcA/wgInYE9gROlrQjcBbwQERsDzyQLpuZWY0q1kniAGCXiFidHtG8DPSPiKXlNhgRy4Hl6e1/SlpE0o39IGBoutlNQD1wZrntmJlZ+1bsFN/qiFgNEBFvAs+1JDk1JKkPsAvJEdlmafIC+AewWaXaMTOz9qfYEdS2kqaktwX0zVsmIkaW27CkLwCTgNMi4h1Ja9dFRDT2e6v0x8NjAXr37l1u82ZmVuWKJaiDGiz/phKNSlqXJDlNjIg/p8WvStoiIpZL2gJ4rdB9I+Jq4GqAQYMG+UfDZmYdVLHRzGfkbkvqmZataEmDSg6VrgMWRcTFeaumAMcAF6T/J7ekHbOOZuSdB1SsrikHT61YXWZZKdaLT5LGS1oJLAaelbRC0jktaHMwcDQwXNKT6d83SBLTvpKeA0aky2ZmVqOKneIbB9QBu0XEiwCStgUmSBoXEZc0t8GIeJjkelYh+zS3PrNa4UFKrdYU68V3NDA6l5wAIuIF4ChgTJaBmZlZbSuWoNaNiJUNC9PrUB401szMMlMsQX1Q5jozM7MWKXYNaoCkdwqUC+iSQTxmZmZA8W7mnVorEDMzs3wlT7dhZmbWmpygzMysKjlBmZlZVXKCMjOzquQEZWZmVckJyszMqpITlJmZVSUnKDMzq0pOUGZmVpWcoMzMrCo5QZmZWVVygjIzs6rkBGVmZlXJCcrMzKqSE5SZmVUlJygzM6tKTlBmZlaVnKDMzKwqOUGZmVlVqqoEJWk/SYslLZF0VlvHY2ZmbadqEpSkTsAVwP7AjsBoSTu2bVRmZtZW1mnrAPLsDiyJiBcAJN0CHAQ83aZRmdWQ1544pTIVHVyZaqy2KSLaOgYAJI0C9ouIE9Llo4E9IuJ7DbYbC4xNF3cAFrdCeD2Ale28De9DdbThfaiONrwP1dMGwDYR0bNhYTUdQZUkIq4Grm7NNiXNjohB7bkN70N1tOF9qI42vA/V00ZTquYaFPAKsHXecq+0zMzMalA1JajHge0l9ZXUGTgCmNLGMZmZWRupmlN8EbFG0veA+4BOwPURsbCNw8ppjVOKWbfhfaiONrwP1dGG96F62mhU1XSSMDMzy1dNp/jMzMzWcoIyM7Oq5ATVgKQ+kha0Zt2S9pa0UNKTkrpm0bZ9lqRukr6bcRuNPeenSVovy7YrTdIpkhZJejeLUV4kzax0nXl1/yurui07TlDV4UjgvyPiyxGxqq2DqZR0+Kpq1g3INEE14TSgXSUoksdqX+BPJMORVVREfKXSdVr75gRV2DqSJqbfFm+XtJ6k3STNlPSUpFmSNqhQ3acAhwO/SMu3kPRgejS1QNLe5TQiaYykeWm8f5C0maQ70uWnJLXowyA9MnimwOO0VNKvJc0FDiuxrvUlTU3jWiDpW5IukPR0ug+/Sbc7LF3/lKQH07JjJU2WVC/pOUnjm7EbFwDbpY/1RZLOlDQ/rf+CZj8ojSv0nG8JTJc0vSUVF3iet5P0aLof51fqyEHSVcC2wIvAMcBF6eO2XSXqT9v4V/q/Iu+BRtoYKunuvOXLJR1bZl2598CNkp5Nn+MRkh5JX4u7S+op6f70DMm1kl6S1KOMtgq9R5ZKujB9rmdJ6lfOfuTty4K85TMknSvpO5IeT9udpNY+6o8I/+X9AX2AAAany9cDPwJeAHZLyzYE1qlQ3WcANwKj0rIfAD9Nb3cCNiijnZ2AZ4Ee6XJ34FbgtLx6N8rgcToDWAr8qJl1fRO4Jm95G5IhrHK9TLul/+cDWzUoOxZYDmwCdAUWAIOasQ8L0tv7AzOB9XKPWYavp9zj1KOFdRd6nu8GRqfLJwH/qsR+pPUtJRn6Zu3rtZJ/uVgr8R5oou6hwN155ZcDx7bguV0D7EzyZX9O+vyKZBzRO9P6f5xuv1/6Wmj2817gPbJR+nzkHqcx+ftV5r4syFs+AzgX2CSv7Hzg+5V+3pv68xFUYS9HxCPp7f8Fvg4sj4jHASLinYhYU6G66xqsfxw4TtK5wM4R8c8y2hgO/CkiVqbxvpGWTUiXP4qIt8sJvoHG9uXWZtYzH9g3PfLam2QEkdXAdZIOBd5Lt3sEuFHSd0g+uHLuj4jXIzk9+mc++5iWYgRwQ0S8B2sfs0op9pyXq9DzvBfJKTiAP1aondZWifdAa3kxIuZHxMfAQuCBSD7N55N86NcBtwBExL3Am2W286n3SN779+a8/3uVWXdT+kt6SNJ8kksRO2XQRqOcoApr+OOwdzKs+1PLEfEg8FWSD+kbJY2pYNuV1ti+vNusSiKeBXYleROeD/yEZHT724EDgXvT7U4CfkYyJNYcSZsUiaNaVHt8VSXj98AaPv2516WF9b2fd/vjvOWPqeBACA3fI5LOya3K36wFTTT2uNwIfC8idgbOo+WPV7M4QRXWW1Lu28i3gUeBLSTtBiBpA0nlvvga1v1w/kpJ2wCvRsQ1wLUkL8rmmgYclvsAl9QdeAD4r3S5k6SNyow/X5P7UipJWwLvRcT/AheRfDhtFBH3AOOAAel220XEYxFxDrCCT8Zu3FdSdyU9IA8mOdIqxT+B3LXE+0m+ta+XttW9nH1pRKHHKb/tchV6nh8lOR0EyXBhWahE7I2q0HugMS8BO0r6vKRuwD4VrLuQR0iuMSPpa8DG5VRS4D2Se0y+lff/by2I81VgU0mbSPo8yRdDSJ7n5ZLWJTmCalVOUIUtBk6WtIjkBXUZyQvgMklPkXyYlftNomHdExqsHwo8JemJtM1Lm9tAJENE/RKYkcZ7MXAqMCw9VJ9DZXphFduXUu0MzJL0JDCe5Jva3ZLmkXyYn55ud1F6QXgByfWip9LyWcAkYB4wKSJml9JoRLwOPJLWtw/J2I+z0zjOKHNfCin0OF0N3NuSThKNPM+nAaenj10/oBKnchu6BfihpCcq2Ukiz1Ba+B5oTES8DNxGcq3yNuCJStXdiPOAr6WvscOAf5Ak+OZq+B45Py3fOH2uTyX5MleWiPgQ+DnJe+l+4Jl01dnAYySJ9pnC986OhzqyskjqQ3JRtn8bx3EsSaeI7xXbthakR4CrIiIkHUHSYeKgto6rVqVHIx9FMtboXsCEiPhyhepeSvLab435mtpE1QwWa2YVMRC4XJKAt4D/bNtwal5v4DZJnwM+AL7TxvG0Kz6CMjOzquRrUGZmVpWcoMzMrCo5QZmZWVVygjIzs6rkBFXDlAwg+0dJL0iaI+lvkg7JW/87Sa+kPZByZcdKWqFkIM+n02GHGpYvVDp4bLpuT0mPpesWpUPYFIpnoqTFSgbDvD79cSCSjlQyIOp8JQP2Dsj0gbGaIikk/TZv+Yzca1TJgKmv6JOBa0cWKH9G0oT890mD+jeXdIuk59P32T2S/q1Vdq6dc4KqUWk35DuBByNi24gYSDLyQK90/eeAQ4CXgSEN7n5r+luOocCvJG2WXx4RO5F0qc39yv0mYGx6n/4kP5AsZCLw7yQ/SuwKnJCWvwgMSYdb+QXJj1zNKuV94FA1Psr4Jelr9zDg+rxElCvfkeQ12/B9knuf3QHUR8R26fvsx8BmDbe1z3KCql3DgQ8i4qpcQUS8FBGXpYtDSQa/nACMLlRBRLwGPE8y+vhaSoaBWp9PBsbclGTE8dxAtU83Ut89kSL5RXuvtHxmROTqejRXblYha0i+9DQ5EkNELEq3bZjIOpOMLFNoINhhwIcN3mdPRcRDLYq4RjhB1a6dgLlNrB9NMkLyHcABudNt+SRtSzJH0JK06FvpUCyvkEz9cFdafgmwWMl8VCdKanKYqLSto0kHiW3geOD/mrq/WRmuAI5UE2NUStqDZBDYFWnRuPT1vhx4NiKeLHC3/iRDi1kZnKAMAElXKJmU7HFJnYFvAHdGxDskY3F9PW/zXCK6GTgxb2qK3Km/zUlGXf4hQET8HBgE/IVksNRCiSfflSSnHj/1LVPSMJIEdWbZO2pWQPo6/z1wSoHVuUT0G+Bb8cnoBrlTfJsC66dDS1kFOUHVroXkjRIdESeTDJjakyQZdQPmp+N91fHp03y5a017RMQdDStO38B3kYxKnit7PiImpG0MUDJq8n3pReZrc9spmRG3J58MEJsr/xLJyNYHpYO8mlXa70i+AK3foPyS9PW+d6FTc+lAq/cCX5W0dfqaflLSSSTvs4FZB95ROUHVrmlAF0n/lVeWm855NHBCRPSJiD5AX5IpLZoz3XMdyfUpJB2QXiwG2B74CHgrIr6evvFPSLc7gSQ5jk4ngCMt700yEeHR6bw4ZhWXngm4jSRJlSx9bQ8Gno+Il9PX9JfT607TgM9LGpu3/ZdUwWnsOzInqBqVHuUcDAyR9KKkWSS97caTTE09NW/bd0mmvfiPItV+K/3mOA/YhaTHHSTXkxanp0n+ABwZER8VuP9VJL2b/pbWk5uU7RySKd2vTMtLmk7DrAy/5bOdIBqTO/W3gGSG5ysbbpC+zw4BRqTdzBcC/00y7YYV4cFizcysKvkIyszMqpITlJmZVSUnKDMzq0pOUGZmVpWcoMzMrCo5QZmZWVVygjIzs6r0/wFCX7/X2EKJcQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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evXc0MzNrpkJHUL+qs5w/yWAAw8objpmZWaLQlO9DWyoQMzOzfMWOxfdzSV3zlreSdHFmUZmZWdUr9oe6X46IN3ILEfE68JVMIjIzM6P4BNVB0qdzC5K6AJ9uZHszM7NmKbab+URgmqSb0+WTgVuzCcnMzKz4oY5+IekZ4OC06L8j4sHswjIzs2pX7BEU6Q91y/ZjXUkdgDnAqxFxuKQ+wO3A1iQjVpyQP8WHmZlVl2LH4jsAuAr4LNAJ6AC8HRFbNKPts4FFQK6OXwBXRMTtkq4FTgEmNKP+qrFy/lnlqWhkeaoxMyuHYjtJXA2MBl4EugCnAteU2qiknsBhwA3pskh+9HtXusmt+OPSzKyqFZugiIglQIeI+CAibgYObUa7vwa+B3yYLm8NvBERG9Ll5cCO9d1R0lhJcyTNWbVqVTNCMDOzSlZsgnpHUifgKUmXShrXhPt+jKTDgZURMbeU+0fEdRExMCIG9ujRo5QqzMysDSi2k8QJJAnpTJJpN3YCvlpim4OAEZK+AnQmuQZ1JdBV0ibpUVRP4NUS6zezEnmyP6skjR4FSeohaY+IeDki1kfEmxFxEcn8UGtLaTAifhARPSOiN3AsMD0ijgNmAKPSzU4EJpdSv5mZtQ+FTtNdBXSvp7wbyVFPOZ0PnCtpCck1qRvLXL+ZmbUhhU7x9Y2Ih+sWRsQjkprdBTwiaoHa9PZSYL/m1mlmZu1DoQS1eSPrOpYzkPaqXOf0wef1zay6FEpQSyR9JSLuzy+U9GVgaXZhmZlVL3dWSRRKUOcAUyUdQzL8ECSz6h4IHJ5hXGZmVuUKzaj7oqS9gK8D/dLimcBpEbE+6+DM2hJ/6zUrr4K/g4qId4GbC21nZmZWTiWNBmFmZpY1JygzM6tITlBmZlaRGr0Glc6iW+8qICLiP8ofkpmZWeFOEh8CAfwBuBdYl3lEZmZmFDjFFxGfI5mo8DMkSepnwJ4k07S/nHl0ZmZWtQpeg4qI5yNifETsQ3IU9TuSKTfMzMwyU/B3UJJ2JJkW40jgdZLkdHfGcZmZWZUr1EliJsmAsXcCJwNr0lWdJHWLiH9mHJ+ZmVWpQkdQO5N0kjgNGJtXrrR8l4ziMiurlfPPKl9lI8tXlZk1rNBYfL1bKA4zM7OPafIPdSXtKukCSQuzCMjMzAyKTFCSdpA0TtKTwML0fsdmGpmZmVW1RhOUpLGSZpBMy741cAqwIiIuiogFLRCfmZlVqUKdJK4G/gp8PSLmAEiKzKNqR3xx3sysNIUS1PbA0cCvJG1H0t28Y+ZRmZlZ1Ss01NGaiLg2IgYDBwNvAK9JWiTp5y0RoJmZVaeie/FFxPKI+FVEDARG4IFjzcwsQwUTlKQDJY2StE26/B/ARXz8h7tFk7STpBmSnpO0UNLZaXk3SQ9JejH9v1Up9ZuZWftQqBffZcBNwFeBqZIuBv4MPAHsVmKbG4DvRMQewAHAGZL2AL4PTIuI3YBp6bKZmVWpQp0kDgP2joj16RHNK0C/iFhWaoMRsQJYkd7+l6RFwI7AEcCQdLNbSbq2n19qO2Zm1rYVOsW3PiLWA0TE68CLzUlOdUnqDexNckS2bZq8AP4BbFuudszMrO0pdAS1i6Qp6W0BffKWiYgRpTYs6TPAJOCciHhT0sZ1EREN/d5K0ljS61+9evUqtXkzM6twhRLUEXWWf1mORiV1JElOEyPiT2nxa5K2j4gVkrYHVtZ334i4DrgOYODAgf7RsJlZO1VoNPOZuduSeqRlq5rToJJDpRuBRRFxed6qKcCJwCXp/8nNacfMzNq2QhMWCrgQ+DbJ9SpJ2gBcFRE/LbHNQcAJwAJJT6VlPyRJTHdKOgV4GTimxPrNzDIz4p7DylbXlJFTy1ZXe1ToFN84oAbYNyL+BiBpF2CCpHERcUVTG4yIR0muZ9Xn4KbWZ2Zm7VOhXnwnAKNzyQkgIpYCxwNjsgzMzMyqW6EE1TEiVtctTK9DedBYMzPLTKEE9V6J68zMzJql0DWo/pLerKdcQOcM4jEzMwMKdzPv0FKBmJmZ5St0BGVmZu1MW+kqX/R8UGZmZi3JCcrMzCqSE5SZmVUkJygzM6tITlBmZlaRnKDMzKwiOUGZmVlFcoIyM7OK5ARlZmYVyQnKzMwqkhOUmZlVJCcoMzOrSE5QZmZWkZygzMysIjlBmZlZRXKCMjOziuQEZWZmFckz6pqZNcHK+WeVr7KR5auqPaqoBCXpUOBKoANwQ0Rc0sohmVkZtZWpxq0yVEyCktQBuAY4BFgOPClpSkQ817qRmVm5tMTRR9naaKB+azkVk6CA/YAlEbEUQNLtwBGAE5S1Ce3hg7E97EN7kPXz0FZOUyoisqu9CSSNAg6NiFPT5ROA/SPizDrbjQXGpou7A4tbILzuwOo23ob3oTLa8D5URhveh8ppA2DniOhRt7CSjqCKEhHXAde1ZJuS5kTEwLbchvehMtrwPlRGG96HymmjMZXUzfxVYKe85Z5pmZmZVaFKSlBPArtJ6iOpE3AsMKWVYzIzs1ZSMaf4ImKDpDOBB0m6md8UEQtbOaycljilmHUb3ofKaMP7UBlteB8qp40GVUwnCTMzs3yVdIrPzMxsIycoMzOrSE5QdUjqLenZlqxb0kGSFkp6SlKXLNq2T5LUVdK3Mm6joef8HEmbZtl2uUk6S9IiSW9L2iOD+meVu868ut/Kqm7LjhNUZTgO+H8R8bmIWNfawZRLOnxVJesKZJqgGnEO0KYSFMljdQjwR6DsCSoiPl/uOq1tc4Kq3yaSJqbfFu+StKmkfSXNkvS0pNmSNi9T3WcBxwD/nZZvL+nh9GjqWUkHldKIpDGSnknj/b2kbSXdnS4/LalZHwbpkcHz9TxOyyT9QtI84Ogi69pM0tQ0rmclfU3SJZKeS/fhl+l2R6frn5b0cFp2kqTJkmolvShpfBN24xJg1/SxvkzS+ZIWpPWXc6Di+p7zHYAZkmY0p+J6nuddJT2e7sfF5TpykHQtsAvwN+BE4LL0cdu1HPWnbbyV/i/Le6CBNoZIui9v+WpJJ5VYV+49cIukF9LneLikx9LX4n6Sekh6KD1DcoOklyV1L6Gt+t4jyyRdmj7XsyX1LWU/8vbl2bzl8yT9RNI3JD2ZtjtJLX3UHxH+y/sDegMBDEqXbwK+BywF9k3LtgA2KVPd5wG3AKPSsu8AP0pvdwA2L6GdPYEXgO7pcjfgDuCcvHq3zOBxOg9YBnyviXV9Fbg+b3lnkiGscr1Mu6b/FwA71ik7CVgBbA10AZ4FBjZhH55Nb38ZmAVsmnvMMnw95R6n7s2su77n+T5gdLp8OvBWOfYjrW8ZydA3G1+v5fzLxVqO90AjdQ8B7ssrvxo4qRnP7QZgL5Iv+3PT51ck44jek9b/g3T7Q9PXQpOf93reI1umz0fucRqTv18l7suzecvnAT8Bts4ruxj4drmf98b+fARVv1ci4rH09v8AXwJWRMSTABHxZkRsKFPdNXXWPwmcLOknwF4R8a8S2hgG/DEiVqfx/jMtm5AufxARa0sJvo6G9uWOJtazADgkPfI6iGQEkfXAjZKOAt5Jt3sMuEXSN0g+uHIeiog1kZwe/ROffEyLMRy4OSLegY2PWbkUes5LVd/zfCDJKTiAP5SpnZZWjvdAS/lbRCyIiA+BhcC0SD7NF5B86NcAtwNExAPA6yW287H3SN7797a8/weWWHdj+kl6RNICkksRe2bQRoOcoOpX98dhb2ZY98eWI+Jh4AskH9K3SBpTxrbLraF9ebtJlUS8AOxD8ia8GPghyej2dwGHAw+k250O/JhkSKy5krYuEEelqPT4KkrG74ENfPxzr3Mz63s37/aHecsfUsaBEOq+RyRdmFuVv1kzmmjocbkFODMi9gIuovmPV5M4QdWvl6Tct5GvA48D20vaF0DS5pJKffHVrfvR/JWSdgZei4jrgRtIXpRNNR04OvcBLqkbMA34ZrrcQdKWJcafr9F9KZakHYB3IuJ/gMtIPpy2jIj7gXFA/3S7XSPiiYi4EFjFR2M3HiKpm5IekCNJjrSK8S8gdy3xIZJv7ZumbXUrZV8aUN/jlN92qep7nh8nOR0EyXBhWShH7A0q03ugIS8De0j6tKSuwMFlrLs+j5FcY0bSF4GtSqmknvdI7jH5Wt7/vzYjzteAbSRtLenTJF8MIXmeV0jqSHIE1aKcoOq3GDhD0iKSF9RVJC+AqyQ9TfJhVuo3ibp1T6izfgjwtKT5aZtXNrWBSIaI+hkwM433cuBsYGh6qD6X8vTCKrQvxdoLmC3pKWA8yTe1+yQ9Q/Jhfm663WXpBeFnSa4XPZ2WzwYmAc8AkyJiTjGNRsQa4LG0voNJxn6ck8ZxXon7Up/6HqfrgAea00migef5HODc9LHrC5TjVG5dtwPflTS/nJ0k8gyhme+BhkTEK8CdJNcq7wTml6vuBlwEfDF9jR0N/IMkwTdV3ffIxWn5VulzfTbJl7mSRMT7wE9J3ksPAc+nqy4AniBJtM/Xf+/seKgjK4mk3iQXZfu1chwnkXSKOLPQttUgPQJcFxEh6ViSDhNHtHZc1So9GvkgkrFGDwQmRMTnylT3MpLXfkvM19QqKmawWDMriwHA1ZIEvAH8V+uGU/V6AXdK+hTwHvCNVo6nTfERlJmZVSRfgzIzs4rkBGVmZhXJCcrMzCqSE5SZmVUkJ6gqpmQA2T9IWipprqS/Sjoyb/2vJb2a9kDKlZ0kaZWSgTyfS4cdqlu+UOngsem6AyQ9ka5blA5hU188EyUtVjIY5k3pjwNzA3yuTe//VN6v6M2aTVJI+lXe8nm516iSAVNf1UcD146op/x5SRPy3yd16v8g733xtKTvNLStfZwfpCqVdkO+B3g4InaJiAEkIw/0TNd/CjgSeAUYXOfud6S/5RgC/FzStvnlEbEnSZfa3K/cbwXGpvfpR/IDyfpMBP6d5EeJXYBT89Y9ktb9uYj4aUk7bVa/d4Gj1PAo41ekr92jgZvykkuufA+S12zd90nOurz3xSEkAxM3ZdT9quUEVb2GAe9FxLW5goh4OSKuSheHkAx+OQEYXV8FEbESeIlk9PGNlAwDtRkfDYy5DcmI47mBap9roL77I0Xyi/aepe2aWZNsIBnZo9GRGCJiUbpt3UTWiWRkmYIDwabvmbHAmemXRGuEE1T12hOY18j60SQjJN8NHJY73ZZP0i4kcwQtSYu+lg7F8irJ1A/3puVXAIuVzEd1mqRGh4lK2zqBdJDY1IHp6ZH/ldSiIypbVbgGOE6NjFEpaX+SQWBXpUXj0tf7CuCFiHiqmIYiYinJaPzbNCfgauAEZQBIuiZNAE9K6gR8BbgnIt4kGYvrS3mb5xLRbcBpeVNT5E79bUcy6vJ3AdJTcgOBP5MMlpqfeOrzG5JTj4+ky/OAnSOiP8m4iPc0Z1/N6kpf578DzqpndS4R/RL4Wnw0ukHuFN82wGbp0FJWRk5Q1WsheaNER8QZJAOm9iBJRl2BBel4XzV8/DRf7lrT/hFxd92K0zfwvSSjkufKXoqICWkb/ZWMmvxgevH4htx2SmbE7cFHA8Tm5t96K719P9CxkesFZqX6NXAKyenpfFekr/eD8r40bZQOtPoA8AVJO+V15jm9vkbSMw8fACvLG3774wRVvaYDnSV9M68sN53zaODUiOgdEb2BPiRTWjRluucakutTSDos73z7biRvzjci4kvpG//UdLtTSZLj6HQCONLy7XL3l7Qfyet2TdN216xx6ZmAO0mSVNHS1+Yg4KWIeCWvM8+19WzbA7gWuDrvSMwa4MFiq1Q62vVI4ApJ3yM5r/42Se+iK0imC89t+7akR4H/LFDt1yTVkCSQ5STTsUNyPekKSe+QXGQ+LiI+qOf+15LM1/PXNB/9KT09OAr4pqQNwDrgWL+5LSO/AoodGX+cpOOBjiRTvfymge26pKcIO5K8/n9PMjWKFeDBYs3MrCL5FJ+ZmVUkJygzM6tITlBmZlaRnKDMzKwiOUGZmVlFcoIyM7OK5ARlZmYV6f8DjgTToXYIpzkAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x1 = df_gap22_cas['app']\n",
-    "y1 = 100 * df_gap22_cas['numRdHit'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_gap22_cas['numWrHit'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,110])\n",
-    "\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*4, y1[i], width=3, color=cmap(1), label='Read' if i==0 else None)\n",
-    "    plt.bar(i*4, y2[i], bottom = y1[i], width=3, color=cmap(2), label='Write' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*4\n",
-    "x2 = df_npbC_cas['app']\n",
-    "y1 = 100 * df_npbC_cas['numRdHit'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_npbC_cas['numWrHit'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*4+offset, y1[i], width=3, color=cmap(1))\n",
-    "    plt.bar(i*4+offset, y2[i], bottom = y1[i], width=3, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4, list(x1)+list(x2))\n",
-    "plt.axvline(x=offset-2, color='black')\n",
-    "\n",
-    "plt.ylabel(\"DRAM Cache  Hit Rate (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "#plt.savefig(\"../figures/cs1_all_mpki.pdf\")\n",
-    "\n",
-    "x1 = df_gap25_cas['app']\n",
-    "y1 = 100 * df_gap25_cas['numRdHit'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_gap25_cas['numWrHit'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,110])\n",
-    "\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*4, y1[i], width=3, color=cmap(1), label='Read' if i==0 else None)\n",
-    "    plt.bar(i*4, y2[i], bottom = y1[i], width=3, color=cmap(2), label='Write' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*4\n",
-    "x2 = df_npbD_cas['app']\n",
-    "y1 = 100 * df_npbD_cas['numRdHit'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_npbD_cas['numWrHit'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*4+offset, y1[i], width=3, color=cmap(1))\n",
-    "    plt.bar(i*4+offset, y2[i], bottom = y1[i], width=3, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4, list(x1)+list(x2))\n",
-    "plt.axvline(x=offset-2, color='black')\n",
-    "\n",
-    "plt.ylabel(\"DRAM Cache  Hit Rate (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "#plt.savefig(\"../figures/cs1_all_mpki.pdf\")\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Avg Tag Check Latency (reads) (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Avg Tag Check Latency (reads) (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2)\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 14,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAjgAAAClCAYAAABREodCAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAAAetElEQVR4nO3de7xc873/8dc7RIJGUhJtCEkoKkS0klQINkGCFKm6/XpccrSpFq1bnMpRShEEaU/dSigtdb+0SV0b2aKKSBChKT+XOC5xiUskRETyOX+sNTGJ2bPXvqyZvXfez8djHnvWd2bWeu+5rPnOd33X96uIwMzMzKwtaVftAGZmZmbNzRUcMzMza3NcwTEzM7M2xxUcMzMza3NcwTEzM7M2xxUcMzMza3NcwTEzM7M2xxUcMzMza3NWr+8OkvoDOwEbAIuAZ4EHIuKDnLOZmZmZNUqdLTiSRkp6EjgVWBN4HngHGAz8XdJ1kjauTEwzMzOz7Mq14KwF7BgRi0rdKGlbYDPgf3PIZWZmZtZo8lxUZmZm1tbU28lY0iaSJkqaJ+kdSX+RtEklwpmZmZk1RpazqP4M3AJ8naSj8a3AjXmGMjMzM2uKeg9RSXomIrZZqWxmRPTLNZmZmZlZI2Wp4JwPfADcBARwMPBVYBxARLyfc0YzMzOzBslSwXmlzM0REe6PY2ZmZi2Kz6IyMzOzNifLSMarAfsAvYrvHxEX5xfLzMzMrPHqreAAE4FPgVnAsnzjmJmZmTVdlgpOj5XPospL165do1evXpXYlJmV8fzzzwOwxRZbVDmJmVl5M2bMmBcR3VYuz1LBuUfSnhFxfw65VtCrVy+mT5+e92bMrB41NTUA1NbWVjWHmVl9JL1aqjxLBecx4E5J7YAlgEjOnlqnGfOZmZmZNZssFZyLgUHArPApV2ZmZtYKZKngvAY8W43KzYcffsjcuXMrvVlr4zp27EiPHj1o3759taOYmVlOslRwXgZqJd0DLC4UVuI08Xnz5tGrVy/WXHPNvDdlq4iI4L333uP111+nd+/e1Y5jZmY5yVLBeSW9rJFeKmbJkiV07Nixkpu0Nk4S6623Hu+++261o5iZWY7qnU08Is4sdalEOEi+kPI2efJkampq2HnnnRkxYgTvvfdeLtuZM2cORx55ZL33u/baa5kwYUK99xs8eHAzpKqO+fPnU1NTQ01NDZ07d6ampoaRI0fSr18/hgwZwl577cWMGTOA5PnYYost2HXXXdl///357LPPlq9ns80246abblq+XFNTw3HHHbd8efjw4V96zivxnjIzs+qqt4IjqZukcZLulvRg4VKJcJXw7rvvctZZZzFx4kSmTp3K+eefv8IXqOWjc+fO1NbWUltbS9++famtraVnz55cdNFFTJ48mauuuopjjjmGTz75BIDRo0czZcoU+vfvz/33JyMWzJw5k8GDBzNx4sQV1v3aa68RESxYsID58+dX/H8zM7Pqy3KI6gbgZmA4cDRwBFDx9v3tz7ivSY9/7MyhJcvvvvtuDjvsMDp16gTA5ptvDsAf/vAHrrvuOhYuXMi5557LnnvuyZgxY5g6dSrt27fnhhtuoF27dowcOZJFixYxaNAgxo4dy0EHHcTbb79Nhw4duO2221hnnXU4/fTTmTJlCn369Fm+3UmTJnHBBRfw+eefc/rppzNs2LCy+UutF5I+JT//+c/ZYYcdGDJkCEcddRQLFixgyy235LLLLmvScwaw7137NOnxf93/b416XI8ePRg6dChPPPHECuUffvjh8ut33HEHP/3pTzn33HNZvHgxHTp0AGDAgAFMmzaNV155hb333nv5oHVmZrbqqLcFB1gvIq4GlkTEQxHxn8BuOeeqmLlz59K9e/cvlR988MHU1tYyefJkLrzwQgAeeeQRpk6dypQpU+jevTtjx47lhBNOoLa2lnPOOQdIDqc89NBDHHTQQdx8883MnTuXadOm8fDDD7PLLrsAsGzZMi688EIefPBBamtrGTduXL05V15vwfHHH8+gQYM45JBDOO+88zj11FOZMmUKnTp14tFHH22Op6hqNthgA9566y0Axo0bR9++fZk2bRpDhyaV1aeeeooBAwYwbNgw/v73vy9/3IgRI7jzzju5++672WefplXQzMysdcrSgrMk/TtX0j7Am8C69T1IUkdgKtAh3c5tEXFGY4PmpXv37rz55ptfKr/vvvv47W9/S0TwzjvvAHDKKadwxBFHsN5663HOOefwwgsvLK/YtGvXjqVLlzJ69GhmzZrFRx99xIgRI3j11VfZZptkpovtttuO+++/n3nz5jF79mx23313AN555x0ios6+IaXWC/DCCy/QsWNHfvOb3wAwe/ZsfvGLXyCJhQsXMnDgwGZ9rirtjTfeYMstt+Tll19m9OjRjBw5kv3335/333+fBQsWMGvWLIYNG8bixYvZfPPNl1dmNt98c5577jnWXnvt5S1dZma2asnSgnO2pM7AScDJwATghAyPWwzsFhH9gG2BYZK2b2zQvOy9995cf/31LFiwAIAXX3yRuXPnMnbsWO655x7+8pe/0K5d8jTttttu/OlPf2L99ddn0qRJbLHFFjz22GNA0irz9NNP8/HHHzN16lSOOeYYIoKePXsya9YsIGlxAOjatSt9+/Zl8uTJ1NbWMnPmzLIdX0utF5Iv8kMPPZTRo0cDybxBF198MbW1tUyfPp399tsvnyetAt544w0eeOABBgwYsLxstdVW49hjj2X8+PHccccdTJgwgXvvvZcpU6Ywd+5cli37Yi7YAw44gMMOO6wa0c3MrAWotwUnIialV+cDu2ZdcTow4MJ0sX16aXEjIXfr1o1f/vKXDB8+nIhg3XXX5eqrr2b48OHsvPPODBw4kC5dugCw3377sWjRIgBuvfVWdtllF4444gjOPvtsdthhB8aMGcOLL77IsGHD2Gijjdhwww3p3r072223HTvttBP9+vUDktaeE088kSFDhiCJPn36cOmll66Q6+KLL15+dtD48eO/tN6Co446irFjx3L++eczZswYRo0axfz582nXrh0TJkygtU1eetJJJ9G1a1fWWGMNLrnkki+NgbTHHntw2mmnsfrqq69wtlSfPn14+OGHly8XzpyaM2dOJWKbmVkLozwHKJa0GjAD+AZwaUT8V7n79+/fP4on25w9ezZbbrllbvls1eX3VnmebNPMWgtJMyKi/8rlWQ5RNVpELI2IbYEewEBJW5cINkrSdEnTPfiamZmZNYdcKzgFEfEhMAX40rnQEXFlRPSPiP7dunWrRBwzMzNr4+rtgyPpXOCCtJKCpK8CJ0XEafU8rhvJqeUfSloT2AM4v+mRzayaGjomVV1jUFl+/BqZZTtNfK+IGFNYiIgPJO0NlK3gAN2B69J+OO2AW4o6LGdW7vRps8bIs9+Zma2oJVW2WlIWy1+WCs5qkjpExGKAtDWmQ30PiohngG81JVz79u359NNPPZu4NZvCbOKexNXMrG3LOlXDZEl/SJdHAtflF+kLXbt29Wm+1uw6duxIjx49qh3DzMxylGUcnPMlzQR2T4t+HRFNmxgqoy5duiwfg8bMrCXz4Q9rCL9f8pelk3FvoDYi7k2X15TUKyLm5B3OzMzMrDGynCZ+K7CsaHlpWmZmZmbWImWp4KweEZ8VFtLra+QXyczMzKxpslRw3pW0b2FB0n7AvPwimZmZmTVNlrOojgZukHQJIOA14PBcU5mZmVlFtNUOz1nOonoJ2F7SV9LlhfU8xMzMzKyqsrTgIGkfYCugY2FU4Yg4K8dcZmZmZo1Wbx8cSVcABwPHkRyiOhDomXMuMzMzs0bL0sl4h4g4HPggIs4EBgGb5xvLzMzMrPGyVHAWpX8/kbQBsIRkIk0zMzOzFilLH5xJkroA44AngQCuyjPUqqAhvdZbS491MzOzliLLWVS/Tq/eLmkS0DEi5ucby8zMzKzxMp1FVRARi4HFOWUxMzMzaxZZ+uCYmZmZtSoNasExMzPL07537ZP5vn/d/285JrHWLutAf/sCO6eLD0XExPwimZmZmTVNvRUcSWOBgcANadHPJA2KiDG5JjOz5VrzXDH+RW5m1ZClBWcfYNuIWAYg6TrgKcAVHDMzM2uRsnYy7lJ0vXMOOczMzMyaTZYWnLHAU5KmkMxFtTNwan0PkrQR8EfgaySDA14ZEb9tQlYzMzOzTLIM9HejpFpgQFr0XxHxVoZ1fw6cFBFPSuoEzJD0QET8q/FxzczMKqMh/cfAfchamiydjCdHxBDgryXK6hQRc4G56fUFkmYDGwKu4LQwnjbCrPLc+dosX3VWcCR1BNYCukr6KsnhKYB1SCoqmUnqBXwLeLxxMc3MzMyyK9eC82PgeGADYAZfVHA+Ai7JugFJXwFuB46PiI9K3D4KGAWw8cYbZ12tmZmZWZ3qrOCkHYJ/K+m4iPhdY1YuqT1J5eaGiLijju1cCVwJ0L9//2jMdszMrPF8uKzlc3+ghqv3NPEmVG4EXA3MjoiLG7MOMzMzs8bIc7LNHYHDgN0kPZ1e9s5xe2ZmZmZAjpNtRsQ/+KLfjpmZmVnF1NuCo8R/SDo9Xd5Y0sD8o5mZmZk1TpYWnMuAZcBuwFnAApKOwwPKPcjMzMzantbS4TlLBec7EfFtSU8BRMQHktbIOZeZmZlZo2Wp4CyRtBrJfFJI6kbSomMV0lpqy2ZmZi1FlgrO/wB3AutLOgf4PnBarqnMrEk8romZreqyTLZ5g6QZwBCSs6L2j4jZuSczMzMza6Qsk21uDHwCTCwui4j/zTOYmZmZWWNlOUT1N5L+NwI6Ar2B54GtcsxlZmZm1mhZDlH1LV6W9G3gp7klMjPLaPsz7mvQ/R87c2hOScyspWnwSMYR8aSk7+QRJm8N2Rl6R1iaz+gyM7PWIEsfnBOLFtsB3wbezC2RmZmZWRNlacHpVHT9c5I+ObfnE8es5XCLn5lZ65WlD86ZlQhiZmZm1lzqrOBImkg6enEpEbFvLonMzMzMmqhcC86F6d/vAV8Hrk+XDwXezjOUmZmZWVPUWcGJiIcAJF0UEf2LbpooaXruyczMzMwaKUsn47UlbRIRLwNI6g2snW8sW1W5Y6+ZmTWHLBWcE4BaSS+TjGbcExiVayozsxx4ElKzVUeWs6julbQZ8M206N8RsTjfWGZmZmaNV+4sqsER8Q+AtEIzc6Xb1wE2john841o1vJ5hGczs5alXAvOAZIuAO4FZgDvkky2+Q1gV5JDVSflntCsDq5UmJlZXcqdRXWCpHWBA4ADge7AImA28PtC605dJF0DDAfeiYitmy9yZfjL08zMrPUq2wcnIt4HrkovDXUtcAnwx0Y81szMzKzR2uW14oiYCryf1/rNzMzM6qKIOmdjaPrKpV7ApKyHqHr37h1nnHFGbnmenJO9vrXGV95o0Lq37trXWZylRWSBhuUpleXpp58GYNttt616lnKcxVlaShZoWB5naT4jR46csdKAxECGFhxJHbKUNZakUZKmS5q+ZMmS5lqtmZmZrcLqbcGR9GREfLu+sjoe24sGtOD0798/pk/PbxaIhoySu/63/qdB625oJ2NncZa8skDD8pTKUlNTA0BtbW3Vs5TjLM7SUrJAw/I4S/ORVLIFp9w4OF8HNgTWlPQtklGMAdYB1solpZmZmVkzKHcW1VDgSKAHcHFR+QJgTH0rlnQjUAN0lfQ6cEZEXN3opGZmZmYZlRsH5zrgOkkHRMTtDV1xRBzapGRmZmZmjZRlLqrbJe0DbEUyknGh/Kw8g5mZmZk1VpazqK4ADgaOI+mHcyDJNA1mZmZmLVKWgf52iIjDgQ8i4kxgELB5vrHMzMzMGi9LBWdR+vcTSRsAS0jmpTIzMzNrkertgwNMktQFGAc8CQSNm5vKzMzMrCKydDL+dXr1dkmTgI4RMT/fWGZmZmaNV28FR1J74CfAzmlRraTfR4TnVTAzM7MWKcshqsuB9sBl6fJhadkP8wplZmZm1hRZKjgDIqJf0fKDkmbmFcjMzMysqbKcRbVU0qaFBUmbAEvzi2RmZmbWNFlacEYDUyS9TDLQX09gZK6pzMzMzJogy1lUkyVtBmyRFj0fEYvzjWVmZmbWeHVWcCR9r46bviGJiLgjp0xmZmZmTVKuBee7ZW4LwBUcMzMza5HqrOBEhPvZmJmZWatUtg+OpF1IJtl8RtJBJIP9vQRc5n44ZmZm1lKV64NzKbAN0FHS88BXgHuBHYFrgB9UJKGZmZlZA5Vrwdk1IvpI6gi8AawfEUsl/R54pjLxzMzMzBqu3EB/nwJExKfAqxGxNF0OwPNQmZmZWYtVrgVnfUknkgzuV7hOutwt92RmZmZmjVSugnMV0KnEdYAJuSUyMzMza6Jyp4mfWckgZmZmZs0ly2SbjSZpmKTnJb0o6Rd5bsvMzMysILcKjqTVgEuBvYA+wKGS+uS1PTMzM7OCPFtwBgIvRsTLEfEZcBOwX47bMzMzMwPKD/T3HvA48AjwT+DxiPikAeveEHitaPl14DuNCWlmZmbWEOXOouoNbA/sAJwKbCfpFZIKzyMRcUtzBJA0ChiVLi5MR01uCboC87LeWSjHKM5SB2epW+Y85bJIzZKzWbI0E2cpzVlKa0mfa2epW8+S203G7aufpLWBkcDxQO+IWK2e+w8CfhURQ9PlUwEiYmz2zNUjaXpE9K92DnCWujhL3VpSHmcpzVlKc5bSnKXhyh2i2oCk9WYHYEBaPAM4DXg0w7qfADaT1JtkqodDgP/XpLRmZmZmGZQ7RPU68CQwHvhF2lE4s4j4XNKxwH3AasA1EfFco5OamZmZZVSugrMjMAgYAZwoaQ5Jy82jwPSIWFzfyiPibuDuZshZDVdWO0ARZynNWerWkvI4S2nOUpqzlOYsDdSQPji9gO8CPwd6RETHHHOZmZmZNVq5FhwkfZMv+uHsCHQBHgOuyD2ZmZmZWSPVOdCfpHnALSRj10wFvhsR3SNiRERcWKmAlSCpl6RnW2oOSTtJek7S05LWrEY2a7kkdZH002rngLLv4eMlrVWNTC2JpJ9Jmi3p42qP7C7pn9XcfoGkhdXOYG1TuZGMN42IbSLixxHxx4h4UVJXNdPAGNYgPwDGRsS2EbGo2mFasnSKkFVNF6BFVHDKOB5Y5Ss4JK/THsCtJFPYVE1E7FDN7ZvlrVwFZ0tJUyTdIelb6a+yZ4G3JQ2rUL5KWl3SDemvq9skrSVpgKR/SpopaZqkTlXI8TPgIODXaXl3SVPT1pxnJe2UZxhJh0t6Jn0O/iTpa5LuTJdnSqrYTjJtHfh3iddpjqTzJT0JHNjM21xb0t/S//VZSQdLOk/Sv9Ln5cL0fgemt8+UNDUtO1LSXyTVSvr/ks5ozmxFzgM2Td8T4yT9l6RZaZbzctpmOaXewxsAUyRNqVSIEu/dTSU9lj43Z1e65UDSFcAmwCvAEcC49DXbtJI5ivIsTP9WdJ9SJk+NpElFy5dIOrIC2y3sV66V9EL63t1d0iPp53agpG6SHlDSkj5B0quSuuaYqdR+Z46kC9L37zRJ38hr+ytlWaFVVtLJkn4l6UeSnkgz3q6W2EIbESUvwHRgT5IvjA+A7dPybwJP1fW41ngBegEB7JguXwOcArwMDEjL1gFWr0KOk4Frge+nZScB/51eXw3olGOerYAXgK7p8rrAzcDxRdvvXOXX6WRgDnBKTts8ALiqaLkn8DxfdNDvkv6dBWy4UtmRwFxgPWBNkh8I/XN6Xp5Nr+9FMrXKWoXXrFKvT4bXqGsFc5R6704CDk2XjwYWVvK5Sbc7h2QU2OWf6WpdCv9/Jfcp9eSoASYVlV8CHFmB7fcCPgf6kvzon5G+b0Uyf+JdaZZT0/sPS9/jub2fS+x3OqfvncLrdHjxc1WB5+fZouWTgV8B6xWVnQ0cV8n3TZZLuRac1SPi/oi4FXgrIh4DiIh/l3lMa/ZaRDySXr8eGArMjYgnACLio4j4vAo5Bq90+xPASEm/AvpGxIIcs+wG3BoR8wAi4v207PJ0eWlEzM9x+6XU9fzcnNP2ZgF7pC1EO5EMWvkpcLWk7wGF+dkeAa6V9COSL4mCByLivUgOLd7Bl1/P5rY78IdI541LX7NKq+89XAml3ruDSA4NAfy5CplaqkruU1qqVyJiVkQsA54DJkfyzT2L5At+MMmE0UTEvSQ/+vO0wn6naD97Y9HfQTlnqM/Wkh6WNIukG8VWVc7zJeUqOMuKrq/c7yPbueWty8r/00dVSfHlHCssR8RUYGeSL9prJR1eqWAtRF3Pz8e5bCziBeDbJDucs4ExwEDgNmA4cG96v6NJRvneCJghab168rZlq+L/3Gq1oH3K56z4nVTJoUiKx3VbVrS8jHrONs7DyvsdSacXbiq+W4Xi1PW6XAscGxF9gTOp7OuVSbkKTj9JH0laAGyTXi8s961QvkraWMn8WZBMKfEY0F3SAABJnSRV4o2+co5/FN8oqSfwdkRcBUwg+RDk5UHgwMKXtaR1gcnAT9Ll1SR1znH7pZR9fpqbkilLPomI64FxJF8EnSMZxPIEoF96v00j4vGIOB14l6SiA8mvsHWVnP22P0lLT3NbABT6hz1A8mt8rTTXujlsrz6lXqPijJVQ6r37GEnTPyRTx1RTpZ+POlV4n1LOq0AfSR0kdQGGVClHKY+Q9IVE0p7AV/PcWIn9TuE1Objob5Ypk5rD28D6ktaT1IHkhx0k79+5ktqTtOC0OHV+YUc9k2m2Qc8Dx0i6BvgX8DuSneTv0i+nRSTN/3l3TFw5x+XAZUW31wCjJS1Js+T2aysinpN0DvCQpKXAUyQDPV4p6ShgKUllp1IfNCj9/ByX4/b6knQGXQYsAU4EJknqSHKM/sT0fuMkbZaWTQZmAtsC04DbgR7A9RExvbkDRsR7aYfIZ4F7gL8C0yV9RjKS+Jjm3mY9Sr1GnwH3SnozInbNO0Ad793jgesl/TdJy1ulD68Wuwm4SkkH7O9HxEtVzFJDhfYp5UTEa5JuIemr9grJa9ZSnAncKOkwkv3dWySV1LysvN/5CUmr8VclPUPSwnRojttfLiKWSDqLZF/2BlDopvJL4HGSH3SP00Iq7MUyj2RsVm1KRtOeFBFbVztLFukZIP0j4thqZzFIW7UWRURIOoSkw/F+1c5lLV/acrE0kjkWBwGXR8S2Fc4wh2R/Mq+S223NKn5s0cysSrYDLpEk4EPgP6sbx1qRjYFbJLUjaY38UZXzWAZuwTEzM7M2p1wnYzMzM7NWyRUcMzMza3NcwTEzM7M2xxUcazWUzIP1Z0kvS5oh6VFJI4pu/42kN9KOgIWyIyW9q2SenX+lIw2vXP6c0nmt0tu2l/R4etvsdITXUnlukPS8krlirknHgyjMqTM/ffzTRYN0mVkTSQpJFxUtn1z4jCqZI+kNfTGv1r4lyv8t6fLi/cRK619atF+YKemkuu5rLZtfNGsV0jNf7gKmRsQmEbEdyWBtPdLb2wEjgNeAXVZ6+M3pKZ01wLmSvlZcHhFbkZwZURhE6zpgVPqYrYFb6oh1A8ncbH1J5pr6YdFtD6fr3jYizmrUP21mpSwGvqe6J7scn352DwSuKaqcFMr7kHxmV95PFCwq2i/sQTK/W14T5VqOXMGx1mI34LOIuKJQEBGvRsTv0sUakjlkLqeOAbAi4h3gJZIJM5dTMkL12nwxv8z6JJNkFubb+lcd67s7UiSDYPVo3L9mZg3wOXAlyUjidYqI2el9V64IrUEyrUC980ml+4xRwLHpjyxrRVzBsdZiK+DJMrcfSjIB3Z3APoXDRcUkbQJsAryYFh0s6WmS0TnXBSam5eOB5yXdKenH6ajFdUq3dRjpvFSpQWnz9j2SWtwkdGat3KXAD1RmqhhJ3yGZS+rdtOiE9PM+F3ghIp7OsqGIeJlkAt31mxLYKs8VHGuVJF2aViCekLQGsDdwV0R8RDJs+NCiuxcqMjcCPy6aYbtw6OrrJJPajQZIDyn1B+4nmUupuOJSymUkh84eTpefBHpGRD+SKT/uasr/amYrSj/nfwR+VuLmQkXmQuDg+GKwt8IhqvWBtdPRrK0NcwXHWovnKJoEMCKOIZmMrxtJZaYLMCsdznwwKx6mKvS1+U5E3LnyitMd4ESSiTQLZS9FxOXpNvopmWjuvrTz4YTC/SSdkWY4seixH0XEwvT63UD7Mv0FzKxxfgMcRXJ4udj49PO+U9GPjuUiYgnJj5adJW1UdDLA0aU2krb8LgXead74ljdXcKy1eBDoKOknRWVrpX8PBX4YEb0iohfQm2QW77XIbjBJ/xwk7VN0vH0zkp3bhxExNN1x/jC93w9JKleHRsSywookfb3weEkDST5n7zXs3zWzctKW2FtIKjmZpZ/NHYGXIuK1opMBrihx327AFcAlRS1B1kp4LiprFdIJEvcHxks6heS4+sckZzeMB44uuu/Hkv4BfLee1R4saTBJBeR14Mi0/LB0O5+QdFL8QUQsLfH4K4BXgUfT+swd6eGt7wM/kfQ5ySz0h3jnaJaLi4Csk9meIOk/gPbAMySHlktZMz3E1Z7k8/8n4OIm5rQq8FxUZmZm1ub4EJWZmZm1Oa7gmJmZWZvjCo6ZmZm1Oa7gmJmZWZvjCo6ZmZm1Oa7gmJmZWZvjCo6ZmZm1Oa7gmJmZWZvzf2Bn4dEfs/EiAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['BWBloat'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['BWBloat'].astype(float)\n",
-    "\n",
-    "\n",
-    "gap_25_cas = df_gap25_cas['BWBloat'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['BWBloat'].astype(float)\n",
-    "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['BWBloat'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['BWBloat'].astype(float)\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas['BWBloat'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['BWBloat'].astype(float)\n",
-    "\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3.5])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"BW Bloat (due to acc amp)\")\n",
-    "plt.legend(fontsize=8, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "plt.ylim([0,3.5])\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"BW Bloat (due to acc amp)\")\n",
-    "plt.legend(fontsize=8, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "# Anything above is old results.\n",
-    "Tag probing results starts here:\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_308484/3690351968.py:27: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3690351968.py:30: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3690351968.py:64: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3690351968.py:67: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(df_gap22_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(df_gap25_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(df_npbC_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(df_npbD_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,220])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Tag Comparison Latency (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,220])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Tag Comparison Latency (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_308484/1065878878.py:27: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/1065878878.py:30: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/1065878878.py:64: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/1065878878.py:67: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['avgPktRespTimeRd'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['avgPktRespTimeRd'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['avgPktRespTimeRd'].astype(float))\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(df_gap22_ram['avgPktRespTimeRd'].astype(float))\n",
-    "\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['avgPktRespTimeRd'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['avgPktRespTimeRd'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['avgPktRespTimeRd'].astype(float))\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(df_gap25_ram['avgPktRespTimeRd'].astype(float))\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['avgPktRespTimeRd'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['avgPktRespTimeRd'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['avgPktRespTimeRd'].astype(float))\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(df_npbC_ram['avgPktRespTimeRd'].astype(float))\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['avgPktRespTimeRd'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['avgPktRespTimeRd'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['avgPktRespTimeRd'].astype(float))\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(df_npbD_ram['avgPktRespTimeRd'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,300])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Avg Rd Resp Time (ns) (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,300])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Avg Rd Resp Time (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_308484/3412151829.py:27: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3412151829.py:30: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3412151829.py:64: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3412151829.py:67: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['avgPktRespTimeWr'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['avgPktRespTimeWr'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['avgPktRespTimeWr'].astype(float))\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(df_gap22_ram['avgPktRespTimeWr'].astype(float))\n",
-    "\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['avgPktRespTimeWr'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['avgPktRespTimeWr'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['avgPktRespTimeWr'].astype(float))\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(df_gap25_ram['avgPktRespTimeWr'].astype(float))\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['avgPktRespTimeWr'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['avgPktRespTimeWr'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['avgPktRespTimeWr'].astype(float))\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(df_npbC_ram['avgPktRespTimeWr'].astype(float))\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['avgPktRespTimeWr'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['avgPktRespTimeWr'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['avgPktRespTimeWr'].astype(float))\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(df_npbD_ram['avgPktRespTimeWr'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,1000])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Avg Wr ORB Time (ns) (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,1000])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Avg Wr ORB time (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 16,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_308484/1314087200.py:35: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/1314087200.py:38: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/1314087200.py:72: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/1314087200.py:75: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['simSeconds'].astype(float)/df_gap22_noDC['simSeconds'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)/df_gap22_noDC['simSeconds'].astype(float)\n",
-    "geo_meanC = df_gap22_ram_prob['simSeconds'].astype(float)/df_gap22_noDC['simSeconds'].astype(float)\n",
-    "geo_meanR = df_gap22_ram['simSeconds'].astype(float)/df_gap22_noDC['simSeconds'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['simSeconds'].astype(float)/df_gap25_noDC['simSeconds'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)/df_gap25_noDC['simSeconds'].astype(float)\n",
-    "geo_meanC = df_gap25_ram_prob['simSeconds'].astype(float)/df_gap25_noDC['simSeconds'].astype(float)\n",
-    "geo_meanR = df_gap25_ram['simSeconds'].astype(float)/df_gap25_noDC['simSeconds'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['simSeconds'].astype(float)/df_npbC_noDC['simSeconds'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)/df_npbC_noDC['simSeconds'].astype(float)\n",
-    "geo_meanC = df_npbC_ram_prob['simSeconds'].astype(float)/df_npbC_noDC['simSeconds'].astype(float)\n",
-    "geo_meanR = df_npbC_ram['simSeconds'].astype(float)/df_npbC_noDC['simSeconds'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['simSeconds'].astype(float)/df_npbD_noDC['simSeconds'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)/df_npbD_noDC['simSeconds'].astype(float)\n",
-    "geo_mean = df_npbD_ram_prob['simSeconds'].astype(float)/df_npbD_noDC['simSeconds'].astype(float)\n",
-    "geo_mean = df_npbD_ram['simSeconds'].astype(float)/df_npbD_noDC['simSeconds'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,2.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Normalized Execution Time\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,2.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Normalized Execution Time\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = 100* df_gap22_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_gap22_ram_prob['numTotMisses'].astype(float)+df_gap22_ram_prob['numTotHits'].astype(float))\n",
-    "#gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "\n",
-    "gap_25_prob = 100* df_gap25_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_gap25_ram_prob['numTotMisses'].astype(float)+df_gap25_ram_prob['numTotHits'].astype(float))\n",
-    "#gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "npb_C_prob = 100* df_npbC_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_npbC_ram_prob['numTotMisses'].astype(float)+df_npbC_ram_prob['numTotHits'].astype(float))\n",
-    "#npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = 100* df_npbD_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_npbD_ram_prob['numTotMisses'].astype(float)+df_npbD_ram_prob['numTotHits'].astype(float))\n",
-    "#npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "#app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "#app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"ACT delayed (%)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "#app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "#app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"ACT delayed (%)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 18,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "x1 = df_gap22_ram_prob['app']\n",
-    "y1 = 100 * df_gap22_ram_prob['noCandidBSlot'].astype(float)/(df_gap22_ram_prob['locMemReadBursts'].astype(float)+df_gap22_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y2 = 100 * df_gap22_ram_prob['foundCandidBSlotRH'].astype(float)/(df_gap22_ram_prob['locMemReadBursts'].astype(float)+df_gap22_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y3 = 100 * df_gap22_ram_prob['foundCandidBSlotRMC'].astype(float)/(df_gap22_ram_prob['locMemReadBursts'].astype(float)+df_gap22_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y4 = 100 * df_gap22_ram_prob['foundCandidBSlotRMD'].astype(float)/(df_gap22_ram_prob['locMemReadBursts'].astype(float)+df_gap22_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,110])\n",
-    "\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*4, y1[i], width=3, color=cmap(1), label='Not Probed' if i==0 else None)\n",
-    "    plt.bar(i*4, y2[i], bottom = y1[i], width=3, color=cmap(2), label='Read-Hit' if i==0 else None)\n",
-    "    plt.bar(i*4, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3), label='Read-MC' if i==0 else None)\n",
-    "    plt.bar(i*4, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4), label='Read-MD' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*4\n",
-    "x2 = df_npbC_ram_prob['app']\n",
-    "y1 = 100 * df_npbC_ram_prob['noCandidBSlot'].astype(float)/(df_npbC_ram_prob['locMemReadBursts'].astype(float)+df_npbC_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y2 = 100 * df_npbC_ram_prob['foundCandidBSlotRH'].astype(float)/(df_npbC_ram_prob['locMemReadBursts'].astype(float)+df_npbC_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y3 = 100 * df_npbC_ram_prob['foundCandidBSlotRMC'].astype(float)/(df_npbC_ram_prob['locMemReadBursts'].astype(float)+df_npbC_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y4 = 100 * df_npbC_ram_prob['foundCandidBSlotRMD'].astype(float)/(df_npbC_ram_prob['locMemReadBursts'].astype(float)+df_npbC_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*4+offset, y1[i], width=3, color=cmap(1))\n",
-    "    plt.bar(i*4+offset, y2[i], bottom = y1[i], width=3, color=cmap(2))\n",
-    "    plt.bar(i*4+offset, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3))\n",
-    "    plt.bar(i*4+offset, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4, list(x1)+list(x2))\n",
-    "plt.axvline(x=offset-2, color='black')\n",
-    "plt.axhline(y=100, color='gray')\n",
-    "\n",
-    "plt.ylabel(\"Breakdown of B slots probing (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=2, loc='lower right')\n",
-    "plt.tight_layout()\n",
-    "#plt.savefig(\"../figures/cs1_all_mpki.pdf\")\n",
-    "\n",
-    "x1 = df_gap25_ram_prob['app']\n",
-    "y1 = 100 * df_gap25_ram_prob['noCandidBSlot'].astype(float)/(df_gap25_ram_prob['locMemReadBursts'].astype(float)+df_gap25_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y2 = 100 * df_gap25_ram_prob['foundCandidBSlotRH'].astype(float)/(df_gap25_ram_prob['locMemReadBursts'].astype(float)+df_gap25_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y3 = 100 * df_gap25_ram_prob['foundCandidBSlotRMC'].astype(float)/(df_gap25_ram_prob['locMemReadBursts'].astype(float)+df_gap25_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y4 = 100 * df_gap25_ram_prob['foundCandidBSlotRMD'].astype(float)/(df_gap25_ram_prob['locMemReadBursts'].astype(float)+df_gap25_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "\n",
-    "# Multi bar Chart\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,110])\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*4, y1[i], width=3, color=cmap(1), label='Not Probed' if i==0 else None)\n",
-    "    plt.bar(i*4, y2[i], bottom = y1[i], width=3, color=cmap(2), label='Read-Hit' if i==0 else None)\n",
-    "    plt.bar(i*4, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3), label='Read-MC' if i==0 else None)\n",
-    "    plt.bar(i*4, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4), label='Read-MD' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*4\n",
-    "x2 = df_npbD_ram_prob['app']\n",
-    "y1 = 100 * df_npbD_ram_prob['noCandidBSlot'].astype(float)/(df_npbD_ram_prob['locMemReadBursts'].astype(float)+df_npbD_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y2 = 100 * df_npbD_ram_prob['foundCandidBSlotRH'].astype(float)/(df_npbD_ram_prob['locMemReadBursts'].astype(float)+df_npbD_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y3 = 100 * df_npbD_ram_prob['foundCandidBSlotRMC'].astype(float)/(df_npbD_ram_prob['locMemReadBursts'].astype(float)+df_npbD_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "y4 = 100 * df_npbD_ram_prob['foundCandidBSlotRMD'].astype(float)/(df_npbD_ram_prob['locMemReadBursts'].astype(float)+df_npbD_ram_prob['locMemWriteBursts'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*4+offset, y1[i], width=3, color=cmap(1))\n",
-    "    plt.bar(i*4+offset, y2[i], bottom = y1[i], width=3, color=cmap(2))\n",
-    "    plt.bar(i*4+offset, y3[i], bottom = y1[i]+y2[i], width=3, color=cmap(3))\n",
-    "    plt.bar(i*4+offset, y4[i], bottom = y1[i]+y2[i]+y3[i], width=3, color=cmap(4))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*4, list(x1)+list(x2))\n",
-    "plt.axvline(x=offset-2, color='black')\n",
-    "plt.axhline(y=100, color='gray')\n",
-    "\n",
-    "plt.ylabel(\"Breakdown of B slots probing (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=2, loc='lower right')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 19,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_308484/3451258688.py:35: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3451258688.py:38: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3451258688.py:70: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_308484/3451258688.py:73: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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9OA8AP46I6en074BvR8Ss9ubZ0NAQs2a1W6VbqnX4YlOMW83YtRa3mrHbi9vY2AhAU1NTj8btSB7X9cYat5pqcV3X4jJ3RFKXenB+Dnw6ncGngKtIRhYfBtwAHN+NNr0M7FQ0PSgtMzOzTcjG/M/PaldHN/qrK+qlOQm4ISLujojvAR/tZuzJwKlKHAis6Oj8GzMzM7NydNSDUydps4hYCxzBe+fgdPheSbcBjcD2kpqBy4FeABExHphKcon4sySXiZ/elQUwMzMzK9VRgnMb8KikZcDbwDQASR8FVrT3xog4uYPXAw/3YGZmZhloN8GJiB+mJ//uCDwU752R/AGSc3HMzMzMNjodXiYeEU+0UvZMNs0xMzMz675yRxM3MzMz22Q4wTEzM7PccYJjZmZmueMEx8zMzHLHCY6ZmZnljhMcMzMzyx0nOGZmZpY7TnDMzMwsd5zgmJmZWe44wTEzM7PccYJjZmZmueMEx8zMzHLHCY6ZmZnljhMcMzMzyx0nOGZmZpY7TnDMzMwsd5zgmJmZWe44wTEzM7PccYJjZmZmueMEx8zMzHLHCY6ZmZnljhMcMzMzyx0nOGZmZpY7TnDMzMwsdzJNcCQdJWmRpGcl/Usrr4+WtFTSnPTvjCzbY2ZmZrVhs6xmLKkO+CnwGaAZmClpckQsKKl6R0Scl1U7zMzMrPZk2YNzAPBsRCyOiNXA7cDIDOOZmZmZAdkmOAOBJUXTzWlZqS9KekrSXZJ2am1GksZImiVp1tKlS7Noq5mZmeVItU8yvh+oj4i9gYeBX7ZWKSJuiIiGiGgYMGBAjzbQzMzMNj1ZJjgvA8U9MoPSshYRsTwi3k0nbwL2y7A9ZmZmViOyTHBmArtJGiJpc2AUMLm4gqQdiyY/ByzMsD1mZmZWIzK7iioi1ko6D/gtUAdMiIj5kn4AzIqIycDXJX0OWAu8DozOqj1mZmZWOzJLcAAiYiowtaTssqLn3wG+k2UbzMzMrPZU+yRjMzMzs4pzgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma5k2mCI+koSYskPSvpX1p5fQtJd6Svz5BUn2V7zMzMrDZkluBIqgN+ChwNfAw4WdLHSqp9Ffh7RHwUuBa4Oqv2mJmZWe3IsgfnAODZiFgcEauB24GRJXVGAr9Mn98FHCFJGbbJzMzMaoAiIpsZS8cDR0XEGen0KcDwiDivqM7TaZ3mdPq5tM6yknmNAcakk7sDizJpdMe2B5Z1WCtfsWstbjVj11rcasautbjVjF1rcasZu9biFuwcEQNKCzerRks6KyJuAG6odjskzYqIhlqKXWtxqxm71uJWM3atxa1m7FqLW83YtRa3I1keonoZ2KloelBa1modSZsB2wLLM2yTmZmZ1YAsE5yZwG6ShkjaHBgFTC6pMxk4LX1+PPD7yOqYmZmZmdWMzA5RRcRaSecBvwXqgAkRMV/SD4BZETEZ+AVws6RngddJkqCNWTUPk1Urdq3FrWbsWotbzdi1FreasWstbjVj11rcdmV2krGZmZlZtfhOxmZmZpY7TnDMzMwsd5zgtEFSfXqfnqrHlPRJSfMlzZG0ZU+2ybIjqZ+kc6rdjp7UzjZ+gaStqtGmniDp65IWSvpHK3d0zzLuH3oqVlHMVT0d06w1TnA2DV8GroqIYRHxdrUbUy3p8B950g+oqQSnHRcAuU1wSD7nzwB3kgxd0yMi4p96KpbZxsYJTvs2kzQp/eV1l6StJO0v6Q+S5kr6k6S+Gcf8OnAi8K9p+Y6SHkt7c56W9MlKBpd0qqSn0uW7WdKHJN2bTs+VlMkOM/1l/5dW1vcLkq6W9CRwQjfmv7WkKekyPC3pJEk/lrQgXd5/S+udkL4+V9JjadloSfdJapL0P5Iur9Bi/xjYNf0sfyLp25LmpbF/3Mnl+56SgW2nS7pN0sVpe6+VNCtdp/tLuiddhiuL3vv/0m15jqSfFxJJSePS986XdEVR/RckXSHpybS9e3RyuVvbxj8CPCLpkU7OqyytbNe7Snoibf+VWfY6SBoP7AI8T3JbjJ+k63rXrGIWxV6VPma632gjdqOkB4qmx0oaXeEYhf3GREnPpNvVpyU9nm7nB0gaIOnhdDu+SdKLkravYBta27e8IOn/p9vXnyR9tFLxiuJu0Buafue/L+lMSTPT9tytMntGN7F9SHkiwn+t/AH1QAAHp9MTgH8GFgP7p2XbAJtlHPNiYCJwfFr2TeDS9Hkd0LeC8fcCngG2T6e3A+4ALiiKt20Pru+LgReAf67A/L8I3Fg0vTPJkB+FKwn7pY/zgIElZaOBV4H+wJbA00BDhZb56fT50cAfgK0K674T89kfmAP0BvoC/5Ouuybg6rTON4BXgB2BLYDmdHn2BO4HeqX1fgacWtyG9HNvAvZOp18Azk+fnwPcVKHPefuMtq3WtusHgJPT6bOAVVnELmrDCyS3s2/5LvfEX2G5yHC/0U7MRuCBovKxwOgKx6oH1gJDSX6wz063KZGMdfibNO530vpHpdtfxbY13r9v2Tb9vAvr+9Ti9VDhZX+6aPpi4PtA/6KyKwvf1Q7mtcnsQzrz5x6c9i2JiMfT57cARwKvRsRMgIhYGRFrM455SMnrM4HTJX0fGBoRb1Yw9uHAnZGOBRYRr6dl49LpdRGxooLxSrW17HdUYN7zgM8o6Q36JMldtN8BfiHpC8Bbab3HgYmSziT5UhY8HBHLIzlEeA/v/1y669PAf0XEW9Cy7st1MHBfRLyTbg/3F71WuLnmPGB+RLwaEe+SJOo7AUcA+wEzJc1Jp3dJ33Oikp6zP5MkCcWHVu5JH2eT7Gg7o6NtvNJa264PIjlcBHBrxvE3BlnuN6rt+YiYFxHrgfnA7yL5zzmPZNs8hGSwZyLiQeDvFY6/wb6laB95W9HjQRWO2Z6PS5omaR7J6Q17lfGeTW0fUhYnOO0rvUnQyirE3GA6Ih4DPkXyD3qipFN7oE09pa1l/0e3ZxzxDLAvyZf0SuASkhHv7wI+CzyY1jsL+C7JF3e2pP4dtG1j9276uL7oeWF6M5Jfur+M5PyuYRGxe0R8X9IQkl9wR0TE3sAUkl93pfNdR+dvGLqprstNVpX2G2vZ8H9M77YqdlPpdl28zWc+3mLpvkXSZYWXiqtlELqt9TsROC8ihgJX0P31vjHuQ8riBKd9gyUVMu8vAU8AO0raH0BSXyVjaGUZc3rxi5J2Bv4WETcCN5F8sSrl98AJhX/qkrYDfgecnU7XSdq2gvFKtbvs3SHpI8BbEXEL8BOSnf22ETEVuBD4RFpv14iYERGXAUt5bzy1z0jaTslVbJ8n6enprjdJuoMBHib5hb1V2o7tOjGfx4ERknpL6kOSsJXrd8DxknYoxE23sW1IEssVkj5EcgitUlr7nIvXRaW1tl0/QXJoAXr2DupZLmebMt5vtOVF4GOStpDUj+SXfTU8TnIeI5L+L/DBSs68lX1LYd2eVPT4x0rGTP0N2EFSf0lb8N73vi/wqqReJD045djU9iFl2SRGE6+iRcC5kiYAC4DrSXaW16f/6N4mObRQyRMUS2OOIzmmWdAIfEvSmjRuxX6JRTKUxg+BRyWtI+lW/AZwg6SvkmTaZ5PNlxVaX/bzKzTvoSQnd64H1gAXAQ9I6k3yC+SitN5PJO2Wlv0OmAsMA/4E3E0yaOwtETGruw2KiOVKToZ8Gvhvkq7gWZJWA1NJepnKmc9MSZOBp0h2evOAsg4lRsQCSd8FHpL0AZJ1c25EPCHpz8BfgCVUJqEraO1zXg08KOmViDisgrHa2q4vAG6RdClJ712Wh16L3Q7cqOTE6uMj4rkeittIRvuNtkTEEkm/Jjln7XmS9V4NVwC3STqFZN/1V5JEs1JK9y1nk/QMf1DSUyQ9FSdXMB4AEbFGydBHfyLpmftL+tL3gBkkP9BmUEZCvQnuQ8rioRpsoyCpnuREvI9Xuy2llFz50RAR51W7LW2R1CciVqU9QI8BYyLiyWq3a2OVrqe3IyIkjSI54XhktdtllZf2bqyLZHzEg4BxETEs45gvkOwzlmUZp5LyuA9xD45ZPtyg5AZyvUmOh2/SO6YesB8wVpKAN4CvVLc5lqHBwK/T3oXVwJlVbs/GKnf7EPfgmJmZWe74JGMzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBsS5RMgjnrZIWS5ot6Y+Sjit6/T8kvZxeuVAoGy1pqZIB2RakwyGUls9XOtBm+tqBkmakry1Ucqv51tozSclAcU9LmpDe5ApJX1YyyOI8JYOkfiLTFWNWYySFpGuKpi8ufE+VDP74st4b5PNzrZT/RcmgjK3+P5L0YUm3S3ou3ddMlfR/emThbJPmBMc6Lb209jfAYxGxS0TsR3I32EHp6x8AjiO5udOhJW+/I70HRSPwo/QOly3lEbEXyaWchbuA/pLkfgzDgI8Dv26jWZOAPUhuurUlcEZa/jxwaHrb8n8FbujaUptZG94FvqC2R+i+Nv3+ngBMKEpkCuUfI/nelu4rCvuae4GmiNg13dd8B/hQaV2zUk5wrCsOB1ZHxPhCQUS8GBHXp5ONJIPejaONO3hGxGvAcySjerdQMvTF1rw3IN4OJCN5Fwb7XNDG/KZGiuTOnoPS8j9ERGFeTxTKzaxi1pL8cLiwvUoRsTCtW5oIbU5y75XWBsE8DFhTsq+ZGxHTutViqwlOcKwr9gLauwnUySQj6N4LHFs4XFRM0i4kI84+mxadpGQk2peB7XhvNNtrgUWS7pX0tXRohTalsU4hHTyzxFdJhkQws8r6KfBltTNWnaThJAM0Lk2LLky/868Cz0TEnFbe9nGS0abNOs0JjnWbpJ9KmitppqTNgWOA30TESpKxUI4sql5IZG4DvhYRr6flhUNXHyYZB+VbABHxA6ABeIhkYMbWEpdiPyM5dLbBLzxJh5EkON/u8oKaWavS7/qvgK+38nIhkfk34KR47+6yhUNUOwBbp0NmmFWMExzrivkUjUYcEeeSjBQ8gCSZ6QfMS8djOYQND1MVzrUZHhH3ls443fndTzLad6HsuYgYl8b4hJLRc3+bnqB4U6GepMvTNlxUPE9Je5OMoDwyIpZ3a8nNrC3/QfIjYuuS8mvT7/wnWzu0FBFrSH64fErSTun3eo6ks0j2Nftl3XDLJyc41hW/B3pLOruobKv08WTgjIioj4h6YAjwmcJVUWU6hOT8HCQdm55oCLAbyYjmb0TEkelO84y03hkkydXJEbG+MCNJg4F7gFMi4pnOLqiZlSftjf01SZJTtvT7fTDwXEQsSb/Xw9Lzbn4PbCFpTFH9vSV9spJtt3xygmOdlvayfB44VNLzkv5EcrXT5cBRwJSiuv8ApgMjOpjtSemvtqeAfUiueILkfJpFaRf3zcCXI2JdK+8fT3JlxR/T+VyWll8G9Ad+lpbP6vQCm1m5ruH9JxG3pXDo6mmgjuTw8gbSfc1xwKfTy8TnA1cBf61Mcy3PPNimmZmZ5Y57cMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLnf8FgEIu5MDrknwAAAAASUVORK5CYII=",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['simSeconds'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)\n",
-    "geo_meanC = df_gap22_ram_prob['simSeconds'].astype(float)\n",
-    "geo_meanR = df_gap22_ram['simSeconds'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['simSeconds'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)\n",
-    "geo_meanC = df_gap25_ram_prob['simSeconds'].astype(float)\n",
-    "geo_meanR = df_gap25_ram['simSeconds'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['simSeconds'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)\n",
-    "geo_meanC = df_npbC_ram_prob['simSeconds'].astype(float)\n",
-    "geo_meanR = df_npbC_ram['simSeconds'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['simSeconds'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)\n",
-    "geo_mean = df_npbD_ram_prob['simSeconds'].astype(float)\n",
-    "geo_mean = df_npbD_ram['simSeconds'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,2.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_ram[i]/gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,  npb_C_ram[i]/npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Speedup\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,2.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_ram[i]/gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    \n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_ram[i]/npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    \n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Speedup\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = (df_gap22_ram_prob['foundCandidBSlotRMC'].astype(float)+df_gap22_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "gap_22 = (df_gap22_ram_prob['numRdMissClean'].astype(float)+df_gap22_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "gap_25_prob = (df_gap25_ram_prob['foundCandidBSlotRMC'].astype(float)+df_gap25_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "gap_25 = (df_gap25_ram_prob['numRdMissClean'].astype(float)+df_gap25_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "npb_C_prob = (df_npbC_ram_prob['foundCandidBSlotRMC'].astype(float)+df_npbC_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "npb_C = (df_npbC_ram_prob['numRdMissClean'].astype(float)+df_npbC_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "npb_D_prob = (df_npbD_ram_prob['foundCandidBSlotRMC'].astype(float)+df_npbD_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "npb_D = (df_npbD_ram_prob['numRdMissClean'].astype(float)+df_npbD_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   100*gap_22_prob[i]/gap_22[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,  100*npb_C_prob[i]/npb_C[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Percentage of probed Rd-MC and Rd-Md\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   100*gap_25_prob[i]/gap_25[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    \n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   100*npb_D_prob[i]/npb_D[i],   width=1, color=cmap(1))\n",
-    "    \n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Percentage of probed Rd-MC and Rd-Md\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = (df_gap22_ram_prob['foundCandidBSlotRMC'].astype(float))\n",
-    "gap_22 = (df_gap22_ram_prob['numRdMissClean'].astype(float))\n",
-    "\n",
-    "gap_25_prob = (df_gap25_ram_prob['foundCandidBSlotRMC'].astype(float))\n",
-    "gap_25 = (df_gap25_ram_prob['numRdMissClean'].astype(float))\n",
-    "\n",
-    "npb_C_prob = (df_npbC_ram_prob['foundCandidBSlotRMC'].astype(float))\n",
-    "npb_C = (df_npbC_ram_prob['numRdMissClean'].astype(float))\n",
-    "\n",
-    "npb_D_prob = (df_npbD_ram_prob['foundCandidBSlotRMC'].astype(float))\n",
-    "npb_D = (df_npbD_ram_prob['numRdMissClean'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   100*gap_22_prob[i]/gap_22[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,  100*npb_C_prob[i]/npb_C[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Percentage of probed Rd-MC\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   100*gap_25_prob[i]/gap_25[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    \n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   100*npb_D_prob[i]/npb_D[i],   width=1, color=cmap(1))\n",
-    "    \n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Percentage of probed Rd-MC\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_308484/3573616793.py:27: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  plt.bar(i*tickSize-barWidth/2,   100*gap_22_prob[i]/gap_22[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-      "/tmp/ipykernel_308484/3573616793.py:31: RuntimeWarning: invalid value encountered in double_scalars\n",
-      "  plt.bar((offset+i)*tickSize-barWidth/2,  100*npb_C_prob[i]/npb_C[i],   width=1, color=cmap(1))\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = (df_gap22_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "gap_22 = (df_gap22_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "gap_25_prob = (df_gap25_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "gap_25 = (df_gap25_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "npb_C_prob = (df_npbC_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "npb_C = (df_npbC_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "npb_D_prob = (df_npbD_ram_prob['foundCandidBSlotRMD'].astype(float))\n",
-    "npb_D = (df_npbD_ram_prob['numRdMissDirty'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   100*gap_22_prob[i]/gap_22[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,  100*npb_C_prob[i]/npb_C[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Percentage of probed Rd-Md\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,100])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   100*gap_25_prob[i]/gap_25[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    \n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   100*npb_D_prob[i]/npb_D[i],   width=1, color=cmap(1))\n",
-    "    \n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Percentage of probed Rd-Md\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['actDelayedDueToTagAct'].astype(float)/1000\n",
-    "gap_25_prob = df_gap25_ram_prob['actDelayedDueToTagAct'].astype(float)/1000\n",
-    "npb_C_prob = df_npbC_ram_prob['actDelayedDueToTagAct'].astype(float)/1000\n",
-    "npb_D_prob = df_npbD_ram_prob['actDelayedDueToTagAct'].astype(float)/1000\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "#app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "#app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,150])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"ACT delayed (K)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "#app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "#app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,150])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"ACT delayed (K)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3 (ipykernel)",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.10"
-  },
-  "orig_nbformat": 4,
-  "vscode": {
-   "interpreter": {
-    "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
-   }
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/plots_1GBdramCache/data-plots.ipynb b/plots_1GBdramCache/data-plots.ipynb
index cb3ad813da..2596a775b4 100644
--- a/plots_1GBdramCache/data-plots.ipynb
+++ b/plots_1GBdramCache/data-plots.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -14,9 +14,6 @@
     "import statistics\n",
     "\n",
     "cmap = plt.get_cmap('Set1')\n",
-    "cmap2 = plt.get_cmap('tab20')\n",
-    "cmap3 = plt.get_cmap('tab20c')\n",
-    "\n",
     "\n",
     "Stats = ['simSeconds ',\n",
     "'hostSeconds ',\n",
@@ -91,22 +88,8 @@
     "'system.mem_ctrl.avgTimeInLocRead ',\n",
     "'system.mem_ctrl.avgTimeInLocWrite ',\n",
     "'system.mem_ctrl.avgTimeInFarRead ',\n",
-    "'system.mem_ctrl.missRatio ',\n",
-    "'system.loc_mem_ctrl.dram.actDelayedDueToTagAct ',\n",
-    "'system.loc_mem_ctrl.dram.avgFBLenEnq',\n",
-    "'system.loc_mem_ctrl.dram.avgReadFBPerEvent',\n",
-    "'system.loc_mem_ctrl.dram.totNumberRefreshEvent',\n",
-    "'system.loc_mem_ctrl.dram.totReadFBSent',\n",
-    "'system.loc_mem_ctrl.dram.totReadFBFailed',\n",
-    "'system.loc_mem_ctrl.dram.totReadFBByRdMC',\n",
-    "'system.loc_mem_ctrl.dram.totStallToFlushFB',\n",
-    "'system.loc_mem_ctrl.dram.totPktsPushedFB',\n",
-    "'system.loc_mem_ctrl.dram.maxFBLenEnq',\n",
-    "'system.loc_mem_ctrl.dram.refSchdRFB',\n",
-    "'system.mem_ctrl.totTimeTagCheckResRdH',\n",
-    "'system.mem_ctrl.totTimeTagCheckResRdMC',\n",
-    "'system.mem_ctrl.totTimeTagCheckResRdMD'\n",
-    "]\n",
+    "'system.mem_ctrl.missRatio '\n",
+    "          ]\n",
     "\n",
     "dfCols = [\n",
     "         'app',\n",
@@ -183,21 +166,7 @@
     "         'avgTimeInLocRead',\n",
     "         'avgTimeInLocWrite',\n",
     "         'avgTimeInFarRead',\n",
-    "         'missRatio',\n",
-    "         'actDelayedDueToTagAct',\n",
-    "         'avgFBLenEnq',\n",
-    "         'avgReadFBPerEvent',\n",
-    "         'totNumberRefreshEvent',\n",
-    "         'totReadFBSent',\n",
-    "         'totReadFBFailed',\n",
-    "         'totReadFBByRdMC',\n",
-    "         'totStallToFlushFB',\n",
-    "         'totPktsPushedFB',\n",
-    "         'maxFBLenEnq',\n",
-    "         'refSchdRFB',\n",
-    "         'totTimeTagCheckResRdH',\n",
-    "         'totTimeTagCheckResRdMC',\n",
-    "         'totTimeTagCheckResRdMD'\n",
+    "         'missRatio'\n",
     "\n",
     "         ]\n",
     "##########################################################\n",
@@ -260,15 +229,12 @@
     "    df['BWBloat'] =  (df['loc1AvgRdBW'].astype(float) + df['loc1AvgWrBW'].astype(float) +\n",
     "                      df['loc2AvgRdBW'].astype(float) + df['loc2AvgWrBW'].astype(float) +\n",
     "                      df['farAvgRdBW'].astype(float) + df['farAvgWrBW'].astype(float)) / ((df['avgRdBWSys'].astype(float) + df['avgWrBWSys'].astype(float)) / 1000000)\n",
-    "    \n",
-    "    df['avgTCLatRdM'] = (df['totTimeTagCheckResRdMC'].astype(float)+df['totTimeTagCheckResRdMD'].astype(float)) / (df['numRdMissClean'].astype(float)+df['numRdMissDirty'].astype(float))\n",
-    "\n",
     "    return df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 66,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -298,40 +264,29 @@
     "df_npbD_noDC = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/noDC/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n"
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Baseline Analysis"
-   ]
-  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 93,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": "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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -395,31 +350,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 90,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -479,31 +430,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 91,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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r2+pbwQRH0u7AaGAv4C/AyIi4t9KBmZmZmZWq0CDjRcBrZJdujweWp/JdACLikcqGZ/XGA57NzKwrKNSCs4jsqqkDgQPIrp5qFWTrR5mZmZnVlULz4AyrUhxmZmZmZVPsYptmZmZmXYYTHDMzM2s4TnDMzMys4RS6imqX9h73VVRmZmZWjwpdRfXjdh4r6SoqSVsA1wKbpmNMjohL0+zINwIDyK7eGhURr3b0+GZrMl/Gb2aWKXQV1b4VqHM58J2IeERST2COpLuAY4AZEXGepNOB08lWGDczMzPrkIIzGbeStAMwiGyZBgAi4tqOVhgRLUBLur9M0nxgc2AEMCztNpVslXEnOFb33GpiZlZ/ikpwJJ1FlnwMAqYDnydbtqHDCU6b4w4APg08CGyakh+AF8i6sMzy8lo6ZmbWnmKvovoKMBx4ISLGAjsDG3amYkn/AfwWODEi3sh9LCKCbHxOvueNlzRb0uylS5d2JgQzMzNrUMUmOG9HxEpguaQNgJeALUqtVFJ3suTm+oi4NRW/KKlferxfquNDImJyRAyJiCF9+/YtNQQzMzNrYMWOwZktqRfwC2AO8CZwfykVShJwFTA/Ii7Keej3wBjgvHR7eynHLxd3gZiZmXVdRSU4EfHNdPdnku4ANoiIx0uscw/gKGCepMdS2QSyxOYmSeOAxcCoEo9vZmZmZdCVL6IodpDxjIgYDhARi9qWdURE/IVVVyXP1eHjmZmZmbVVaCbjHsB6wMaSNuKDxGQDsku7zczMzOpOoRacrwEnApsBucsyvAFcVqGYzMzMzDql0EzGlwKXSvp2RPy0SjGZmZmZdUqxV1H9XNLxwN5puwn4eUS8V5GozMzMzDqh2ATnCqB7uoXsKqgrgWMrEZSZmZlZZxQaZLx2RCwHPhMRO+c8dLekuZUNzczMzKw0hWYyfijdrpC0dWuhpI8BKyoWlZmZmVknFOqiar0s/BRgpqRn0/YAYGylgjIzMzPrjEIJTl9JJ6f7PwfWSvdXkK0CPrNSgZmZmZmVqlCCsxbwH3x45uG1gZ4VicjMzMyskwolOC0RcXZVIjEzMzMrk2LH4JiZFa0cC/RB7RbpM7Our1CCU9XFLyUdBFxK1jU2JSLOq2b9ZmZWXV15tWqrb4WWavjfagUiaS3gcuBzQDPwsKTfR8RT1YrBzMys1twCWh6F5sGppl2BhRHxbES8C9wAjKhxTGZmZtYF1VOCsznwfM52cyozMzMz6xBFRK1jAEDSV4CDIuLYtH0U8NmI+Fab/cYD49PmdsAzVQ10VRsDL9ew/lz1Eku9xAGOJZ96iQMcSz71Egc4lnzqJQ5wLLm2ioi+bQuLXWyzGpYAW+Rs909lq4iIycDkagXVHkmzI2JIreOA+omlXuIAx1LPcYBjqec4wLHUcxzgWIpRT11UDwPbShooaR3gCOD3NY7JzMzMuqC6acGJiOWSvgXcSXaZ+C8j4skah2VmZmZdUN0kOAARMR2YXus4OqAuusqSeomlXuIAx5JPvcQBjiWfeokDHEs+9RIHOJaC6maQsZmZmVm51NMYHDMzM7OycIJTJEkDJD1RjzFI2kvSk5Iek/SRWsRm9UlSL0nfrHUc0O7n90RJ69Uipnoh6XhJ8yW9JWlQDeP4a63qziXpzVrHYF2fE5zGcCQwKSI+FRFv1zqYepaWBFmT9ALqIsFpx4nAGp3gkL1HnwNuBmqW4ETE7rWq26zcnOB0zNqSrk//ad0iaT1Jn5H0V0lzJT0kqWeVYzgeGAX8TyrvJ2lWas15QtJelQxG0tGSHk+//3WSNpV0W9qeK6lqJ8zUQvB0nvdokaTzJT0CHFbG+taX9Mf0ez4h6XBJ50l6Kr0mF6b9DkuPz5U0K5UdI+l2SU2S/ibprHLF1cZ5wNbp83CBpO9KmpdiqcVitvk+v5sBMyXNrEYAeT6zW0t6IL0u51S79UDSz4CPAc8BY4AL0vu1dTXjSLG8mW6reh5pJ55hkqblbF8m6ZgK19l6HrlG0oL0ed1f0n3pu7qrpL6S7kot51MkLZa0cQVjyneuWSTpR+lz+5CkbSpVf04cq7TCSjpF0vclfVXSwym+36peWmQjwj9F/AADgAD2SNu/BE4DngU+k8o2ANaucgynANcAX0ll3wH+O91fC+hZwXg+CSwANk7bvYEbgRNz6t+wxu/RKcAi4LQK1PefwC9ytrcim1m7dfB+r3Q7D9i8TdkxQAvQB/gI8AQwpEKvyRPp/ueBvwLrtb5f1Xpvinh/Nq5SDPk+s9OA0Wn768Cb1XxdUr2LyGaDff+7XIuf1t+9mueRAnEMA6bllF8GHFPhugcAy4EdyRoB5qTPqsjWR/xdiuOMtP9B6XNdsc9wnnPNhukz0/oeHZ37OlX4tXkiZ/sU4PtAn5yyc4BvV/Pzsroft+B0zPMRcV+6/yvgQKAlIh4GiIg3ImJ5lWPYs83jDwNjJX0f2DEillUwlv2AmyPiZXh/9fn9gCvT9oqIeL2C9eezutfnxgrUNQ/4XGod2ots5u13gKskfRn4V9rvPuAaSV8l+2PR6q6IeCWybsVb+fB7WW77A1dHxL/g/fer2gp9fist32d2KFnXEMCvqxxPvarmeaQePRcR8yJiJfAkMCOyv97zyP7I70m2IDQRcQfwaoXjWeVck3Ne/U3O7dAKx9CeHSTdK2ke2ZCJT9Ywlvc5wemYttfUv1EHMayyHRGzgL3J/theI+noagVWJ1b3+rxV9ooiFgC7kJ18zgEmALsCtwCHAHek/b4OfI9sKZI5kvoUiLWRrYm/c5dTR+eR5az6d6pHler9d879lTnbK6nB/HFtzzWSJrY+lLtbFUJZ3ftxDfCtiNgR+AHVe5/a5QSnY7aU1Jol/x/gAaCfpM8ASOopqdIf/rYx/CX3QUlbAS9GxC+AKWRfikq5Gzis9Q+2pN7ADOAbaXstSRtWsP582n19yknSZsC/IuJXwAVkfxA2jGzCypOAndN+W0fEgxExEVjKB2uufU5Sb2VXvh1K1tJTbsuA1nFhd5H9V75eiqt3BeorJN/7kxtjpeX7zD5A1gUA2RIxtVTN12K1qnweac9iYJCkdSX1AobXKI627iMb+4ikA4CNKllZnnNN6/txeM7t/ZWMIXkR2ERSH0nrkv0jB9lntkVSd7IWnLrgBKdjngGOkzSf7AP9U7IP1k8lzSX7A1LpzLVtDFe2eXwYMFfSoym2SysVSGRLaZwL3JN+/4uAE4B9U1PlHKp/RUih16ecdgQekvQYcBbZfy7TJD1O9of75LTfBWkg4BNkY2DmpvKHgN8CjwO/jYjZ5Q4wIl4B7kt1Dydb3212ivmUctdXhHzvz2TgjmoMMl7NZ/ZE4OT0vm0DVLtbNdcNwKmSHq3FIOMcw6jSeaQ9EfE8cBPZGLWbgEdrEUcePwAOSN+rw4AXyJLTSml7rjknlW+UPrcnkP1TVVER8R5wNtm56y7g6fTQmcCDZInf0/mfXX2eydgahqQBZAPtdqh1LIWkK0GGRMS3ah3Lmi61aL0dESHpCLIBxyNqHZfVr9R6sSKyNRSHAldGxKeqHMMisnPIy9Wstyupq7WozMxqYDBwmSQBrwH/VdtwrAvYErhJUjfgXeCrNY7H8nALjpmZmTUcj8ExMzOzhuMEx8zMzBqOExwzMzNrOE5wrMtQts7VryU9K2mOpPsljcx5/BJJS9LAv9ayYyQtVbamzlNpNuG25U8qrVuVHttN0oPpsflpNtd88Vwv6Rlla8P8Ms0BgaQjla11NE/ZOmU7V/SFMVuDSApJP87ZPqX1O6psXaQl+mANrS/lKX9a0pW554k2x/+opBsk/T2dZ6ZL+nhVfjkrKyc41iWkK1x+B8yKiI9FxGCySdn6p8e7ASOB54F92jz9xnQJ5zDgh5I2zS2PiE+SXQnROmnWVGB8es4OZPNv5HM98AmyOSo+Ahybyp8D9kmzev4P2TwvZlYe/wa+rNUvbnlx+u4eBvwyJ5FpLR9E9p1te55oPc/cBjRFxNbpPHMGsGnbfa3+OcGxrmI/4N2I+FlrQUQsjoifps1hZGvGXAmMzneAiHgJ+DvZopjvUzb79Pp8sJ7MJmQLYbaup/XUao43PRKyia/6p/K/RkTrsR5oLTezslhO9k9DuxPbRcT8tG/bRGgdsglZ860ftS/wXpvzzNyIuLdTEVtNOMGxruKTwCPtPD6abMG524AvtHYX5ZL0MeBjwMJUdHiaGXQJ2arSf0jlFwPPSLpN0tcktTs7darrKNLaU22MA/7U3vPNrMMuB45UO0vBSPos2dpRS1PRSen73gIsiIjH8jxtB7IZ2K0BOMGxLknS5ZLmSnpY0jrAwcDvIuINsinDD8zZvTWR+Q3wtZxVtFu7rj5KtojdqQARcTYwBPgz2XpJ+RKXXFeQdZ2t8l+epH3JEpzvlvyLmtmHpO/5tcDxeR5uTWQuBA6PDyZ7a+2i2gRYP81abQ3MCY51FU+Ss+BfRBxHtrZSX7JkphcwL01fvierdlO1jrX5bETc1vbA6QT4B7LFMlvL/h4RV6Y6dla2uNydaZDilNb9JJ2VYjg595iSdiJbpHBEWg/KzMrrErJ/INZvU35x+r7vla9rKa2ndAewt6Qt0nf6MUlfJzvPDK504FYdTnCsq7gb6CHpGzll66Xb0cCxETEgIgYAA8lW6l6P4u1JNj4HSV9Igw0BtgVWAK9FxIHpxHls2u9YsuRqdESsbD2QpC2BW4GjImJBR39RMysstcTeRJbkFC19t/cA/h4Rz6fv9KfSuJu7gXUljc/ZfydJe5UzdqsOJzjWJaRWlkOBfSQ9J+khsqudzgIOAv6Ys+9bZKt5f7HAYQ9P/7k9Dnya7IonyMbTPJOaua8DjoyIFXme/zOyqyvuT8eZmMonAn2AK1J52VcJNzMAfsyHBxGvTmvX1RPAWmRdy6tI55mRwP7pMvEngUlkq4VbF+O1qMzMzKzhuAXHzMzMGo4THDMzM2s4TnDMzMys4TjBMTMzs4bjBMfMzMwajhMcMzMzazhOcMzMzKzhOMExMzOzhvP/AesU0o+YDkIkAAAAAElFTkSuQmCC",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -563,53 +510,49 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
-    "app_gap = df_gap22_cas['app']\n",
-    "gap_22_cas = df_gap22_cas['simSeconds'].astype(float)*1000\n",
+    "app_gap = df_gap22_ram['app']\n",
     "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)*1000\n",
+    "gap_22_orc = df_gap22_orc['simSeconds'].astype(float)*1000\n",
     "gap_22_noDC = df_gap22_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "\n",
-    "gap_25_cas = df_gap25_cas['simSeconds'].astype(float)*1000\n",
     "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)*1000\n",
+    "gap_25_orc = df_gap25_orc['simSeconds'].astype(float)*1000\n",
     "gap_25_noDC = df_gap25_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "\n",
-    "app_npb = df_npbC_cas['app']\n",
-    "npb_C_cas = df_npbC_cas['simSeconds'].astype(float)*1000\n",
+    "app_npb = df_npbC_ram['app']\n",
     "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)*1000\n",
+    "npb_C_orc = df_npbC_orc['simSeconds'].astype(float)*1000\n",
     "npb_C_noDC = df_npbC_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
-    "npb_D_cas = df_npbD_cas['simSeconds'].astype(float)*1000\n",
     "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)*1000\n",
+    "npb_D_orc = df_npbD_orc['simSeconds'].astype(float)*1000\n",
     "npb_D_noDC = df_npbD_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "\n",
@@ -619,13 +562,13 @@
     "plt.ylim([0,3])\n",
     "\n",
     "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i]/gap_22_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i]/gap_22_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
+    "    plt.bar(i*3,   gap_22_ram[i]/gap_22_noDC[i],   width=1, color=cmap(1), label='TDRAM' if i==0 else None)\n",
+    "    plt.bar(i*3+1, gap_22_orc[i]/gap_22_noDC[i],   width=1, color=cmap(2), label='Oracle' if i==0 else None)\n",
     "\n",
     "offset = i*3+2\n",
     "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i]/npb_C_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i]/npb_C_noDC[i],   width=1, color=cmap(2))\n",
+    "    plt.bar(offset+i*3+1,   npb_C_ram[i]/npb_C_noDC[i],     width=1, color=cmap(1))\n",
+    "    plt.bar(offset+i*3+2,   npb_C_orc[i]/npb_C_noDC[i],   width=1, color=cmap(2))\n",
     "\n",
     "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
     "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
@@ -635,7 +578,7 @@
     "plt.axhline(y=1, color='grey')\n",
     "\n",
     "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
+    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
     "plt.tight_layout()\n",
     "\n",
     "# Multi bar Chart2\n",
@@ -644,13 +587,13 @@
     "plt.ylim([0,3])\n",
     "\n",
     "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i]/gap_25_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i]/gap_25_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
+    "    plt.bar(i*3,   gap_25_ram[i]/gap_25_noDC[i],   width=1, color=cmap(1), label='TDRAM' if i==0 else None)\n",
+    "    plt.bar(i*3+1, gap_25_orc[i]/gap_25_noDC[i],   width=1, color=cmap(2), label='Oracle' if i==0 else None)\n",
     "\n",
     "offset = i*3+2\n",
     "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i]/npb_D_noDC[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i]/npb_D_noDC[i],   width=1, color=cmap(2))\n",
+    "    plt.bar(offset+i*3+1,   npb_D_ram[i]/npb_D_noDC[i],     width=1, color=cmap(1))\n",
+    "    plt.bar(offset+i*3+2,   npb_D_orc[i]/npb_D_noDC[i],   width=1, color=cmap(2))\n",
     "\n",
     "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
     "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
@@ -660,37 +603,33 @@
     "plt.axhline(y=1, color='grey')\n",
     "\n",
     "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
-    "plt.legend(fontsize=8, ncol=1)\n",
+    "plt.legend(fontsize=9, ncol=2)\n",
     "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 71,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -698,25 +637,21 @@
     "app_gap = df_gap22_cas['app']\n",
     "gap_22_cas = df_gap22_cas['simSeconds'].astype(float)*1000\n",
     "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)*1000\n",
-    "gap_22_orc = df_gap22_orc['simSeconds'].astype(float)*1000\n",
     "gap_22_noDC = df_gap22_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "\n",
     "gap_25_cas = df_gap25_cas['simSeconds'].astype(float)*1000\n",
     "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)*1000\n",
-    "gap_25_orc = df_gap25_orc['simSeconds'].astype(float)*1000\n",
     "gap_25_noDC = df_gap25_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "\n",
     "app_npb = df_npbC_cas['app']\n",
     "npb_C_cas = df_npbC_cas['simSeconds'].astype(float)*1000\n",
     "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)*1000\n",
-    "npb_C_orc = df_npbC_orc['simSeconds'].astype(float)*1000\n",
     "npb_C_noDC = df_npbC_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "npb_D_cas = df_npbD_cas['simSeconds'].astype(float)*1000\n",
     "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)*1000\n",
-    "npb_D_orc = df_npbD_orc['simSeconds'].astype(float)*1000\n",
     "npb_D_noDC = df_npbD_noDC['simSeconds'].astype(float)*1000\n",
     "\n",
     "\n",
@@ -726,13 +661,13 @@
     "plt.ylim([0,3])\n",
     "\n",
     "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i]/gap_22_orc[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i]/gap_22_orc[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
+    "    plt.bar(i*3,   gap_22_cas[i]/gap_22_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
+    "    plt.bar(i*3+1, gap_22_ram[i]/gap_22_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
     "\n",
     "offset = i*3+2\n",
     "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i]/npb_C_orc[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i]/npb_C_orc[i],   width=1, color=cmap(2))\n",
+    "    plt.bar(offset+i*3+1,   npb_C_cas[i]/npb_C_noDC[i],     width=1, color=cmap(1))\n",
+    "    plt.bar(offset+i*3+2,   npb_C_ram[i]/npb_C_noDC[i],   width=1, color=cmap(2))\n",
     "\n",
     "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
     "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
@@ -741,7 +676,7 @@
     "plt.axvline(x=offset, color='black')\n",
     "plt.axhline(y=1, color='grey')\n",
     "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to ideal cache\")\n",
+    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
     "plt.legend(fontsize=8, ncol=1)\n",
     "plt.tight_layout()\n",
     "\n",
@@ -751,13 +686,13 @@
     "plt.ylim([0,3])\n",
     "\n",
     "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i]/gap_25_orc[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i]/gap_25_orc[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
+    "    plt.bar(i*3,   gap_25_cas[i]/gap_25_noDC[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
+    "    plt.bar(i*3+1, gap_25_ram[i]/gap_25_noDC[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
     "\n",
     "offset = i*3+2\n",
     "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i]/npb_D_orc[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i]/npb_D_orc[i],   width=1, color=cmap(2))\n",
+    "    plt.bar(offset+i*3+1,   npb_D_cas[i]/npb_D_noDC[i],     width=1, color=cmap(1))\n",
+    "    plt.bar(offset+i*3+2,   npb_D_ram[i]/npb_D_noDC[i],   width=1, color=cmap(2))\n",
     "\n",
     "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
     "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
@@ -766,38 +701,34 @@
     "plt.axvline(x=offset, color='black')\n",
     "plt.axhline(y=1, color='grey')\n",
     "\n",
-    "plt.ylabel(\"Execution time\\nnormalized to ideal cache\")\n",
+    "plt.ylabel(\"Execution time\\nnormalized to no-DRAM-$\")\n",
     "plt.legend(fontsize=8, ncol=1)\n",
     "plt.tight_layout()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 72,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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zPDw88OGHH8q+0ERFRaG4uBiurq5wd3fH/PnzAQArVqzAvHnz4O7ujpiYGHh5ecnaio6OxrFjx+Dk5ISJEyeib9++smVhYWHw9PSEl5cX3Nzc0L179xqvJDg7O8PS0lL2cHV1xZgxY+Ds7AxPT09YW1tXud7MmTMxefJk9O3bF3fu3MHPP/+MnTt3ws3NTTaA9uHDh/V5qZu0wsJC2UB9V1dX3LhxA0eOHJHdEepZfn5+cHR0xNq1a5GdnS137ADAuHHj5AbwP/Hqq6/ilVdeaZR9UEdbt26FnZ0dXF1d0alTJwwbNgxz5szB5MmTq6wfHR2N0aNHw8PDA8nJyXLH81dffYW///4bzs7OcHd3rzKJFvt+qg+J8GxnORUpKytDZGSk7BZlylBQUAATExPk5+dDKpUqbbtERI3l/v37ssvoRUVFsruOkOo8evQI165dQ7t27aCnp6fqcIiIqlTduUrM92WlX7EoKiqqlP2npKTg1VdfRXh4uLLDISIiIiKiBlDnxKKkpASXLl1CWVmZQvVv3LgBb29vmJiYwMTEBKGhoXjw4AEmTpwILy8vGBgY4MiRI3UNh4iIiIiIVEh0YvHgwQMEBwfDwMAAzs7OyMjIAABMnz4dK1asqHa9OXPm4NGjR4iOjkavXr0QHR0NHx8fSKVSXLlyBd99951c30siIiIiImo6RCcW4eHhSE1NRVJSklz/q/79+2P79u3VrnfgwAFs2LAB06ZNw3fffQdBEDBu3DisX78elpaWdYueiIiIiIjUguh5LHbv3o3t27eje/fucvecdnZ2xpUrV6pd77///pPdKqtFixYwMDDA4MGD6xAyERFR06Mm90ohIqpSQ5yjRCcWOTk5aNGiRaXy+/fv1zrR3dP3J9bQ0ICOjo7YzRMRETUp2trakEgkyMnJgYWFBSeFJSK1IwgCcnJyIJFIoK2tXed2RCcWXbt2xZ49ezB9+nQA/zdT5pdffglvb+9q1xMEAR06dJDVLyoqgoeHR6XJUDgRDRERPU80NTVhaWmJzMxMpKenqzocIqIqSSQSWFpaQlNTs85tiE4sli1bhsGDB+P8+fMoKytDdHQ0zp8/jyNHjmD//v3Vrvf0TJZEREQvEiMjIzg4OKC0tFTVoRARVUlbW7teSQVQxwnyrly5ghUrViA1NRVFRUV46aWXEBYWBldX13oFo2ycII+InjecII/oxeW3e6io+gn+exopEnqeiPm+LPqKBQC0b98emzZtqlNwgiDg1KlTSE9Ph0QiQbt27eDh4cE+p0RERERETVidEgsAyM7ORnZ2NioqKuTK3dzcql1n3759CA4OxvXr12Ujz58kFzExMejdu3ddwyEiIiIiIhUSnVicOnUKAQEBuHDhQqXbUkkkEpSXl1e5XlpaGoYNGwYvLy9ERUXB0dERgiDg/PnzWLt2LYYMGYIzZ87Azs6ubntCREREREQqIzqxCAoKQocOHbB582a0bNlS4S5Ma9asQffu3ZGYmChX7ujoiJEjR6J///6IiorCunXrxIZEREREREQqJjqxuHr1KuLj42Fvby9qvaSkJCxfvrzKZRKJBDNnzkR4eLjYcIiIiIiISA1o1F5FXr9+/ZCamip6QxkZGTXeNcrFxQXXr18X3S4REREREame6CsWX375JQICAvDPP//AxcWl0ux8fn5+Va5XVFQEAwODats1MDDAgwcPxIZDRERERERqQHRicfToURw+fBi//vprpWU1Dd4GgPPnz+P27dtVLrtz547YUIiIiIiISE2ITiymT5+O8ePHY8GCBWjZsqWodfv161fpTlLA44REEATOZUFERET0nOge8bvCdY8tGtSIkZCyiE4scnNzMWvWLNFJxbVr18RuioiIGpiYD3qAH/ZERKQ40YnFq6++in379qF9+/ai1rOxsRG7KSIiIqJ6YTJNpDyiE4sOHTogPDwchw4dgqura6XB2yEhIQq35erqil9++QVWVlZiwyAiIiIiIjVSp7tCGRkZYf/+/di/f7/cMolEIiqxSE9PR2lpqdgQiIiISIn4qz8RKUJ0YsGxEkRERESNT2xC18KjkQIhUpDoCfIa0ssvvwx9fX1VhkBERERERA1AoSsWoaGhWLJkCQwNDREaGlpj3cjISIU3/ssvvyhctyrLly/Hzp07cfHiRejr66NHjx745JNP0LFjx3q1S0RERERE4iiUWCQnJ8vGQiQnJ9d7o+Xl5di9ezcuXLgAAHB2doafnx80NTVFtbN//36899578PT0RFlZGebPn4+BAwfi/PnzMDQ0rHecRERERESkGIUSi3379lX5d12kpaVh6NChyMzMlF1ZWL58OaysrLBnzx5Rt7H97bff5J7HxcWhRYsWOHXqFHr37l2vOImIiIiISHGix1gEBQWhsLCwUvn9+/cRFBRU6/ohISGws7PDjRs3cPr0aZw+fRoZGRlo166dqDtKVSU/Px8AYGZmVuXy4uJiFBQUyD2IiIiIiKj+RCcWW7ZswcOHDyuVP3z4EFu3bq11/f3792PlypVyX/7Nzc2xYsWKSrevFaOiogIzZ85Ez5494eLiUmWd5cuXw8TERPbg/BlERERERA1D4dvNFhQUQBAECIKAwsJC6OnpyZaVl5fjl19+QYsWLWptR1dXt8orHkVFRdDR0VE0nEree+89/PPPPzh06FC1dcLDw+UGnxcUFDC5ICIiamB+u4cqXDfBf08jRkJEyqRwYmFqagqJRAKJRIIOHTpUWi6RSLBo0aJa2xk2bBimTJmCzZs3o1u3bgCA48ePY+rUqfDz8xMR+v+ZNm0afv75Zxw4cACWlpbV1tPV1YWurm6dtkFERERERNVTOLHYt28fBEFA3759ER8fL9eVSUdHBzY2NmjTpk2t7axduxYBAQHw9vaGtrY2AKCsrAx+fn6Ijo4WFbwgCJg+fTp27dqFpKQktGvXTtT6REREysCZq4noRaBwYuHj4wPg8czb1tbWkEgkddqgqakpfvzxR1y+fBkXL14EAHTq1An29vai23rvvfewbds2/PjjjzA2Nsbt27cBACYmJpx4j4iIiIhIiRROLJ6wsbFpkA07ODjAwcGhXm1s2LABAODr6ytXHhsbi8DAwHq1TUREREREihOdWNRXeXk54uLikJiYiOzsbFRUVMgt/+uvvxRuSxCEhg6PiIiIiIjqQOmJxYwZMxAXF4ehQ4fCxcWlzl2qiIiIiIhIfSg9sfjuu+/w/fffY8iQIcreNBERERERNRKlJxY6Ojp1GqhN6ol3OiEiIiIioA4zb//333+YMGEC2rRpAy0tLWhqaso9avP+++8jOjqa4yOIiIiIiJ4joq9YBAYGIiMjAwsWLEDr1q1Fj5E4dOgQ9u3bh19//RXOzs6yuSye2Llzp9iQiIiIiIhIxUQnFocOHcLBgwfh7u5epw2amppi5MiRdVqXiIiIiIjUk+jEwsrKql7dmGJjY+u8LhERERERqSfRYyzWrFmDefPmIT09vRHCISIiIiKipkj0FYs33ngDDx48QPv27WFgYFBpjEReXl6DBUdERKrlt3uownUT/Pc0YiRERKTuRCcWa9asaYQwiIiIiOhFJeZHDIA/ZKgr0YlFQEBAY8RBRERERERNWJ0myCsvL8fu3btx4cIFAICzszP8/PwUmseCSJ2ImeCPk/sRERERVU90YpGWloYhQ4bg5s2b6NixIwBg+fLlsLKywp49e9C+ffta29i/fz9WrVolS0ycnJwwZ84cvPzyy2LDISJSKs42T0REVDXRiUVISAjat2+PY8eOwczMDACQm5uL8ePHIyQkBHv21Nzn7euvv8akSZPw6quvIiQkBABw+PBh9OvXD3FxcXjzzTfrsBtN24v0RYV9KImI6Hny+YivRdV/+8fxjRQJkeqJTiz2798vl1QAgLm5OVasWIGePXvWuv7SpUuxcuVKzJo1S1YWEhKCyMhILFmy5IVMLIiIiIiImjrR81jo6uqisLCwUnlRURF0dHRqXf/q1asYPnx4pXI/Pz9cu3ZNbDhERERERKQGRF+xGDZsGKZMmYLNmzejW7duAIDjx49j6tSp8PPzq3V9KysrJCYmwt7eXq587969sLKyEhsOERERUYMR22V3KMY2UiRETY/oxGLt2rUICAiAt7e3bHK8srIy+Pn5ITo6utb133//fYSEhCAlJQU9evQA8HiMRVxcnELrExERERGR+hGdWJiamuLHH3/E5cuXcfHiRQBAp06dKl2BqM4777yDVq1aYfXq1fj+++9l62/fvh0jRowQGw4REREREamBOs1jAQAODg5wcHCo07ojR47EyJEj67ppIiIiIiJSMwolFqGhoViyZAkMDQ0RGhpaY93IyMgal9vZ2eHkyZMwNzeXK7937x5eeuklXL16VZGQiIiIiIhIjSiUWCQnJ6O0tFT2d32kp6ejvLy8UnlxcTFu3rxZr7aJqGl4keZuIaoLzvlDRE2RQonFvn37qvxbjISEBNnfv//+O0xMTGTPy8vLkZiYCFtb2zq1TUREREREqiV6jEVQUBCio6NhbGwsV37//n1Mnz4dMTExVa7n7+8PAJBIJAgICJBbpq2tDVtbW6xevVpsOEREREREpAZEJxZbtmzBihUrKiUWDx8+xNatW6tNLCoqKgAA7dq1w8mTJ9G8efM6hEtERERE1LjYZbduFE4sCgoKIAgCBEFAYWEh9PT0ZMvKy8vxyy+/oEWLFrW2w9m1qalin2ciIiKi6imcWJiamkIikUAikaBDhw6VlkskEixatKhBgyMiIiIioqZB4cRi3759EAQBffv2RXx8PMzMzGTLdHR0YGNjgzZt2jRKkERERFS9z0d8Lar+2z+Ob6RIiOhFpnBi4ePjA+BxVyZra2tIJJJGC4qI6GnshtY08MstEdGLTfTg7evXr+P69evVLu/du3e9AiIiIiIioqZHdGLh6+tbqezpqxdVTX5XUFCgcPtSqVRsSERE9ALgXVqIiNSb6MTi7t27cs9LS0uRnJyMBQsWYOnSpVWu82TgtyKqSkyIiIjEYhe6poFd6IieH6ITi6dnzH5iwIAB0NHRQWhoKE6dOlVp+dOzdaenp2PevHkIDAyEt7c3AODo0aPYsmULli9fLjYces7xA4eaOn65JSKiF4XoxKI6LVu2xKVLl6pc9mTgNwAsXrwYkZGRGDt2rKzMz88Prq6u+OKLLyrNyk1ERERE9DT+8KieRCcWZ86ckXsuCAKysrKwYsUKuLu717r+0aNHsXHjxkrlXbt2xVtvvSU2HCKi5wo/LImImh5enX5MdGLh7u4OiUQCQRDkyrt3746YmJha17eyssKmTZuwcuVKufIvv/wSVlZWYsMhUlv8gkhEROqMn1PU0EQnFteuXZN7rqGhAQsLC+jp6Sm0flRUFEaNGoVff/0VXl5eAIATJ07g8uXLiI+PFxXLgQMH8Omnn+LUqVPIysrCrl274O/vL6oNIiIiIiKqP9GJhY2NTb02OGTIEPz777/YsGEDLl68CAAYPnw4pk6dKvqKxf3799G5c2cEBQXh1VdfrVdcTQkvtxHVjL/CERGROhPzOdWUPqNEJxYhISGwt7dHSEiIXPn69euRlpaGNWvW1NqGlZUVli1bJnbTlQwePBiDBw+udzslJSUoKSmpdzt1pQlxt9jVKNcQVX/DyK0K1w3ePkZU240de4VGhaj6Yv+PYuJXt9ibsqZ83Kh77CUlJdDW1q709xONGb86vV8B8a99Y74H1f24qQ2Pm+q9yOeb2jTlz1h1eu1V/f1AzPYlwrODJWrRtm1bJCQkoEuXLnLlp0+fhp+fHzIzM2tt4+DBg/j8889x9epV7NixA23btsVXX32Fdu3aoVevXmLCkZFIJLV2hSouLkZxcbHseUFBAaysrDBv3jyFu3IREREREb0oHj16hBUrViA/P7/WiazFpVcAcnNzq5zLQiqV4s6dO7WuHx8fj0GDBkFfXx+nT5+WfdHPz89vkKsYNVm+fDlMTExkDw4WJyIiIiJqGKKvWLi4uGDq1KmYNm2aXPm6deuwYcMGnD9/vsb1PTw8MGvWLEycOBHGxsZITU2FnZ0dkpOTMXjwYNy+fVv8XqB+VyxycnJqzcAaU5+le0XVt3DbIKr+oK9fU7iu2K5Q6hQ70Ljxq1vsTVlTPm7UPfb79++jZcuWAID//vsPhoaGcssbM351er8C4l/774eLu4GIGOp+3NSGx031XuTzTW2a8mesOr32qv5+UFBQAAsLC4WuWIgeYxEaGopp06YhJycHffv2BQAkJiZi9erVCo2vuHTpEnr37l2p3MTEBPfu3RMbjii6urrQ1dWtVK6jowMdHZ1G3XZNyqEpqn6Fprh+hRoVil+YEvs6qFPsQOPGr26xN2VN+bhR99hLS0tRWloqW/bs8saMX53er4D4174x34PqftzUhsdN9V7k801tmvJnrDq99qr+fiBm+6ITi6CgIBQXF2Pp0qVYsmQJAMDW1hYbNmzAxIkTa12/VatWSEtLg62trVz5oUOHYGdnJzYcIiKiBsG7iRER1Y/oxAIA3nnnHbzzzjvIycmBvr4+jIyMFF538uTJmDFjBmJiYiCRSHDr1i0cPXoUs2fPxoIFC0TFUVRUhLS0NNnza9euISUlBWZmZrC2thbVFhERERER1V2dEouysjIkJSXhypUrePPNNwEAt27dglQqrTXJmDdvHioqKtCvXz88ePAAvXv3hq6uLmbPno3p06eLiuPvv/9Gnz59ZM9DQ0MBAAEBAYiLixO3U0RNTPeI30XVP7ZoUCNFQkRERFSHxOL69et45ZVXkJGRgeLiYgwYMADGxsb45JNPUFxcjI0bN9a4vkQiwQcffIA5c+YgLS0NRUVFcHJyEnXV4wlfX1+IHHtO9MISM7EiJ1UkIiIisUTfbnbGjBno2rUr7t69C319fVn5yJEjkZiYWOv6W7duxYULF6CjowMnJyd069YNRkZGePToEbZuVXwiNyIiIiIiUh+iE4uDBw/iww8/rDRC3NbWFjdv3qx1/cDAQHTr1g3x8fK39cvPz8ekSZPEhkNERERERGpAdGJRUVGB8vLK05xnZmbC2NhYoTYWLVqECRMm4KOPPhK7eSIiIiIiUkOiE4uBAwfKzVchkUhQVFSEiIgIDBkyRKE2xo8fj7/++guff/45Ro8ejYcPH4oNg4iIiIiI1IjoxGL16tU4fPgwnJyc8OjRI7z55puyblCffPJJretLJBIAQPfu3XH8+HGkpaWhR48eSE9PFx08ERERERGpB9F3hbK0tERqaiq2b9+O1NRUFBUVITg4GOPGjZMbzF2dp+/iZG1tjSNHjmDcuHEYMGCA2FCIqJFwojAiIiISS3RikZOTAwsLC4wbNw7jxo2TW3b27Fm4urrWuH5ERITcrWUNDAywa9cuRERE4MCBA2LDISIiIiIiNSC6K5Srqyv27Kl8j/tVq1ahW7duta4fEREBAwODSuWLFi3Cvn37xIZDRERERERqQPQVi9DQUIwaNQqTJk1CZGQk8vLyMHHiRJw9exbbtm2rcp2EhAQMHjwY2traSEhIqLZtiUSC4cOHiw2JiIiIiIhUTHRiMXfuXAwYMAATJkyAm5sb8vLy4OXlhTNnzqBVq1ZVruPv74/bt2+jRYsW8Pf3r7ZtiURS5a1siYiIiIhIvYnuCgUA9vb2cHFxQXp6OgoKCvDGG29Um1QAj+e+aNGihezv6h5MKoiIiIiImibRicXhw4fh5uaGy5cv48yZM9iwYQOmT5+ON954A3fv3m2MGImIiIiISM2J7grVt29fzJo1C0uWLIG2tjY6deqEPn36YPz48XB1dUVmZmalddauXatw+yEhIWJDIiIiIiIiFROdWPzxxx/w8fGRK2vfvj0OHz6MpUuXVrlOVFSUQm1LJBImFkRERERETZDoxOLZpOIJDQ0NLFiwoMpl165dE7sZIiIiIiJqQhQeYzFkyBDk5+fLnq9YsQL37t2TPc/NzYWTk1ODBkdERERERE2Dwlcsfv/9dxQXF8ueL1u2DK+//jpMTU0BAGVlZbh06ZJCbWVmZiIhIQEZGRkoKSmRWxYZGaloSEREREREpCYUTiwEQajxuaISExPh5+cHOzs7XLx4UXbbWkEQ8NJLL9WpTSIiIiIiUq06zWNRH+Hh4Zg9ezbOnj0LPT09xMfH48aNG/Dx8cFrr72m7HCIiIiIiKgBKJxYSCQSSCSSSmViXbhwARMnTgQAaGlp4eHDhzAyMsLixYvxySefiG6PiIiIiIhUT1RXqMDAQOjq6gIAHj16hKlTp8LQ0BAA5MZf1MTQ0FA2rqJ169a4cuUKnJ2dAQB37twRFTwREREREakHhROLgIAAuefjx4+vVOfJlYiadO/eHYcOHUKnTp0wZMgQvP/++zh79ix27tyJ7t27KxoOERERERGpEYUTi9jY2AbZYGRkJIqKigAAixYtQlFREbZv3w4HBwfeEYqIiIiIqIkSPUFefdnZ2cn+NjQ0xMaNG5UdAhERERERNTClJxZPKyoqQkVFhVyZVCpVUTRERERERFRXSr/d7LVr1zB06FAYGhrCxMQEzZo1Q7NmzWBqaopmzZopOxwiIiIiImoASr9iMX78eAiCgJiYGLRs2bJOt6wlIiIiIiL1ovTEIjU1FadOnULHjh2VvWkiIiIiImokSu8K5enpiRs3bih7s0RERERE1IiUfsXiyy+/xNSpU3Hz5k24uLhAW1tbbrmbm5uyQyIiIiIionpSemKRk5ODK1euYNKkSbIyiUQCQRAgkUhQXl6u7JCIiIiIiKielJ5YBAUFwcPDA99++y0HbxMRERERPSeUnlhcv34dCQkJsLe3V/amiYiIiIiokSh98Hbfvn2Rmpqq7M0SEREREVEjUvoVi+HDh2PWrFk4e/YsXF1dKw3e9vPzU3ZIRERERERUT0pPLKZOnQoAWLx4caVlHLxNRERERNQ0Kb0rVEVFRbWPuiYV//vf/2Braws9PT14eXnhxIkTDRw1ERERERHVRKmJRWlpKbS0tPDPP/80WJvbt29HaGgoIiIicPr0aXTu3BmDBg1CdnZ2g22DiIiIiIhqptTEQltbG9bW1g3a3SkyMhKTJ0/GpEmT4OTkhI0bN8LAwAAxMTENtg0iIiIiIqqZ0sdYfPDBB5g/fz6++uormJmZ1autkpISnDp1CuHh4bIyDQ0N9O/fH0ePHq1Uv7i4GMXFxbLn+fn5AICCgoJ6xVFfZcX3RdUvfVAqqv7D0ocK1xX7WqhT7EDjxt+UYwfExd+UYwcaN351j/3+/ftyy579IYfHTfVe5OOmNjxuqsfjpnr8jK1eY343a2hPti8IQq11JYIitRqQh4cH0tLSUFpaChsbGxgaGsotP336tMJt3bp1C23btsWRI0fg7e0tK587dy7279+P48ePy9X/6KOPsGjRovrtABERERHRC+bGjRuwtLSssY7Sr1j4+/sre5My4eHhCA0NlT2vqKhAXl4ezM3Nm/wM4AUFBbCyssKNGzcglUpVHY4ojF11mnL8jF11mnL8jF01mnLsQNOOn7GrTlOP/wlBEFBYWIg2bdrUWlfpiUVERESDtdW8eXNoamriv//+kyv/77//0KpVq0r1dXV1oaurK1dmamraYPGoA6lU2mQPXsauOk05fsauOk05fsauGk05dqBpx8/YVaepxw8AJiYmCtVT+u1mnzh16hS+/vprfP3110hOTq5TGzo6OujSpQsSExNlZRUVFUhMTJTrGkVERERERI1L6VcssrOzMWbMGCQlJcmuFty7dw99+vTBd999BwsLC1HthYaGIiAgAF27dkW3bt2wZs0a3L9/H5MmTWqE6ImIiIiIqCpKv2Ixffp0FBYW4ty5c8jLy0NeXh7++ecfFBQUICQkRHR7b7zxBlatWoWFCxfC3d0dKSkp+O2339CyZctGiF596erqIiIiolJXr6aAsatOU46fsatOU46fsatGU44daNrxM3bVaerx14XS7wplYmKCvXv3wtPTU678xIkTGDhwIO7du6fMcIiIiIiIqAEo/YpFRUUFtLW1K5Vra2ujoqJC2eEQEREREVEDUHpi0bdvX8yYMQO3bt2Sld28eROzZs1Cv379lB0OERERERE1AKV3hbpx4wb8/Pxw7tw5WFlZycpcXFyQkJBQ68QbRERERESkfpR+xcLKygqnT5/Gnj17MHPmTMycORO//PILTp8+zaRCAb6+vpg5c6aqwxCltpgfPHiAUaNGQSqVQiKRcJwNkRppiuec54kgCJgyZQrMzMwgkUiQkpKi6pAU1pSPnaYcO5EqKf12swAgkUgwYMAADBgwQBWbJzWzZcsWHDx4EEeOHEHz5s0VnoSFqKnw9fWFu7s71qxZo+pQGpytra3sRyJqeL/99hvi4uKQlJQEOzs7tG7dGrt27YK/v7+qQ6vVzp07qxxTSUTPL5UkFomJiUhMTER2dnalAdsxMTGqCIlU6MqVK+jUqRNcXFxUHQo9paSkBDo6OqoOg+iFduXKFbRu3Ro9evRQdSiimZmZqToEIlIypXeFWrRoEQYOHIjExETcuXMHd+/elXtQ7crKyjBt2jSYmJigefPmWLBgAZ4MlSkuLkZYWBisrKygq6sLe3t7bN68WcURVx+zr68vVq9ejQMHDkAikcDX1xcA8Nlnn8HBwQF6enpo2bIlRo8erdodwOM7mq1cuRL29vbQ1dWFtbU1li5dCgDIzMzE2LFjYWZmBkNDQ3Tt2hXHjx9XccTyfH19MW3atGqPHVtbWyxZsgQTJ06EVCrFlClTlBbbDz/8AFdXV+jr68Pc3Bz9+/fH/fv3kZSUhG7dusHQ0BCmpqbo2bMnrl+/DgBITU1Fnz59YGxsDKlUii5duuDvv/8GAMTFxcHU1BS7d++WHUeDBg3CjRs3lLZPTwQGBmL//v2Ijo6GRCKBRCJBeno6zp07h2HDhkEqlcLY2Bgvv/wyrly5ovT4FFHT+/f69euYNWuWbN/URU3v1yNHjsDd3R16enro2rUrdu/erZbdjAIDAzF9+nRkZGRAIpHA1tYWADBy5Ei55+rq6e5E6nhOV5REIsHu3bvlykxNTREXF6eSeJ7m6+uL6dOnY+bMmWjWrBlatmyJTZs2ySYKNjY2hr29PX799VfZOgkJCbL/RZ8+fbBlyxa16IZc3edAYGAg/P39sWjRIlhYWEAqlWLq1KkoKSlRabxP2NraVroa7e7ujo8++ggAEBkZCVdXVxgaGsLKygrvvvsuioqKlB+okij9isXGjRsRFxeHCRMmKHvTz40tW7YgODgYJ06cwN9//40pU6bA2toakydPxsSJE3H06FGsXbsWnTt3xrVr13Dnzh1Vh1xtzDt37sS8efPwzz//YOfOndDR0cHff/+NkJAQfPXVV+jRowfy8vJw8OBBVe8CwsPDsWnTJkRFRaFXr17IysrCxYsXUVRUBB8fH7Rt2xYJCQlo1aoVTp8+rZa3T67p2AEgm2wyIiJCaTFlZWVh7NixWLlyJUaOHInCwkIcPHgQgiDA398fkydPxrfffouSkhKcOHFC9uV13Lhx8PDwwIYNG6CpqYmUlBS5bhcPHjzA0qVLsXXrVujo6ODdd9/FmDFjcPjwYaXtGwBER0fj33//hYuLCxYvXgwAKC8vR+/eveHr64u//voLUqkUhw8fRllZmVJjU1RN79/OnTtjypQpsmNIXVT3fi0oKMDw4cMxZMgQbNu2DdevX1fbblzR0dFo3749vvjiC5w8eRKamppo0aIFYmNj8corr0BTU1PVISpEXc/pz4stW7Zg7ty5OHHiBLZv34533nkHu3btwsiRIzF//nxERUVhwoQJyMjIwH///YfRo0djxowZeOutt5CcnIzZs2erehdq/BwAHvd00dPTQ1JSEtLT0zFp0iSYm5vLfixQZxoaGli7di3atWuHq1ev4t1338XcuXPx2WefqTq0xiEomZmZmZCWlqbszT43fHx8hE6dOgkVFRWysrCwMKFTp07CpUuXBADCn3/+qcIIK6spZkEQhBkzZgg+Pj6yZfHx8YJUKhUKCgqUHWq1CgoKBF1dXWHTpk2Vln3++eeCsbGxkJubq4LIFFfb/8HGxkbw9/dXelynTp0SAAjp6ely5bm5uQIAISkpqcr1jI2Nhbi4uCqXxcbGCgCEY8eOycouXLggABCOHz/ecMEryMfHR5gxY4bseXh4uNCuXTuhpKRE6bGIpchxExUVpaLoqlbT+3XDhg2Cubm58PDhQ1nZpk2bBABCcnKyEqNUTFRUlGBjYyN7DkDYtWuXyuIR48lxr47n9No8/Z6t6jU3MTERYmNjlR7Xs3x8fIRevXrJnpeVlQmGhobChAkTZGVZWVkCAOHo0aNCWFiY4OLiItfGBx98IAAQ7t69q6ywK6nuc0AQBCEgIEAwMzMT7t+/LyvbsGGDYGRkJJSXlyszzCpVdQ7s3LmzEBERUWX9HTt2CObm5o0fmIoovSvUW2+9hW3btil7s8+V7t27y3U58Pb2xuXLl5GcnAxNTU34+PioMLqqVRdzeXl5pboDBgyAjY0N7OzsMGHCBHzzzTd48OCBMsOt5MKFCyguLq5yrpWUlBR4eHg0if7Etf0funbtqvSYOnfujH79+sHV1RWvvfYaNm3ahLt378LMzAyBgYEYNGgQhg8fjujoaGRlZcnWCw0NxVtvvYX+/ftjxYoVlboRaWlpwdPTU/bc0dERpqamuHDhgtL2rTopKSl4+eWXm8zAVjHvX3VQ0/v10qVLcHNzg56enqysW7duygzvhaOO5/TniZubm+xvTU1NmJubw9XVVVbWsmVLAEB2djYuXbokd14E1OP4r+5z4OnlBgYGsufe3t4oKipSSfdWsfbu3Yt+/fqhbdu2MDY2xoQJE5Cbm/vcvgeUnlg8evQIkZGR8PHxwfTp0xEaGir3oLp7+oOyKTM2Nsbp06fx7bffonXr1li4cCE6d+6s0v6f+vr6dVrW1BgaGip9m5qamvjzzz/x66+/wsnJCevWrUPHjh1x7do1xMbG4ujRo+jRowe2b9+ODh064NixYwCAjz76COfOncPQoUPx119/wcnJCbt27VJ6/HXxPB0z6oivr3pRx3O6GBKJRNYl54nS0lIVRVPZsz9QSCQSubInPwqoY/fcJ2r6HFB3Ghoa1R4f6enpGDZsGNzc3BAfH49Tp07hf//7HwCozRiRhqb0xOLMmTNwd3eHhoYG/vnnHyQnJ8se6jZwTl09Oyj42LFjcHBwQOfOnVFRUYH9+/erKLLqVRdzdX2EtbS00L9/f6xcuRJnzpxBeno6/vrrL2WEWiUHBwfo6+sjMTGx0jI3NzekpKQgLy9PBZGJI/b/oCwSiQQ9e/bEokWLkJycDB0dHVmS4OHhgfDwcBw5cgQuLi5yVzw7dOiAWbNm4Y8//sCrr76K2NhY2bKysjLZYG7g8S/V9+7dQ6dOnZS3Y/+fjo6O3K/7bm5uOHjwoFp9OalJTcfNs/umDmp6v3bs2BFnz55FcXGxrOzkyZPKDK9etLW11e71VoS6ndPFsLCwkLtaevny5Sb7a3PHjh3lzouA+hz/NX0OpKam4uHDh7K6x44dg5GRkWyiZVV69vgoKCiQJUSnTp1CRUUFVq9eje7du6NDhw64deuWqkJVCqUP3t63b5+yN/ncycjIQGhoKN5++22cPn0a69atw+rVq2Fra4uAgAAEBQXJBm9fv34d2dnZeP3119Uy5qr8/PPPuHr1Knr37o1mzZrhl19+QUVFBTp27KjkqP+Pnp4ewsLCMHfuXOjo6KBnz57IycnBuXPnMGHCBCxbtgz+/v5Yvnw5WrdujeTkZLRp0wbe3t4qi7kqYv4PynL8+HEkJiZi4MCBaNGiBY4fP46cnBzo6+sjPDwcfn5+aNOmDS5duoTLly9j4sSJePjwIebMmYPRo0ejXbt2yMzMxMmTJzFq1ChZu9ra2pg+fTrWrl0LLS0tTJs2Dd27d1fJZX9bW1scP34c6enpMDIywrRp07Bu3TqMGTMG4eHhMDExwbFjx9CtWzeVHufVqem4sbW1xYEDBzBmzBjo6uqiefPmKo625vfrm2++iQ8++ABTpkzBvHnzkJGRgVWrVgGAWt3Vqjq2trZITExEz549oauri2bNmqk6pFqp4zldjL59+2L9+vXw9vZGeXk5wsLCmkw3xme9/fbbiIyMRFhYGIKDg5GSkiK7u5Uqj//qPgc6deqEM2fOoKSkBMHBwfjwww+Rnp6OiIgITJs2DRoaSv99vJK+ffsiLi4Ow4cPh6mpKRYuXCj7sc7e3h6lpaVYt24dhg8fjsOHD2Pjxo0qjriRqXqQB4nj4+MjvPvuu8LUqVMFqVQqNGvWTJg/f75sYOXDhw+FWbNmCa1btxZ0dHQEe3t7ISYmRq1jfnbw9sGDBwUfHx+hWbNmgr6+vuDm5iZs375dRdH/n/LycuHjjz8WbGxsBG1tbcHa2lpYtmyZIAiCkJ6eLowaNUqQSqWCgYGB0LVrV5UMEq5Jbf8HVQ3CPX/+vDBo0CDBwsJC0NXVFTp06CCsW7dOuH37tuDv7y87lm1sbISFCxcK5eXlQnFxsTBmzBjByspK0NHREdq0aSNMmzZNNiA3NjZWMDExEeLj4wU7OztBV1dX6N+/v3D9+nWl758gCMKlS5eE7t27C/r6+gIA4dq1a0JqaqowcOBAwcDAQDA2NhZefvll4cqVKyqJrya1HTdHjx4V3NzcBF1dXUGdPlJqer8ePnxYcHNzE3R0dIQuXboI27ZtEwAIFy9eVHHUlT07eDshIUGwt7cXtLS05MrV0ZMB0Op6Tq/J04O3b968KQwcOFAwNDQUHBwchF9++UWtBm8/fWMIQaj6XI6nBqD/+OOPgr29vaCrqyv4+voKGzZsEADI3dBA2ar7HBCEx4O3R4wYISxcuFAwNzcXjIyMhMmTJwuPHj1SWbxPy8/PF9544w1BKpUKVlZWQlxcnNzg7cjISKF169aCvr6+MGjQIGHr1q0qHyzfmCSC8EzHMCJ6Lj3Psz8/Ky4uDjNnzmwyfbhJtb755htMmjQJ+fn5HJ9BL5ylS5di48aNajsQOjAwEPfu3as0lwipJ5XMvE1ERKQqW7duhZ2dHdq2bYvU1FSEhYXh9ddfZ1JBL4TPPvsMnp6eMDc3x+HDh/Hpp59i2rRpqg6LnhNMLIiI6IVy+/ZtLFy4ELdv30br1q3x2muvNYmJtogawuXLl/Hxxx8jLy8P1tbWeP/99xEeHq7qsOg5wa5QRERERERUb6ofTk9ERERERE0eEwsiIiIiIqo3JhZERERERFRvTCyIiIiIiKjemFgQEREREVG9MbEgIiIiIqJ6Y2JB1ATdvn0bM2bMgL29PfT09NCyZUv07NkTGzZswIMHD+TqLl++HJqamvj0008rtRMXFweJRAKJRAINDQ1YWlpi0qRJyM7OltV5slwikUBLSwvW1tYIDQ1FcXGxrE5OTg7eeecdWFtbQ1dXF61atcKgQYNw+PDhavchPT0dwcHBaNeuHfT19dG+fXtERESgpKRErs7T23/yOHbsWH1ePiKiBhcYGAiJRIIVK1bIle/evRsSiQQAkJSUJHcua9myJUaNGoWrV6/K6tva2sqWa2pqok2bNggODsbdu3dr3P7T53NNTU00a9YMXl5eWLx4MfLz8xt+h4mqwMSCqIm5evUqPDw88Mcff2DZsmVITk7G0aNHMXfuXPz888/Yu3evXP2YmBjMnTsXMTExVbYnlUqRlZWFzMxMbNq0Cb/++ismTJggVyc2NhZZWVm4du0aPvvsM3z11Vf4+OOPZctHjRqF5ORkbNmyBf/++y8SEhLg6+uL3Nzcavfj4sWLqKiowOeff45z584hKioKGzduxPz58yvV3bt3L7KysmSPLl26iHnJiIiUQk9PD5988kmtScClS5dw69Yt7NixA+fOncPw4cNRXl4uW7548WJkZWUhIyMD33zzDQ4cOICQkJBat//0+fzIkSOYMmUKtm7dCnd3d9y6dave+0dUK4GImpRBgwYJlpaWQlFRUZXLKyoqZH8nJSUJbdu2FUpKSoQ2bdoIhw8flqsbGxsrmJiYyJUtXbpU0NDQEB48eCAIgiAAEHbt2iVXJzg4WBgyZIggCIJw9+5dAYCQlJRUzz0ThJUrVwrt2rWTPb927ZoAQEhOTq5320REjSkgIEAYNmyY4OjoKMyZM0dWvmvXLuHJ1619+/YJAIS7d+/Kln/zzTcCAOHixYuCIAiCjY2NEBUVJdf2kiVLBCcnpxq3X9X5XBAE4b///hOaN28ujBs3rm47RiQCr1gQNSG5ubn4448/8N5778HQ0LDKOk8uuQPA5s2bMXbsWGhra2Ps2LHYvHlzrdvQ19dHRUUFysrKqlz+77//4q+//oKXlxcAwMjICEZGRti9e7dc96i6yM/Ph5mZWaVyPz8/tGjRAr169UJCQkK9tkFE1Fg0NTWxbNkyrFu3DpmZmQqto6+vDwBy3UCfdvPmTfz000+yc65YLVq0wLhx45CQkCB3VYSoMTCxIGpC0tLSIAgCOnbsKFfevHlz2Rf8sLAwAEBBQQF++OEHjB8/HgAwfvx4fP/99ygqKqq2/cuXL2Pjxo3o2rUrjI2NZeVjx46FkZER9PT00LFjRzg7OyM8PBwAoKWlhbi4OGzZsgWmpqbo2bMn5s+fjzNnzojet3Xr1uHtt9+WlRkZGWH16tXYsWMH9uzZg169esHf35/JBRGprZEjR8Ld3R0RERG11s3KysKqVavQtm1bufN6WFgYjIyMoK+vD0tLS0gkEkRGRtY5JkdHRxQWFtbYPZWoITCxIHoOnDhxAikpKXB2dpZdNfj222/Rvn17dO7cGQDg7u4OGxsbbN++XW7d/Px8GBkZwcDAAB07dkTLli3xzTffyNWJiopCSkoKUlNT8fPPP+Pff/+VG4cxatQo3Lp1CwkJCXjllVeQlJSEl156CXFxcQCAqVOnyhIfIyOjSvHfvHkTr7zyCl577TVMnjxZVt68eXOEhobCy8sLnp6eWLFiBcaPH1/lQHQiInXxySefYMuWLbhw4UKVyy0tLWFoaIg2bdrg/v37iI+Ph46Ojmz5nDlzkJKSgjNnziAxMREAMHToUNkVh6fPp1OnTq01HkEQAMhf0SZqDFqqDoCIFGdvbw+JRIJLly7JldvZ2QH4v0vqwONuUOfOnYOW1v+9zSsqKhATE4Pg4GBZmbGxMU6fPg0NDQ20bt1aro0nWrVqBXt7ewBAx44dUVhYiLFjx+Ljjz+Wlevp6WHAgAEYMGAAFixYgLfeegsREREIDAzE4sWLMXv27Cr36datW+jTpw969OiBL774otbXwMvLC3/++Wet9YiIVKV3794YNGgQwsPDERgYWGn5wYMHIZVK0aJFC7mrw080b95cdm51cHDAmjVr4O3tjX379qF///5ISUmR1ZVKpbXGc+HCBUilUpibm9d5n4gUwcSCqAkxNzfHgAEDsH79ekyfPr3acRZnz57F33//jaSkJLkxC3l5efD19cXFixfh6OgIANDQ0JB9gClKU1MTAPDw4cNq6zg5OWH37t0AHvfxbdGiRaU6N2/eRJ8+fdClSxfExsZCQ6P2i6gpKSlo3bq1qHiJiJRtxYoVcHd3r9R1FQDatWsHU1NThdt69pwr5pydnZ2Nbdu2wd/fX6FzLFF9MLEgamI+++wz9OzZE127dsVHH30ENzc3aGho4OTJk7h48SK6dOmCzZs3o1u3bujdu3el9T09PbF582ZR3Ynu3buH27dvo6KiApcvX8bixYvRoUMHdOrUCbm5uXjttdcQFBQENzc3GBsb4++//8bKlSsxYsSIatu8efMmfH19YWNjg1WrViEnJ0e2rFWrVgCALVu2QEdHBx4eHgCAnTt3IiYmBl9++aXCsRMRqYKrqyvGjRuHtWvXil63sLAQt2/fhiAIuHHjBubOnQsLCwv06NGjxvUEQZCtd+/ePRw9ehTLli2DiYlJpfk1iBoDEwuiJqZ9+/ZITk7GsmXLEB4ejszMTOjq6sLJyQmzZ8/GlClTYGdnJxvE/axRo0Zh9erVWLZsmcLbnDRpEoDH/XNbtWqF3r17Y9myZdDS0oKRkRG8vLwQFRWFK1euoLS0FFZWVpg8eXKVc1I88eeffyItLQ1paWmwtLSUW/akPzAALFmyBNevX4eWlhYcHR2xfft2jB49WuHYiYhUZfHixZXGtSli4cKFWLhwIQDAwsICnp6e+OOPP2rtylRQUIDWrVtDIpFAKpWiY8eOCAgIwIwZMxTqMkVUXxLh6U9wIiIiIiKiOmBnOyIiIiIiqjcmFkREREREVG9MLIiIiIiIqN6YWBARERERUb0xsSAiIiIionpjYkFERERERPXGxIKIiIiIiOqNiQUREREREdUbEwsiIiIiIqo3JhZERERERFRvTCyIiIiIiKjemFgQEREREVG9/T9kJ08gfyKUDAAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -884,31 +815,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 73,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
+      "image/png": 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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -986,31 +913,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 86,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1098,31 +1021,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 85,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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1EDkRERHRG0WtyeEAUK1aNQQHB2shChEREZF+4xARERERkUQsnIiIiIgkYuFEREREJBELJyIiIiKJXrlwKiwsRHJyMoqKijSZh4iIiEhvqX1V3cOHDzFixAisWbMGAHDx4kXUrl0bI0aMQI0aNTBx4kSNh3zTdY/rUi79bA3+o1z6ISIiel5l+jun9ohTREQETp8+jYSEBJiamiq3BwYGYtOmTRoNV1xcjMmTJ8Pd3R1mZmaoU6cOZsyYASGEch8hBKZMmQInJyeYmZkhMDAQKSkpGs1BREREBLxC4RQXF4elS5eibdu2kMlkyu2NGjXC5cuXNRpuzpw5WLFiBZYuXYrz589jzpw5iIyMxJIlS5T7REZGYvHixVi5ciWOHj0KCwsLBAUF4fHjxxrNQkRERKT2qbo7d+7A3t6+xPb8/HyVQkoTDh06hB49eqBLl6dDgLVq1cLGjRtx7NgxAE9HmxYuXIivvvoKPXr0AACsXbsWDg4OiIuLQ58+fTSah4iIiCo3tUecmjdvjj/++L9zjM+KpR9//BGtW7fWXDIAbdq0QXx8PC5evAgAOH36NP766y906tQJAHD16lVkZGQgMDBQ+Rhra2u8/fbbOHz4cJnHLSgoQG5ursqNiIiI6GXUHnGaNWsWOnXqhHPnzqGoqAiLFi3CuXPncOjQIezbt0+j4SZOnIjc3Fw0aNAAhoaGKC4uxsyZM9GvXz8AQEZGBgCU+FJhBwcHZVtpZs+ejWnTpmk0KxEREVV8ao84tW3bFqdOnUJRURE8PT2xa9cu2Nvb4/Dhw2jWrJlGw23evBnr16/Hhg0bcPLkSaxZswbz5s1TXtH3qiIiIpCTk6O8Xb9+XUOJiYiIqCJTe8QJAOrUqYNVq1ZpOksJ48aNw8SJE5VzlTw9PXHt2jXMnj0boaGhcHR0BABkZmbCyclJ+bjMzEx4e3uXeVwTExOYmJhoNTsRERFVPGqPOBkaGuL27dsltt+7dw+GhoYaCfXMw4cPYWCgGtHQ0BAKhQIA4O7uDkdHR8THxyvbc3NzcfToUY3PtyIiIiJSe8Tp+TWUnldQUABjY+PXDvS8bt26YebMmXB1dUWjRo2QlJSE+fPnY9CgQQCeTkwfPXo0vvnmG3h4eMDd3R2TJ0+Gs7MzgoODNZqFKp7KtGAbERFphuTCafHixQCeFis//vgjLC0tlW3FxcXYv38/GjRooNFwS5YsweTJk/HZZ5/h9u3bcHZ2xieffIIpU6Yo9xk/fjzy8/MxdOhQZGdno23bttixY4fK4pxE+owFHBHRm0Ny4bRgwQIAT0ecVq5cqXJaztjYGLVq1cLKlSs1Gs7KygoLFy7EwoULy9xHJpNh+vTpmD59ukb7JiIiIvo3yYXT1atXAQABAQGIjY1F1apVtRaKiIiISB+pPcdp79692shBREREpPdeaTmCGzduYOvWrUhLS0NhYaFK2/z58zUSjKiyuJ00snw6Cn5xM+daERG9nNqFU3x8PLp3747atWvjwoULaNy4MVJTUyGEQNOmTbWRkYiIiEgvqF04RURE4IsvvsC0adNgZWWFX3/9Ffb29ujXrx86duyojYxEWqEvIz1ERPTmULtwOn/+PDZu3Pj0wUZGePToESwtLTF9+nT06NEDn376qcZDvun4B5qIiKhiUHvlcAsLC+W8JicnJ1y+fFnZdvfuXc0lIyIiItIzao84tWrVCn/99RcaNmyIzp074/PPP8eZM2cQGxuLVq1aaSMjERERkV5Qu3CaP38+8vLyAADTpk1DXl4eNm3aBA8PD15RR0RERBWa2oVT7dq1lf+2sLDQ+GrhRERERPpK7TlOZYmNjYWXl5emDkdERESkd9QqnL7//nu8//77+Oijj3D06FEAwJ49e+Dj44MBAwbA19dXKyGJiIiI9IHkwunbb7/FiBEjkJqaiq1bt6JDhw6YNWsW+vXrhw8//BA3btzAihUrtJmViIiISKckz3GKjo7GqlWrEBoaigMHDsDPzw+HDh3CpUuXYGFhoc2MRERERHpB8ohTWloaOnToAABo164dqlSpgmnTprFoIiIiokpDcuFUUFAAU1NT5X1jY2PY2tpqJRQRERGRPlJrOYLJkyfD3NwcAFBYWIhvvvkG1tbWKvtwLSciIiKqqCQXTu3bt0dycrLyfps2bXDlyhWVfWQymeaSEREREekZyYVTQkKCFmMQERER6T+1Vw4nel3d47qUSz9bg/8ol36IiKjy0NjK4UREREQVHUeciIjopThSTPQUR5yIiIiIJFK7cEpLS4MQosR2IQTS0tI0EoqIiIhIH6l9qs7d3R23bt2Cvb29yvasrCy4u7ujuLhYY+GIiIiedztpZPl0FFw+3dCbR+0RJyFEqes15eXlqawsTkRERFTRSB5xGjt2LICni1w+v4I4ABQXF+Po0aPw9vbWeEAiIiIifSG5cEpKSgLwdMTpzJkzMDY2VrYZGxujSZMm+OKLLzSfkIiIiEhPSC6c9u7dCwAICwvDokWLIJfLtRaKiIiISB+pPTk8OjpaGzmoHHAdFiIiotcjqXDq1asXYmJiIJfL0atXrxfuGxsbq5FgRERERPpGUuFkbW2tvJLO2tpaq4GIiIiI9JWkwun503M8VUdERESVld5/5Up6ejr69+8POzs7mJmZwdPTE4mJicp2IQSmTJkCJycnmJmZITAwECkpKTpMTERERBWV5MnhPj4+pS58+W8nT558rUDPu3//Pnx9fREQEIA///wT1atXR0pKCqpWrarcJzIyEosXL8aaNWvg7u6OyZMnIygoCOfOneOCnERERKRRkgun4OBg5b+FEJg9ezaGDRsGW1tbbeQCAMyZMwcuLi4qpwfd3d1VcixcuBBfffUVevToAQBYu3YtHBwcEBcXhz59+mgtGxEREVU+kgunqVOnqtz/7rvvMGrUKNSuXVvjoZ7ZunUrgoKC0Lt3b+zbtw81atTAZ599hiFDhgAArl69ioyMDAQGBiofY21tjbfffhuHDx9m4UREREQapddznK5cuYIVK1bAw8MDO3fuxKeffoqRI0dizZo1AICMjAwAgIODg8rjHBwclG2lKSgoQG5ursqNiIiI6GXUXgCzPCkUCjRv3hyzZs0C8HSe1dmzZ7Fy5UqEhoa+8nFnz56NadOmaSomERERVRJ6PeLk5OSEt956S2Vbw4YNkZaWBgBwdHQEAGRmZqrsk5mZqWwrTUREBHJycpS369evazg5ERERVUSSR5wWL16scr+oqAgxMTGoVq2ayvaRI0dqJhkAX19fJCcnq2y7ePEi3NzcADydKO7o6Ij4+Hh4e3sDAHJzc3H06FF8+umnZR7XxMQEJiYmGstJRERElYPkwmnBggUq9x0dHbFu3TqVbTKZTKOF05gxY9CmTRvMmjULH3zwAY4dO4YffvgBP/zwg7K/0aNH45tvvoGHh4dyOQJnZ2eVqwCJiIgqottJmvub+0LB5dPNm0By4XT16lVt5ihVixYtsGXLFkRERGD69Olwd3fHwoUL0a9fP+U+48ePR35+PoYOHYrs7Gy0bdsWO3bs4BpOREREpHF6PTkcALp27YquXbuW2S6TyTB9+nRMnz69HFMRERFRZaTXk8OJiIiI9AkLJyIiIiKJWDgRERERScTCiYiIiEgiFk5EREREErFwIiIiIpJI75cjICIiIv1WmRbi5IgTERERkUSvXDhdunQJO3fuxKNHjwAAQgiNhSIiIiLSR2oXTvfu3UNgYCDq1auHzp0749atWwCAwYMH4/PPP9d4QCIiIiJ9oXbhNGbMGBgZGSEtLQ3m5ubK7R9++CF27Nih0XBERERE+kTtyeG7du3Czp07UbNmTZXtHh4euHbtmsaCEREREekbtUec8vPzVUaansnKyoKJiYlGQhERERHpI7ULp3bt2mHt2rXK+zKZDAqFApGRkQgICNBoOCIiIiJ9ovapusjISLzzzjtITExEYWEhxo8fj3/++QdZWVk4ePCgNjISERER6QW1R5waN26Mixcvom3btujRowfy8/PRq1cvJCUloU6dOtrISERERKQXXmnlcGtra3z55ZeazkJERESk116pcMrOzsaxY8dw+/ZtKBQKlbaPP/5YI8GIiIiI9I3ahdPvv/+Ofv36IS8vD3K5HDKZTNkmk8lYOBERVUCV6bvIiF5E7cLp888/x6BBgzBr1qxSlyUg/cUPPiIiotej9uTw9PR0jBw5kkUTERERVTpqjzgFBQUhMTERtWvX1kYeqgQ48kVERG8qSYXT1q1blf/u0qULxo0bh3PnzsHT0xNVqlRR2bd79+6aTUhElQoLayLSZ5IKp+Dg4BLbpk+fXmKbTCZDcXHxa4ciIiIi0keSCqd/LzlAREREVBmpPTmciIiIqLJSu3AaOXIkFi9eXGL70qVLMXr0aE1kIiIiItJLahdOv/76K3x9fUtsb9OmDX755ReNhCIiIiLSR2oXTvfu3YO1tXWJ7XK5HHfv3tVIKCIiIiJ9pHbhVLduXezYsaPE9j///JNrOxEREVGFpvYCmGPHjkV4eDju3LmDDh06AADi4+Px3XffYeHChZrOR0RERKQ31C6cBg0ahIKCAsycORMzZswAANSqVQsrVqzgF/wSERFRhaZ24QQAn376KT799FPcuXMHZmZmsLS01HQuIiIC0D2uS7n0szX4j3Lph+hN90qF0zPVq1fXVA4iIiIivfdKC2D+8ssv+OCDD9CqVSs0bdpU5aZN3377LWQymcp6UY8fP8bw4cNhZ2cHS0tLhISEIDMzU6s5iIiIqHJSe8Rp8eLF+PLLLzFw4ED89ttvCAsLw+XLl3H8+HEMHz5cGxkBAMePH8f3338PLy8vle1jxozBH3/8gZ9//hnW1tYIDw9Hr169cPDgQa1lISKiyo2nUCsvtUecli9fjh9++AFLliyBsbExxo8fj927d2PkyJHIycnRRkbk5eWhX79+WLVqFapWrarcnpOTg6ioKMyfPx8dOnRAs2bNEB0djUOHDuHIkSNayUJERESVl9qFU1paGtq0aQMAMDMzw4MHDwAAAwYMwMaNGzWb7v8bPnw4unTpgsDAQJXtJ06cwJMnT1S2N2jQAK6urjh8+LBWshAREVHlpfapOkdHR2RlZcHNzQ2urq44cuQImjRpgqtXr0IIofGA//3vf3Hy5EkcP368RFtGRgaMjY1hY2Ojst3BwQEZGRllHrOgoAAFBQXK+7m5uRrLS0RERBWX2iNOHTp0wNatWwEAYWFhGDNmDN599118+OGH6Nmzp0bDXb9+HaNGjcL69ethamqqsePOnj0b1tbWypuLi4vGjk1EREQVl9ojTj/88AMUCgUAKK9mO3ToELp3745PPvlEo+FOnDiB27dvq1ytV1xcjP3792Pp0qXYuXMnCgsLkZ2drTLqlJmZCUdHxzKPGxERgbFjxyrv5+bmsngiIiKil1K7cDIwMICBwf8NVPXp0wd9+vTRaKhn3nnnHZw5c0ZlW1hYGBo0aIAJEybAxcUFVapUQXx8PEJCQgAAycnJSEtLQ+vWrcs8romJCUxMTLSSmYiIiCouyafqUlJS0Ldv31LnA+Xk5OCjjz7ClStXNBrOysoKjRs3VrlZWFjAzs4OjRs3hrW1NQYPHoyxY8di7969OHHiBMLCwtC6dWu0atVKo1mIiIiIJBdOc+fOhYuLC+RyeYm2Z/OE5s6dq9FwUixYsABdu3ZFSEgI2rdvD0dHR8TGxpZ7DiIiIqr4JJ+q27dvH3766acy2z/44AN89NFHGgn1IgkJCSr3TU1NsWzZMixbtkzrfRMREVHlJnnEKS0tDfb29mW2V6tWDdevX9dIKCIiIiJ9JLlwsra2xuXLl8tsv3TpUqmn8YiIiIgqCsmFU/v27bFkyZIy2xcvXox27dppJBQRERGRPpJcOEVERODPP//E+++/j2PHjiEnJwc5OTk4evQoQkJCsHPnTkRERGgzKxEREZFOSZ4c7uPjg19++QWDBg3Cli1bVNrs7OywefNmlYUqiYiIiCoatRbA7Nq1K65du4YdO3bg0qVLEEKgXr16eO+992Bubq6tjERERER6Qe2Vw83MzDT+nXREREREbwK1v+SXiIiIqLJi4UREREQkEQsnIiIiIonUnuNERBXT7aSR5dNRcPl0Q0SkDRxxIiIiIpJI8oiToaGhpP2Ki4tfOQwRERGRPpNcOAkh4ObmhtDQUPj4+GgzExEREZFeklw4HTt2DFFRUVi0aBHc3d0xaNAg9OvXD1WrVtVmPiIiIiK9IXmOU/PmzbFixQrcunULY8eOxZYtW1CzZk306dMHu3fv1mZGIiIiIr2g9uRwU1NT9O/fH/Hx8Th79ixu376Njh07IisrSxv5iIiIiPTGKy1HcOPGDcTExCAmJgYPHz7EuHHjIJfLNZ2NiIiISK9ILpwKCwuxZcsWREVF4cCBA+jUqRMWLlyITp06Sb7ijoiIiOhNJrlwcnJygpWVFUJDQ7F8+XLY29sDAPLz81X248gTERERVVSSC6f79+/j/v37mDFjBr755psS7UIIyGQyruNEREREFZbkwmnv3r3azEFERESk9yQXTn5+fi/dh1fWERERUUWmke+q27VrFz744APUqFFDE4cjIiIi0kuvXDhdu3YNU6dORa1atdC7d28YGBhg7dq1msxGREREpFfUWsepsLAQsbGx+PHHH3Hw4EEEBgbixo0bSEpKgqenp7YyEhEREekFySNOI0aMgLOzMxYtWoSePXvixo0b+P333yGTybiOExEREVUKkkecVqxYgQkTJmDixImwsrLSZiYiIiK9djtpZPl0FFw+3ZB0kkec1q1bh2PHjsHJyQkffvghtm3bxjWbiIiIqFKRXDj17dsXu3fvxpkzZ9CgQQMMHz4cjo6OUCgUOHfunDYzEhEREekFta+qc3d3x7Rp05CamoqffvoJISEh6N+/P2rWrImRI8tp6JKIiIhIB9S6qu55MpkMQUFBCAoKQlZWFtauXYuYmBgNRiMiIiLSLxpZANPW1hbt2rVDzZo1NXE4IiIiIr2kVuG0c+dOfPHFF5g0aRKuXLkCALhw4QKCg4PRsmVLKBQKrYQkIiIi0geSC6eoqCh06tQJMTExmDNnDlq1aoWffvoJrVu3hqOjI86ePYvt27drNNzs2bPRokULWFlZwd7eHsHBwUhOTlbZ5/Hjxxg+fDjs7OxgaWmJkJAQZGZmajQHEREREaBG4bRo0SLMmTMHd+/exebNm3H37l0sX74cZ86cwcqVK9GwYUONh9u3bx+GDx+OI0eOYPfu3Xjy5Anee+895OfnK/cZM2YMfv/9d/z888/Yt28fbt68iV69emk8CxEREZHkyeGXL19G7969AQC9evWCkZER5s6dq9V5TTt27FC5HxMTA3t7e5w4cQLt27dHTk4OoqKisGHDBnTo0AEAEB0djYYNG+LIkSNo1aqV1rIRERFR5SN5xOnRo0cwNzcH8PSKOhMTEzg5OWktWGlycnIAPJ2MDgAnTpzAkydPEBgYqNynQYMGcHV1xeHDh8s1GxEREVV8ai1H8OOPP8LS0hIAUFRUhJiYGFSrVk1lH22t5aRQKDB69Gj4+vqicePGAICMjAwYGxvDxsZGZV8HBwdkZGSUeayCggIUFBQo7+fm5molMxEREVUskgsnV1dXrFq1Snnf0dER69atU9lHJpNprXAaPnw4zp49i7/++uu1jzV79mxMmzZNA6mIiIioMpFcOKWmpmoxxouFh4dj27Zt2L9/v8qcKkdHRxQWFiI7O1tl1CkzMxOOjo5lHi8iIgJjx45V3s/NzYWLi4tWshMREVHFoZEFMLVFCIHw8HBs2bIFe/bsgbu7u0p7s2bNUKVKFcTHxyu3JScnIy0tDa1bty7zuCYmJpDL5So3IiIiopdRa46TQqFATEwMYmNjkZqaCplMBnd3d7z//vsYMGAAZDKZRsMNHz4cGzZswG+//QYrKyvlvCVra2uYmZnB2toagwcPxtixY2Frawu5XI4RI0agdevWvKKOiF5L97gu5dLP1uA/yqUfItIMySNOQgh0794d//nPf5Ceng5PT080atQI165dw8CBA9GzZ0+Nh1uxYgVycnLg7+8PJycn5W3Tpk3KfRYsWICuXbsiJCQE7du3h6OjI2JjYzWehYiIiEjyiFNMTAz279+P+Ph4BAQEqLTt2bMHwcHBWLt2LT7++GONhRNCvHQfU1NTLFu2DMuWLdNYv0RERESlkVw4bdy4EZMmTSpRNAFAhw4dMHHiRKxfv16jhRMRka7cTtLOFcIlBJdPN0SkGZJP1f3999/o2LFjme2dOnXC6dOnNRKKiIiISB9JLpyysrLg4OBQZruDgwPu37+vkVBERERE+khy4VRcXAwjo7LP7BkaGqKoqEgjoYiIiIj0keQ5TkIIDBw4ECYmJqW2P/8VJkREREQVkeTCKTQ09KX7cGI4ERERVWSSC6fo6Ght5iAiIiLSe3r9lStERERE+oSFExEREZFELJyIiIiIJGLhRERERCQRCyciIiIiiVg4EREREUnEwomIiIhIIhZORERERBKxcCIiIiKSiIUTERERkUQsnIiIiIgkYuFEREREJBELJyIiIiKJWDgRERERScTCiYiIiEgiFk5EREREErFwIiIiIpKIhRMRERGRRCyciIiIiCRi4UREREQkEQsnIiIiIolYOBERERFJxMKJiIiISCIWTkREREQSsXAiIiIikoiFExEREZFELJyIiIiIJKowhdOyZctQq1YtmJqa4u2338axY8d0HYmIiIgqmApROG3atAljx47F1KlTcfLkSTRp0gRBQUG4ffu2rqMRERFRBVIhCqf58+djyJAhCAsLw1tvvYWVK1fC3Nwcq1ev1nU0IiIiqkCMdB3gdRUWFuLEiROIiIhQbjMwMEBgYCAOHz5c6mMKCgpQUFCgvJ+TkwMAyM3N1UrGooJ8rRz3316WnzmYQ59y5Ofnq2wrLi7WSY6y6EuOJw+f6EUOfXk9mIM5tHlcIcTLdxZvuPT0dAFAHDp0SGX7uHHjRMuWLUt9zNSpUwUA3njjjTfeeOONN+Xt+vXrL6073vgRp1cRERGBsWPHKu8rFApkZWXBzs4OMplMh8meys3NhYuLC65fvw65XM4czMEczMEczMEcWiSEwIMHD+Ds7PzSfd/4wqlatWowNDREZmamyvbMzEw4OjqW+hgTExOYmJiobLOxsdFWxFcml8v14g3FHMzBHMzBHMxRkXMAgLW1taT93vjJ4cbGxmjWrBni4+OV2xQKBeLj49G6dWsdJiMiIqKK5o0fcQKAsWPHIjQ0FM2bN0fLli2xcOFC5OfnIywsTNfRiIiIqAKpEIXThx9+iDt37mDKlCnIyMiAt7c3duzYAQcHB11HeyUmJiaYOnVqidOJzMEczMEczMEczKFbMiGkXHtHRERERG/8HCciIiKi8sLCiYiIiEgiFk5EREREErFw0iF/f3+MHj1ab/t/+PAhQkJCIJfLIZPJkJ2dXW7ZiEqj698ZfSWEwNChQ2FrawuZTIZTp07pJIc+/Hz0IQNVbCycqExr1qzBgQMHcOjQIdy6dUvy4mBUMb0Jf5Bq1aqFhQsX6jpGuduxYwdiYmKwbds23Lp1Cz4+PoiLiyv3HLGxsZgxY0a590tUnirEcgSkHZcvX0bDhg3RuHFjXUfRe4WFhTA2NtZ1DKqkLl++DCcnJ7Rp00anOWxtbXXaP1F54IiTjhUVFSE8PBzW1taoVq0aJk+erPx25oKCAkyYMAEuLi4wMTFB3bp1ERUVVS79+/v747vvvsP+/fshk8ng7+8PAFi+fDk8PDxgamoKBwcHvP/++xrNo1AoEBkZibp168LExASurq6YOXMmAODGjRvo27cvbG1tYWFhgebNm+Po0aMa7f8Zf39/hIeHl/mzqVWrFmbMmIGPP/4YcrkcQ4cOfaV+fvnlF3h6esLMzAx2dnYIDAxEfn4+EhIS0LJlS1hYWMDGxga+vr64du0aAOD06dMICAiAlZUV5HI5mjVrhsTERABATEwMbGxsEBcXp/w5BQUF4fr166/1egwcOBD79u3DokWLIJPJIJPJkJqain/++Qddu3aFXC6HlZUV2rVrh8uXL79WXy/zovfstWvXMGbMGGVGbXjRe/TQoUPw9vaGqakpmjdvjri4OK2fOhs4cCBGjBiBtLQ0yGQy1KpVCwDQs2dPlfvl4flRSW1/Vkghk8lKjLzZ2NggJiZGo/34+/tjxIgRGD16NKpWrQoHBwesWrVKuRCzlZUV6tatiz///FP5mK1btypfn4CAAKxZs0bjUyLK+nwZOHAggoODMW3aNFSvXh1yuRzDhg1DYWGhxvoGSh8B9vb2xtdffw0AmD9/Pjw9PWFhYQEXFxd89tlnyMvL02gGbeCIk46tWbMGgwcPxrFjx5CYmIihQ4fC1dUVQ4YMwccff4zDhw9j8eLFaNKkCa5evYq7d++WS/+xsbGYOHEizp49i9jYWBgbGyMxMREjR47EunXr0KZNG2RlZeHAgQMazRMREYFVq1ZhwYIFaNu2LW7duoULFy4gLy8Pfn5+qFGjBrZu3QpHR0ecPHkSCoVCo/0/70U/GwCYN28epkyZgqlTp77S8W/duoW+ffsiMjISPXv2xIMHD3DgwAEIIRAcHIwhQ4Zg48aNKCwsxLFjx5SFQL9+/eDj44MVK1bA0NAQp06dQpUqVZTHffjwIWbOnIm1a9fC2NgYn332Gfr06YODBw++8muxaNEiXLx4EY0bN8b06dMBAMXFxWjfvj38/f2xZ88eyOVyHDx4EEVFRa/cjxQves82adIEQ4cOVf6MtKGs92hubi66deuGzp07Y8OGDbh27Vq5nNpctGgR6tSpgx9++AHHjx+HoaEh7O3tER0djY4dO8LQ0FDrGf6tPD4r9M2aNWswfvx4HDt2DJs2bcKnn36KLVu2oGfPnpg0aRIWLFiAAQMGIC0tDZmZmXj//fcxatQo/Oc//0FSUhK++OILjeZ50ecLAMTHx8PU1BQJCQlITU1FWFgY7OzslP8JKA8GBgZYvHgx3N3dceXKFXz22WcYP348li9fXm4ZXokgnfHz8xMNGzYUCoVCuW3ChAmiYcOGIjk5WQAQu3fv1kn/QggxatQo4efnp2z79ddfhVwuF7m5uVrJk5ubK0xMTMSqVatKtH3//ffCyspK3Lt3Tyt9/9vLXhs3NzcRHBz8Wn2cOHFCABCpqakq2+/duycAiISEhFIfZ2VlJWJiYkpti46OFgDEkSNHlNvOnz8vAIijR4++Vl4/Pz8xatQo5f2IiAjh7u4uCgsLX+u46mZ42c9lwYIFWuv/Re/RFStWCDs7O/Ho0SPltlWrVgkAIikpSWuZhBBiwYIFws3NTXkfgNiyZYtW+yzNs/eItj8rpGQQovTXwdraWkRHR2u8z7Zt2yrvFxUVCQsLCzFgwADltlu3bgkA4vDhw2LChAmicePGKsf48ssvBQBx//59jWQq6/NFCCFCQ0OFra2tyM/PV25bsWKFsLS0FMXFxRrpX4jSfx+bNGkipk6dWur+P//8s7Czs9NY/9rCU3U61qpVK5VTCq1bt0ZKSgqSkpJgaGgIPz8/nfRfXFxcYt93330Xbm5uqF27NgYMGID169fj4cOHGsty/vx5FBQU4J133inRdurUKfj4+JTrHIqXvTbNmzd/reM3adIE77zzDjw9PdG7d2+sWrUK9+/fh62tLQYOHIigoCB069YNixYtwq1bt5SPGzt2LP7zn/8gMDAQ3377bYlTY0ZGRmjRooXyfoMGDWBjY4Pz58+/Vt5/O3XqFNq1a6cy2lUe1HnPatqL3qPJycnw8vKCqampclvLli21nkkfafuzQh95eXkp/21oaAg7Ozt4enoqtz37CrDbt28jOTlZ5XcU0Px7pazPl+fbzc3Nlfdbt26NvLy81z6tr47//e9/eOedd1CjRg1YWVlhwIABuHfvnt6/V1g46annP3z1hZWVFU6ePImNGzfCyckJU6ZMQZMmTTR2Tt7MzOyV2nTFwsLitR5vaGiI3bt3488//8Rbb72FJUuWoH79+rh69Sqio6Nx+PBhtGnTBps2bUK9evVw5MgRAMDXX3+Nf/75B126dMGePXvw1ltvYcuWLZp4SmrRx5+JtlXG5/wqtP1ZIZVMJlOemnrmyZMnWunr3/+BkMlkKtueFfvanF7wvBd9vpQXAwODMl//1NRUdO3aFV5eXvj1119x4sQJLFu2DAA0PtdK01g46di/JzcfOXIEHh4eaNKkCRQKBfbt26eT/suaF2FkZITAwEBERkbi77//RmpqKvbs2aORLB4eHjAzM0N8fHyJNi8vL5w6dQpZWVka6UsKdV+bVyGTyeDr64tp06YhKSkJxsbGyiLIx8cHEREROHToEBo3bowNGzYoH1evXj2MGTMGu3btQq9evRAdHa1sKyoqUk4WB56OhGRnZ6Nhw4avldXY2FhlVMfLywsHDhzQ2h+isrzo5/LvjJr2ovdo/fr1cebMGRQUFCi3HT9+XGtZXqRKlSrlMgL3Itr8rJCqevXqKqO1KSkpejGaUb9+fZXfUUA775UXfb6cPn0ajx49Uu575MgRWFpawsXFRWP9//v1z83NVRZuJ06cgEKhwHfffYdWrVqhXr16uHnzpsb61iYWTjqWlpaGsWPHIjk5GRs3bsSSJUswatQo1KpVC6GhoRg0aBDi4uJw9epVJCQkYPPmzeXSf2m2bduGxYsX49SpU7h27RrWrl0LhUKB+vXraySLqakpJkyYgPHjx2Pt2rW4fPkyjhw5gqioKPTt2xeOjo4IDg7GwYMHceXKFfz66684fPiwRvoujTqvzas4evQoZs2ahcTERKSlpSE2NhZ37tyBmZkZIiIicPjwYVy7dg27du1CSkoKGjZsiEePHiE8PBwJCQm4du0aDh48iOPHj6sURVWqVMGIESNw9OhRnDhxAgMHDkSrVq1e+1RArVq1cPToUaSmpuLu3bsIDw9Hbm4u+vTpg8TERKSkpGDdunVITk5+3ZfmhV70c6lVqxb279+P9PR0jV9IAbz4PfrRRx9BoVBg6NChOH/+PHbu3Il58+YBgNau8CtLrVq1EB8fj4yMDJXTM+VF258VUnXo0AFLly5FUlISEhMTMWzYsHI/tVyaTz75BBcuXMCECRNw8eJFbN68WXmln6beK2V9vjz7rCgsLMTgwYNx7tw5bN++HVOnTkV4eDgMDDRXFnTo0AHr1q3DgQMHcObMGYSGhir/41m3bl08efIES5YswZUrV7Bu3TqsXLlSY31rla4nWVVmfn5+4rPPPhPDhg0TcrlcVK1aVUyaNEk58fXRo0dizJgxwsnJSRgbG4u6deuK1atXl1v//54cfuDAAeHn5yeqVq0qzMzMhJeXl9i0aZPG8gghRHFxsfjmm2+Em5ubqFKlinB1dRWzZs0SQgiRmpoqQkJChFwuF+bm5qJ58+avPeG5LC97bTQxCfncuXMiKChIVK9eXZiYmIh69eqJJUuWiIyMDBEcHKz8ubu5uYkpU6aI4uJiUVBQIPr06SNcXFyEsbGxcHZ2FuHh4coJydHR0cLa2lr8+uuvonbt2sLExEQEBgaKa9euve5LIpKTk0WrVq2EmZmZACCuXr0qTp8+Ld577z1hbm4urKysRLt27cTly5dfu6+yvOzncvjwYeHl5SVMTEyEtj7eXvQePXjwoPDy8hLGxsaiWbNmYsOGDQKAuHDhglayPPPvyeFbt24VdevWFUZGRirbte3ZxOzy+Kx4WQYhhEhPTxfvvfeesLCwEB4eHmL79u1amxz+/IUTQpT+GYHnJqv/9ttvom7dusLExET4+/uLFStWCAAqFxe8jrI+X4R4Ojm8R48eYsqUKcLOzk5YWlqKIUOGiMePH2uk72dycnLEhx9+KORyuXBxcRExMTEqk8Pnz58vnJychJmZmQgKChJr167V6AR5bZEJ8a8TkEQEf39/eHt7v3GrUMfExGD06NH8ehw9sX79eoSFhSEnJ4fzo+iFZs6ciZUrV5bL5OyBAwciOztbJ6vLVwRcx4mISEPWrl2L2rVro0aNGjh9+jQmTJiADz74gEUTlbB8+XK0aNECdnZ2OHjwIObOnYvw8HBdxyIJWDgREWlIRkYGpkyZgoyMDDg5OaF3797luqAgvTlSUlLwzTffICsrC66urvj8888RERGh61gkAU/VEREREUnEq+qIiIiIJGLhRERERCQRCyciIiIiiVg4EREREUnEwomIiIhIIhZORERERBKxcCIiIiKSiIUTVQgZGRkYNWoU6tatC1NTUzg4OMDX1xcrVqwo8W3os2fPhqGhIebOnVviODExMZDJZJDJZDAwMEDNmjURFhaG27dvK/d51i6TyWBkZARXV1eMHTsWBQUFyn3u3LmDTz/9FK6urjAxMYGjoyOCgoJw8ODBMp9DamoqBg8eDHd3d5iZmaFOnTqYOnUqCgsLlfskJCSgR48ecHJygoWFBby9vbF+/frXeemIKp2BAwdCJpPh22+/VdkeFxen/JLdhIQEld91BwcHhISE4MqVK8r9a9WqpWw3NDSEs7MzBg8eLOmLlQsLCxEZGYkmTZrA3Nwc1apVg6+vL6Kjo/HkyRPNPmHSKK4cTm+8K1euwNfXFzY2Npg1axY8PT1hYmKCM2fO4IcffkCNGjXQvXt35f6rV6/G+PHjsXr1aowbN67E8eRyOZKTk6FQKHD69GmEhYXh5s2b2Llzp3Kf6OhodOzYEU+ePFHuY2FhgRkzZgAAQkJCUFhYiDVr1qB27drIzMxEfHw87t27V+bzuHDhAhQKBb7//nvUrVsXZ8+exZAhQ5Cfn4958+YBAA4dOgQvLy9MmDABDg4O2LZtGz7++GNYW1uja9eumnpJiSo8U1NTzJkzB5988gmqVq1a5n7JycmwsrJCSkoKhg4dim7duuHvv/+GoaEhAGD69OkYMmQIiouLcfHiRQwdOhQjR47EunXryjxmYWEhgoKCcPr0acyYMQO+vr6Qy+U4cuQI5s2bBx8fH3h7e2v6KZOm6PY7holeX1BQkKhZs6bIy8srtV2hUCj/nZCQIGrUqCEKCwuFs7OzOHjwoMq+0dHRwtraWmXbzJkzhYGBgXj48KEQQvUbzp8ZPHiw6Ny5sxBCiPv37wsAIiEh4TWfmRCRkZHC3d39hft07txZhIWFvXZfRJVFaGio6Nq1q2jQoIEYN26ccvuWLVvEsz+Le/fuFQDE/fv3le3r168XAMSFCxeEEEK4ubmJBQsWqBx7xowZ4q233nph/3PmzBEGBgbi5MmTJdoKCwvL/Cwj/cBTdfRGu3fvHnbt2oXhw4fDwsKi1H2eDb0DQFRUFPr27YsqVaqgb9++iIqKemkfZmZmUCgUKCoqKrX94sWL2LNnD95++20AgKWlJSwtLREXF6dy+u5V5OTkwNbW9rX3ISJVhoaGmDVrFpYsWYIbN25IesyzL2t+/vT589LT0/H7778rPwvKsn79egQGBsLHx6dEW5UqVcr8LCP9wMKJ3miXLl2CEAL169dX2V6tWjVlATNhwgQAQG5uLn755Rf0798fANC/f39s3rwZeXl5ZR4/JSUFK1euRPPmzWFlZaXc3rdvX1haWsLU1BT169dHo0aNlF/QaWRkhJiYGKxZswY2Njbw9fXFpEmT8Pfff6v93JYsWYJPPvmkzH02b96M48ePIywsTK1jExHQs2dPeHt7Y+rUqS/d99atW5g3bx5q1Kih8nkzYcIEWFpawszMDDVr1oRMJsP8+fNfeKyUlBQ0aNDgtfOTbrBwogrp2LFjOHXqFBo1aqQc9dm4cSPq1KmDJk2aAAC8vb3h5uaGTZs2qTw2JycHlpaWMDc3R/369eHg4FBiAvaCBQtw6tQpnD59Gtu2bcPFixcxYMAAZXtISAhu3ryJrVu3omPHjkhISEDTpk0RExMDABg2bJiysLO0tCyRPz09HR07dkTv3r0xZMiQUp/j3r17ERYWhlWrVqFRo0av/FoRVWZz5szBmjVrcP78+VLba9asCQsLCzg7OyM/Px+//vorjI2Nle3jxo3DqVOn8PfffyM+Ph4A0KVLFxQXFwOAyu/5sGHDAABCCC0/K9IqXZ8rJHodd+/eFTKZTMyePbvUdj8/PzFq1CghhBAtWrQQMplMGBoaKm8ymUy0adNGuX90dLSwsrISKSkp4vLly8p5Tc9DKXOcNm7cKACIlJSUMrMOHjxYuLq6CiGEyMzMFCkpKcrb89LT04WHh4cYMGCAKC4uLvVYCQkJwsLCQnz//fdl9kdEpQsNDRU9evRQ3u/cubPo0aNHqXOcTp48KS5duiRyc3NLHKe0OU6HDx8WAMTu3buFEELl9zwzM1MIIYSXl5d47733tPPkSOt4VR290ezs7PDuu+9i6dKlGDFiRJlzA86cOYPExEQkJCSozAfKysqCv78/Lly4oBw6NzAwQN26ddXK8ewKm0ePHpW5z1tvvYW4uDgAgL29Pezt7Uvsk56ejoCAADRr1gzR0dEwMCg5KJyQkICuXbtizpw5GDp0qFo5iaikb7/9Ft7e3iVO+QOAu7s7bGxsJB/r358FpX2WfPTRR5g0aRKSkpJKzHN68uQJCgsLOc9Jj7Fwojfe8uXL4evri+bNm+Prr7+Gl5cXDAwMcPz4cVy4cAHNmjVDVFQUWrZsifbt25d4fIsWLRAVFVXquk5lyc7ORkZGBhQKBVJSUjB9+nTUq1cPDRs2xL1799C7d28MGjQIXl5esLKyQmJiIiIjI9GjR48yj5meng5/f3+4ublh3rx5uHPnjrLN0dERwNPTc127dsWoUaMQEhKCjIwMAICxsTEniBO9Ik9PT/Tr1w+LFy9W+7EPHjxARkYGhBC4fv06xo8fj+rVq6NNmzZlPmb06NH4448/8M4772DGjBlo27at8nNizpw5iIqK4nIE+kzXQ15EmnDz5k0RHh4u3N3dRZUqVYSlpaVo2bKlmDt3rsjJyRF2dnYiMjKy1MfOmTNH2Nvbi8LCwlKXI/g3AMqbTCYTTk5O4sMPPxSXL18WQgjx+PFjMXHiRNG0aVNhbW0tzM3NRf369cVXX31V6qm/Z6Kjo1WO/fztmdDQ0FLb/fz81H7NiCqrf5+qE0KIq1evCmNj4xcuR/Bvbm5uKr+H1atXF507dxZJSUkvzfD48WMxe/Zs4enpKUxNTYWtra3w9fUVMTEx4smTJ6/x7EjbZEJwlhoRERGRFLyqjoiIiEgiFk5EREREErFwIiIiIpKIhRMRERGRRCyciIiIiCRi4UREREQkEQsnIiIiIolYOBERERFJxMKJiIiISCIWTkREREQSsXAiIiIikoiFExEREZFE/w+SFau+/KVbegAAAABJRU5ErkJggg==",
       "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
+       "<Figure size 600x300 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1196,163 +1115,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 26,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 432x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Multi bar Chart 1\n",
-    "x1 = df_gap22_cas['app']\n",
-    "y1 = 100 * df_gap22_cas['numRdMissClean'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_gap22_cas['numRdMissDirty'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_gap22_cas['numWrMissClean'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_gap22_cas['numWrMissDirty'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y5 = 100 * df_gap22_cas['numRdHit'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "y6 = 100 * df_gap22_cas['numWrHit'].astype(float)/(df_gap22_cas['numTotMisses'].astype(float)+df_gap22_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,105])\n",
-    "\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*6, y1[i], width=4, color=cmap2(0), label='Read-Miss-Clean' if i==0 else None)\n",
-    "    plt.bar(i*6, y2[i], bottom = y1[i], width=4, color=cmap2(1), label='Read-Miss-Dirty' if i==0 else None)\n",
-    "    plt.bar(i*6, y3[i], bottom = y1[i]+y2[i], width=4, color=cmap2(2), label='Write-Miss-Clean' if i==0 else None)\n",
-    "    plt.bar(i*6, y4[i], bottom = y1[i]+y2[i]+y3[i], width=4, color=cmap2(3), label='Write-Miss-Dirty' if i==0 else None)\n",
-    "    plt.bar(i*6, y5[i], bottom = y1[i]+y2[i]+y3[i]+y4[i], width=4, color=cmap2(4), label='Read-Hit' if i==0 else None)\n",
-    "    plt.bar(i*6, y6[i], bottom = y1[i]+y2[i]+y3[i]+y4[i]+y5[i], width=4, color=cmap2(5), label='Write-Hit' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*6\n",
-    "x2 = df_npbC_cas['app']\n",
-    "y1 = 100 * df_npbC_cas['numRdMissClean'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_npbC_cas['numRdMissDirty'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_npbC_cas['numWrMissClean'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_npbC_cas['numWrMissDirty'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y5 = 100 * df_npbC_cas['numRdHit'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "y6 = 100 * df_npbC_cas['numWrHit'].astype(float)/(df_npbC_cas['numTotMisses'].astype(float)+df_npbC_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*6+offset, y1[i], width=4, color=cmap2(0))\n",
-    "    plt.bar(i*6+offset, y2[i], bottom = y1[i], width=4, color=cmap2(1))\n",
-    "    plt.bar(i*6+offset, y3[i], bottom = y1[i]+y2[i], width=4, color=cmap2(2))\n",
-    "    plt.bar(i*6+offset, y4[i], bottom = y1[i]+y2[i]+y3[i], width=4, color=cmap2(3))\n",
-    "    plt.bar(i*6+offset, y5[i], bottom = y1[i]+y2[i]+y3[i]+y4[i], width=4, color=cmap2(4))\n",
-    "    plt.bar(i*6+offset, y6[i], bottom = y1[i]+y2[i]+y3[i]+y4[i]+y5[i], width=4, color=cmap2(5))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*6, list(x1)+list(x2))\n",
-    "plt.yticks(np.arange(6)*20, [0,20,40,60,80,100])\n",
-    "plt.axvline(x=offset-3, color='black')\n",
-    "\n",
-    "plt.ylabel(\"DRAM Cache Miss Rate (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=3,loc=(0,1.02))\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"/home/babaie/projects/rambusDesign/paper-draft1/papers/tdram-paper/hpca2024-latex-template/figures/hitMissBreakdown_g22nC.pdf\")\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "x1 = df_gap25_cas['app']\n",
-    "y1 = 100 * df_gap25_cas['numRdMissClean'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_gap25_cas['numRdMissDirty'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_gap25_cas['numWrMissClean'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_gap25_cas['numWrMissDirty'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y5 = 100 * df_gap25_cas['numRdHit'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "y6 = 100 * df_gap25_cas['numWrHit'].astype(float)/(df_gap25_cas['numTotMisses'].astype(float)+df_gap25_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(6,3)\n",
-    "plt.ylim([0,105])\n",
-    "\n",
-    "for i,app in enumerate(x1): \n",
-    "    plt.bar(i*6, y1[i], width=4, color=cmap2(0), label='Read-Miss-Clean' if i==0 else None)\n",
-    "    plt.bar(i*6, y2[i], bottom = y1[i], width=4, color=cmap2(1), label='Read-Miss-Dirty' if i==0 else None)\n",
-    "    plt.bar(i*6, y3[i], bottom = y1[i]+y2[i], width=4, color=cmap2(2), label='Write-Miss-Clean' if i==0 else None)\n",
-    "    plt.bar(i*6, y4[i], bottom = y1[i]+y2[i]+y3[i], width=4, color=cmap2(3), label='Write-Miss-Dirty' if i==0 else None)\n",
-    "    plt.bar(i*6, y5[i], bottom = y1[i]+y2[i]+y3[i]+y4[i], width=4, color=cmap2(4), label='Read-Hit' if i==0 else None)\n",
-    "    plt.bar(i*6, y6[i], bottom = y1[i]+y2[i]+y3[i]+y4[i]+y5[i], width=4, color=cmap2(5), label='Write-Hit' if i==0 else None)\n",
-    "\n",
-    "offset = (i+1)*6\n",
-    "x2 = df_npbD_cas['app']\n",
-    "y1 = 100 * df_npbD_cas['numRdMissClean'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y2 = 100 * df_npbD_cas['numRdMissDirty'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y3 = 100 * df_npbD_cas['numWrMissClean'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y4 = 100 * df_npbD_cas['numWrMissDirty'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y5 = 100 * df_npbD_cas['numRdHit'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "y6 = 100 * df_npbD_cas['numWrHit'].astype(float)/(df_npbD_cas['numTotMisses'].astype(float)+df_npbD_cas['numTotHits'].astype(float))\n",
-    "\n",
-    "for i,app in enumerate(x2): \n",
-    "    plt.bar(i*6+offset, y1[i], width=4, color=cmap2(0))\n",
-    "    plt.bar(i*6+offset, y2[i], bottom = y1[i], width=4, color=cmap2(1))\n",
-    "    plt.bar(i*6+offset, y3[i], bottom = y1[i]+y2[i], width=4, color=cmap2(2))\n",
-    "    plt.bar(i*6+offset, y4[i], bottom = y1[i]+y2[i]+y3[i], width=4, color=cmap2(3))\n",
-    "    plt.bar(i*6+offset, y5[i], bottom = y1[i]+y2[i]+y3[i]+y4[i], width=4, color=cmap2(4))\n",
-    "    plt.bar(i*6+offset, y6[i], bottom = y1[i]+y2[i]+y3[i]+y4[i]+y5[i], width=4, color=cmap2(5))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*6, list(x1)+list(x2))\n",
-    "plt.yticks(np.arange(6)*20, [0,20,40,60,80,100])\n",
-    "plt.axvline(x=offset-3, color='black')\n",
-    "\n",
-    "plt.ylabel(\"DRAM Cache Miss Rate (%)\", fontsize=10)\n",
-    "plt.legend(fontsize=9, ncol=3, loc=(0,1.02))\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"/home/babaie/projects/rambusDesign/paper-draft1/papers/tdram-paper/hpca2024-latex-template/figures/hitMissBreakdown_g25nD.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1425,120 +1208,27 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_cas_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade-tcBrkDwn2/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_cas_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade-tcBrkDwn2/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_cas_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade-tcBrkDwn2/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_cas_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/cascade-tcBrkDwn2/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ram_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus-tcBrkDwn/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ram_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus-tcBrkDwn/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_ram_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus-tcBrkDwn/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ram_brk = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/baseline/rambus-tcBrkDwn/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "app_gap = df_gap22_cas_brk['app']\n",
-    "gap_22_cas = df_gap22_cas_brk['avgTCLatRdM'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "print(gap_22_cas)\n",
-    "gap_25_cas = df_gap25_cas_brk['avgTCLatRdM'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "\n",
-    "app_npb = df_npbC_cas_brk['app']\n",
-    "npb_C_cas = df_npbC_cas_brk['avgTCLatRdM'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "\n",
-    "npb_D_cas = df_npbD_cas_brk['avgTCLatRdM'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_22_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_22_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_C_cas[i],   width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-C\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Avg Tag Check Latency\\n(Rd-Miss) (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "# Multi bar Chart2\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*3,   gap_25_cas[i],   width=1, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*3+1, gap_25_ram[i],   width=1, color=cmap(2), label='TDRAM' if i==0 else None)\n",
-    "\n",
-    "offset = i*3+2\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar(offset+i*3+1,   npb_D_cas[i],     width=1, color=cmap(1))\n",
-    "    plt.bar(offset+i*3+2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.3, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.75, -0.01, \"NPB-D\")\n",
-    "\n",
-    "plt.xticks(np.arange(14)*3+0.5, list(app_gap)+list(app_npb))\n",
-    "plt.axvline(x=offset, color='black')\n",
-    "\n",
-    "plt.ylabel(\"Avg Tag Check Latency\\n(Rd-Miss) (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2)\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "print(gap_22_cas)\n",
-    "print(gap_22_ram)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 576x144 with 1 Axes>"
+       "<Figure size 800x200 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
@@ -1609,1140 +1299,6 @@
     "plt.legend(fontsize=8, ncol=2, loc='upper left')\n",
     "plt.tight_layout()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### *Tag Probing Optimization*\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 31,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/rambusTagPr/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/rambusTagPr/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap25_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/rambusTagPr/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ram_prob = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/rambusTagPr/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 32,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_462509/3550682698.py:24: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/3550682698.py:27: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/3550682698.py:61: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/3550682698.py:64: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(df_gap22_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(df_gap25_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(df_npbC_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['avgTimeTagCheckResRd'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['avgTimeTagCheckResRd'].astype(float))\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(df_npbD_ram['avgTimeTagCheckResRd'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,220])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Tag Comparison Latency (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,220])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Tag Comparison Latency (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 33,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_462509/3960693518.py:27: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/3960693518.py:30: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/3960693518.py:64: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/3960693518.py:67: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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kZmZm1kBZEpy9IuKnwGqAiPgQ8OVJZmZm1mxlSXA+lbQVScdiJO1FwdVUZmZmZs1NlquofgiMA3aX9CDQC/h6nkGZmZmZNUSWq6jGS5oGHE5yaurbEbE098jMzMzMipTlKqoJEbEsIsZGxNMRsVTShMYIzszMzKwYNbbgSGoDbA3sJGl7PutYvB2wWyPEZmZmZlaU2k5RfRO4FPgcMI3PEpyVwJ35hmVmZmZWvBoTnIi4DbhN0iURcUcjxmRmZmbWIFk6Gd8hqRuwH9CmoPyBPAMzMzMzK1adCY6k64EykgTnGeBYYDLgBMfMzMyapSwD/Z0C9APejYivAwcA7XKNyszMzKwBsiQ4H0XEOmCNpO2AJcDu+YZlZmZmVrwsIxlPldQeuJfkaqpVwF/yDMrMzMysIepswYmIiyNieUTcAxwNnANcU9frJN0naYmkmQVlP5S0SNL09HZcwXNXS3pD0lxJxxS5P2ZmZmaZWnAqRcR8AElvA53qWH0EyXg5VTsj3xoRPy8skLQfcBrQlWTcnT9J+nxErK1PfGZmZmaQrQ9OdVTXChExCfhXxu0NAB6OiE8i4i3gDeDQImMzMzOzTVyxCU40oM6hkl5LT2Ftn5btBrxTsM5CPB2EmZmZFam2uajuoPpERkD7Iuu7G/jvdLv/DdwCnFufDUi6ALgAoFOnus6SmZmZ2aaotj44U4t8rkYR8V7FY0n3Ak+ni4tY/9LzjmlZddsYBgwD6NGjR0NakszMzKyFqm0uqvtLXZmkDhGxOF08Cai4wmoM8JCkX5B0Mt4HeLnU9ZuZmdmmoV5XUdWHpFEkUzzsJGkhcD1QJulAklNU80lmLCciZkl6FJgNrAGG+AoqMzMzK1ZuCU5EnF5N8a9rWf9/gP/JKx4zMzPbdBR7FZWZmZlZs5VlNvGdgfOBzoXrR0S9rn4yMzMzayxZTlGNBp4H/gS4X4yZmZk1e1kSnK0j4nu5R2JmZmZWIln64DxdOCmmmZmZWXOXJcH5NkmS87GkD9LbyrwDMzMzMytWnaeoIqJtYwRiZmZmViqZxsGRdALw5XSxPCKerm19MzMzs6ZU5ykqSTeTnKaand6+LemmvAMzMzMzK1aWFpzjgAMjYh2ApPuBV4Cr8wzMzMzMrFhZRzJuX/C4XQ5xmJmZmZVMlhacm4BXJE0ERNIX56pcozIzMzNrgCxXUY2SVA4ckhZ9LyLezTUqMzMzswbIMhdVL2B6RIyRdCZwpaTbImJB/uGZJU546vhM6405cWzOkZiZ2cYgSx+cu4EPJR0AXAa8CTyQa1RmZmZmDZAlwVkTEQEMAO6KiLsAD/5nZmZmzVaWTsYfSLoaOBP4sqTNgNb5hmVmZmZWvCwtOKcCnwDfSDsXdwR+lmtUZmZmZg2Q5Sqqd4FfFCy/jfvgmJmZWTNWY4IjaXJE9Jb0ARCFTwEREdvlHp2ZmZlZEWpMcCKid3rvDsVmZma2Uam1D46kVpJeb6xgzMzMzEqh1gQnItYCcyV1aqR4zMzMzBosy2Xi2wOzJL0M/LuiMCJOyC0qMzMzswbIkuD8IPcozMzMzEooy2XizzVGIGZmZmalUudAf5IOlzRF0ipJn0paK2llYwRnZmZmVowsIxnfCZwO/APYCjgPuCvPoMzMzMwaIkuCQ0S8AbSKiLUR8RvgK/mGZWZmZla8LJ2MP5S0BTBd0k+BxWRMjMzMzMyaQpZE5ax0vaEkl4nvDvxXnkGZmZmZNUSWq6gWpC04nYEngLkR8WnegZmZmZkVK8tVVMcDbwK3k3Q4fkPSsRled5+kJZJmFpTtIOmPkv6R3m+flkvS7ZLekPSapIOK3yUzMzPb1GU5RXUL0CciyiLiSKAPcGuG141gw87IVwETImIfYEK6DHAssE96uwC4O8P2zczMzKqVJcH5IL2KqsI84IO6XhQRk4B/VSkeANyfPr4fOLGg/IFI/BVoL6lDhtjMzMzMNpDlKqqpkp4BHgUCGAhMkXQyQEQ8UY/6do2Ixenjd4Fd08e7Ae8UrLcwLVtMFZIuIGnloVMnzwFqZmZmG8rSgtMGeA84EigD3icZ8K8/8NViK46IIEmY6vu6YRHRIyJ67LzzzsVWb2ZmZi1Ylquovl7C+t6T1CEiFqenoJak5YtILj+v0DEtMzMzM6u3OhMcSV2AS0guE69cPyJOKKK+McA5wM3p/eiC8qGSHgYOA1YUnMoyMzMzq5csfXCeAn4N/B5Yl3XDkkaRnNLaSdJC4HqSxOZRSd8AFgCD0tWfAY4D3gA+BErZamRmZmabmCwJzscRcXt9NxwRp9fwVL9q1g1gSH3rMDMzM6tOlgTnNknXA+OBTyoKI+JvuUVlZmZm1gBZEpz9Seaj6stnp6giXTYz2yid8NTxmdYbc+LYnCMxszxkSXAGAnt6/ikzMzPbWGQZB2cm0D7nOMzMzMxKJksLTnvgdUlTWL8PTjGXiZuZmZnlLkuCc33uUZiZmZmVUJaRjJ+TtCtwSFr0ckQsqe01ZmZmZk2pzj44kgYBL5N0Nh4EvCTplLwDMzMzMytWllNU1wCHVLTaSNoZ+BPweJ6BmZmZmRUry1VUm1U5JbUs4+vMzMzMmkSWFpxxkv4AjEqXTwWezS8kMzMzs4bJ0sn4CkknA73TomER8WS+YZlt2jzKrplZw9SY4EjaG9g1Il6IiCeAJ9Ly3pL2iog3GytIMzMzs/qorS/NL4GV1ZSvSJ8zMzMza5ZqS3B2jYgZVQvTss65RWRmZmbWQLUlOO1reW6rEsdhZmZmVjK1dTKeKun8iLi3sFDSecC0fMMyax7c2dfMbONUW4JzKfCkpK/xWULTA9gCOCnnuMzMzMyKVmOCExHvAV+S1AfolhaPjYg/N0pkZmZmZkXKMg7ORGBiI8RiZmZmVhKecsHMzMxanCxTNZjZJsKdqs2spXCCY2ZmZvXW3H8Q1ZngSPoAiCrFK4CpwHcjYl4egVntmvsHy8zMrCllacH5JbAQeAgQcBqwF/A34D6gLKfYzMzMzIqSJcE5ISIOKFgeJml6RHxP0vfzCszMNg1ujbSWxJ/n5iPLVVQfShokabP0Ngj4OH2u6qkrMzMzsyaXJcH5GnAWsAR4L318pqStgKE5xmZmZmZWlCwD/c0D+tfw9OTShmNmZmbWcFmuomoDfAPoCrSpKI+Ic3OMy8zMzKxoWToZ/xZ4HTgG+DHJKas5eQZlZmal5c6vtqnJkuDsHREDJQ2IiPslPQQ835BKJc0HPgDWAmsiooekHYBHgM7AfGBQRPxfQ+qx5u/w6/+Qab1duucciJltFHzMsKxq7GQsqSL5WZ3eL5fUDWgH7FKCuvtExIER0SNdvgqYEBH7ABPSZTMzM7N6q60F52XgIJJxb7YHfgCMAbYFrsshlgF8Nmjg/UA58L0c6jEzs0biU2PWVGpLcAQQEcPT5eeAPUtUbwDjJQXwq4gYBuwaEYvT598Fdi1RXWZmZraJqS3B2VnSZTU9GRG/aEC9vSNikaRdgD9Ker3KtiNNfjYg6QLgAoBOnTo1IAQza4maex8Nt2iYNY7aBvprRXI6qm0Nt6JFxKL0fgnwJHAo8J6kDgDp/ZIaXjssInpERI+dd965IWGYmZlZC1VbC87iiPhxqSuUtA2wWUR8kD7+T5LLz8cA5wA3p/ejS123mZmZbRrq7IOTg12BJyVV1P9QRIyTNAV4VNI3gAXAoJzqNzMzsxautgSnXx4VplM/HFBN+bK86rTScf8BMzPbGNTYByci/tWYgZiZmZmVSpaRjK0OWVo13KLRvDT3K23MrHnxMWPjk2WyzW2AjyJinaTPA18Eno2I1XW81IrgL5GZmVnDZWnBmQQckY5mPB6YApxKMummmdVDUyWwzbleJ+tmzUtL+aFd2zg4FRQRHwInA/8bEQOBrvmGZWZmZla8LC04ktSTpMXmG2lZq/xCMjOzrJpz61we9dqGfHVr9bK04FwKXA08GRGzJO0JTMw1KjMzM7MGqLMFJyKeI5loE0mbAUsj4lt5B2ZmZmZWrCxXUT0EXAisJelgvJ2k2yLiZ3kHZ43Hzc1mZtaSZOmDs19ErJT0NeBZ4CpgGuAEx8ws5R8JZs1LlgSntaTWwInAnRGxWlLkG1bz4AOWmZnZxilLJ+NfAfOBbYBJkvYAVuYZlJmZmVlDZOlkfDtwe0HRAkl98gvJzMzMrGFqTHAknRkRIyVdVsMqv8gpJjMzs02eu0k0TG0tONuk920bIxAzMzOzUqkxwYmIX6X3P2q8cMzMzMwarrZTVLfX9ByAB/szMzOz5qq2U1QXAjOBR4F/AmqUiMzMzMwaqLYEpwMwEDgVWAM8AjweEcsbIS4zMzOzotU4Dk5ELIuIeyKiD/B1oD0wW9JZjRWcmZmZWTGyzEV1EHA6cDTJVA3T8g7KzMzMrCFq62T8Y+B4YA7wMHB1RKxprMDMzMzMilVbC861wFvAAentRkmQdDaOiPiP/MMzMzMzq7/aEpwujRaFmZmZWQnVNtDfgqplkr4aEU/nG5KZmZlZw2SZTbzQj3OJwszMzKyE6pvgeLA/MzMza/bqm+B8M5cozMzMzEqotsvET66hvCNARDyRV1BmZmZmDVHbVVT90/tdgC8Bf06X+wAvAk5wzMzMrFmq7SqqrwNIGg/sFxGL0+UOwIhGic7MzMysCFn64Oxekdyk3gM65RQPkr4iaa6kNyRdlVc9ZmZm1nLVORcVMEHSH4BR6fKpwJ/yCEZSK+AuknmvFgJTJI2JiNl51GdmZmYtU50JTkQMlXQS8OW0aFhEPJlTPIcCb0TEPABJDwMDACc4ZmZmllmWFhzShOZJAEmbSfpaRDyYQzy7Ae8ULC8EDsuhHjMzM2vBartMfDtgCEnSMQb4Y7p8OfAqkEeCUydJFwAXpIurJM1tijiq2AlYWtsKKv0YiXXW6Xpdb2PXm07I26h1NoDrdb0tpd5NaV+rs0d1hbW14PwW+D/gL8B5wPdJRjI+MSKmlzq61CJg94LljmlZpYgYBgzLqf6iSJoaET1aep2u1/W2lDpdr+ttSfVuSvtaH7UlOHtGxP4AkoYDi4FOEfFxjvFMAfaR1IUksTkNOCPH+szMzKwFqi3BWV3xICLWSlqYc3JDRKyRNBT4A9AKuC8iZuVZp5mZmbU8tSU4B0hamT4WsFW6LCAiYrs8AoqIZ4Bn8th2jprilFlTnaZzva63JdTpel1vS6p3U9rXzBQRTR2DmZmZWUnVdzZxMzMzs2bPCU49SeosaWZT1yfpCEmzJE2XtFVjxWOlJ6m9pIubOo7GUstn+lJJWzdFTI1B0rckzZH0b0n7NVKdLzZGPVXqXNXYdZpVxwnOxutrwE0RcWBEfNTUwTSFdGqPlqA9sMkkOLW4FGixCQ7J3/ho4DGgURKciPhSY9Rj1hw5wSnO5pIeTH+NPS5pa0mHSHpR0quSXpbUNsf6vgUMAv47Le8gaVLamjNT0hGlqljS2ZJeS/frt5J2lfRkuvyqpFwOoOmv/NereZ/nS/qJpL8BAxuw/W0kjU33YaakUyXdLGl2ur8/T9cbmD7/qqRJadlgSaMllUv6h6TrG7i7NwN7pX+/n0n6nqQZaZ0313O/fpBOVjtZ0ihJl6dx3ippavpeHiLpiTT2Gwpee2b62Z0u6VcVCaSku9PXzpL0o4L150v6kaS/pfF+sR6hVveZ/hwwUdLE+uxzVtV8lveS9Nc09hvybHmQdA+wJ/AWcA7ws/R93iuvOtN6V6X3uR0jaqm7TNLTBct3Shpc4joqjhMjJP09/UwdJemF9PN9qKSdJf0x/fwOl7RA0k4lqLu6Y8h8ST9NP1MvS9q7FPtZpd71WkDT7/gPJZ0vaUoaz++UoTV0IzpeFCcifKvHDegMBNArXb4PuBKYBxySlm0HbJ5jfZcDI4BT0rLvAtekj1sBbUtUd1fg78BO6fIOwCPApQV1tWvE9/lyYD5wZQm2/1/AvQXLewBz+azjffv0fgawW5WywSTjQu0IbAXMBHo0cF9npo+PBV4Etq54z+uxnUOA6UAboC3wj/Q9Kwd+kq7zbeCfQAdgS5LpUHYE9gV+D7RO1/tf4OzCGNK/dznwH+nyfOCS9PHFwPAS/G13yunzVN1n+Wng9HT5QmBVHnUXxDCfZOTXyu9u3reKfSKnY0QddZYBTxeU3wkMLnFdnYE1wP4kP9inpZ8nkcxj+FRa79Xp+l9JP3sN/pyx4TGkXfo3rnifzy7c/xLv88yC5cuBHwI7FpTdUPHdrGU7G8XxoiE3t+AU552IeCF9PBI4BlgcEVMAImJlRKzJsb7eVZ6fAnxd0g+B/SPigxLV2xd4LCKWAkTEv9Kyu9PltRGxokR1Vaem/X6kBNueARytpDXoCJKBJT8Gfi3pZODDdL0XgBGSzif5wlb4Y0Qsi+T04BNs+Dcp1lHAbyLiQ6h8z7PqBYyOiI/Tz8DvC54bk97PAGZFxOKI+IQkMd8d6AccDEyRND1d3jN9zSAlLWavkCQKhadXnkjvp5EceLOq6zNdatV9lnuSnC4CeCjn+ptaXseI5uCtiJgREeuAWcCESP6LziD5TPYGHgaIiHEkI/SXwnrHkIJj4aiC+54lqiuLbpKelzSDpAtD1zrW35iOF0VxglOcqtfWr6x2rfzqW285IiaRzPa+iOSf8dk5x9NYatrvfzd4wxF/Bw4i+QLfQDIVyaHA48BXgXHpehcC15J8qadJ2rGO2JqrT9L7dQWPK5Y3J/nFe38kfboOjIgvRMQPlYwqfjnQLyL+AxhL8ouv6nbXknHy3tTG9v5t1JroGLGG9f/HtKlpxQaq+nku/KzX5zNZL1WPIZKuq3iqcLUcqq7pfR0BDI1kBoIf0bD3u7kdL4riBKc4nSRVZOZnAH8FOkg6BEBSW0ml/ONVrW9y4ZOS9gDei4h7geEkX7pS+DMwsOKfuqQdgAnARelyK0ntSlRXdWrd74aQ9Dngw4gYCfyM5ODfLpKBJr8DHJCut1dEvBQR1wHv89lcaUdL2kHJFWwnkrT0FOsDkiZiSCa1/XrF+fP0Pc/qBaC/pDaStiVJ1LKaAJwiaZeKetPP1XYkCeUKSbuSnEIrher+toXvQ6lV91n+K8lpBkimhWksee5ntXI8RtRmAbCfpC0ltSf5ld8UXiDps4ik/wS2L8VGqzmGVLynpxbc/6UUdVXxHrCLpB0lbcln3/O2wGJJrUlacOqyMR0vipJ7BtVCzQWGSLoPmA3cQXIAvSP9h/cRyamGUnVarFrf3STnPCuUAVdIWp3WWZJfZxExS9L/AM9JWkvS5PhtYJikb5Bk4ReRz5cYqt/vS0q07f1JOnquI5mW5DLgaUltSH6dXJau9zNJ+6RlE4BXgQOBl4HfkUwIOzIiphYbSEQsU9IpcibwLEnz8FRJn5KM6v39jNuZImkM8BrJQXAGkOkUYkTMlnQtMF7SZiTvyZCI+KukV4DXgXdoWCJXqLq/7afAOEn/jIg+JaoHqPGzfCkwUtI1JC12eZ5uLfQwcK+SjtWnRMSbjVBnGTkcI2oTEe9IepSkj9pbJO95U/gRMErSWSTHqndJksyGqnoMuYikBXh7Sa+RtFacXoJ61hMRqyX9mOQYtIjkuwnwA+Alkh9iL1FHEr2RHS+K4pGMrVmS1Jmkg163po6lKiVXgvSIiKFNHUtVkraNiFVpC9Ak4IKI+FtTx9Ucpe/RRxERkk4j6XA8oKnjstJKWznWRjLXYU/g7og4MKe65pMcG5bmsf1Sa+nHC7fgmLUsw5QMIteG5Bx5izlY5eBg4E5JApYD5zZtOJaTTsCjaUvDp8D5TRxPc9KijxduwTEzM7MWx52MzczMrMVxgmNmZmYtjhMcMzMza3Gc4JiZmVmL4wTHiqJk0s2HJM2TNE3SXySdVPD8LyUtSq9cqCgbLOl9JZOzzU6nP6haPkvpxJrpc4dLeil9bo6Soeari+dBJZPGzZR0XzrYVcWEfyvS10/XZ6ONmlkJSApJtxQsX17xPVUyCeQifTbJ5wnVlL+uZILGav8fSVpbcGx4VdJ3a1rXrJA/JFZv6WW1TwGTImLPiDiYZCTYjunzmwEnkQz0dGSVlz+SjkFRBtyYjnZZWR4RXUku5awYDfR+krEZDgS6AY/WENaDwBdJBt/aCjiv4LnnC4YU/3FRO21mNfkEOFk1z9B9a/r9HQjcV5CcVJTvR/K9rXqsqPBRwbHhaJLRca8vVfDWcjnBsWL0BT6NiHsqCiJiQUTckS6WkUx6dzc1jOQZEUuAN0lm8a6kZIqLbfhsQrxdSGburpjcc3YN23smUiQjfHYsbtfMrJ7WAMNIpjipUUTMSdetmghtQTIOS52TYKbHjQuAoekPLbMaOcGxYnQFahsQ6nSSmXSfBI6vOF1USNKeJLPPvpEWnapkVtpFwA58NrPtrcBcSU9K+mY6lUKN0rrOIp0sM9Uzbdp+VlJdM+yaWf3dBXxNtcxNJ+kwkska30+LvpN+5xcDf4+I6Vkqioh5QCuSHz9mNXKCYw0m6a40gZgiaQvgOOCpiFhJMifKMQWrVyQyo4BvRsS/0vKKU1f/j2ROlCsA0lNKPYDxJJMyFiYu1flfklNnz6fLfwP2iIgDSOYMe6oh+2pmG0q/6w8A36rm6YpE5ufAqfHZ6LIVp6h2AbZJp8swKxknOFaMWRTMRhwRQ0hmCt6ZJJlpD8xI52XpzfqnqSr62hwWEU9W3XB68Ps9yezeFWVvRsTdaR0HKJlF9w9px8PhFetJuj6N4bKC166MiFXp42eA1rX0FTCz4v0S+AbJKeZCt6bf+SMKfnhUiojVJD9cvixp94ILAi6srpK09XctsKS04VtL4wTHivFnoI2kiwrKtk7vTwfOi4jOEdEZ6AIcXXFVVEa9SfrnIOn4gnPt+5Ac2JZHxDHpQfO8dL3zSJKr0yNiXcWGJP2/itdLOpTkM7+sfrtrZnVJW2MfJUlyMku/n72ANyPinYILAu6pZt2dgXuAOwtagsyq5ck2rd7S2ZdPBG6VdCXJOfV/k1zZcCtwYcG6/5Y0Gehfx2ZPldSbJAFZCAxOy89K6/mQpIPi1yJibTWvvwdYAPwlzWeeSE9vnQJcJGkN8BFwmg+MZrm5BRiacd3vSDoTaA28RnJ6uTpbpae4WpMcA34L/KKBcdomwJNtmpmZWYvjU1RmZmbW4jjBMTMzsxbHCY6ZmZm1OE5wzMzMrMVxgmNmZmYtjhMcMzMza3Gc4JiZmVmL4wTHzMzMWpz/D9hvBbmrzojIAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['avgTCLatRdM'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['avgTCLatRdM'].astype(float))\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(df_gap22_ram_brk['avgTCLatRdM'].astype(float))\n",
-    "\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['avgTCLatRdM'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['avgTCLatRdM'].astype(float))\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(df_gap25_ram_brk['avgTCLatRdM'].astype(float))\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['avgTCLatRdM'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['avgTCLatRdM'].astype(float))\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(df_npbC_ram_brk['avgTCLatRdM'].astype(float))\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['avgTCLatRdM'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram_brk['avgTCLatRdM'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['avgTCLatRdM'].astype(float))\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(df_npbD_ram_brk['avgTCLatRdM'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,220])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Rd-Miss Tag Comparison Latency (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=1, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,220])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Rd-Miss Tag Comparison Latency (ns)\")\n",
-    "plt.legend(fontsize=9, ncol=1, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 34,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/tmp/ipykernel_462509/764342778.py:32: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/764342778.py:35: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/764342778.py:69: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_gap[len(app_gap)] = \"gmean\"\n",
-      "/tmp/ipykernel_462509/764342778.py:72: SettingWithCopyWarning: \n",
-      "A value is trying to be set on a copy of a slice from a DataFrame\n",
-      "\n",
-      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
-      "  app_npb[len(app_npb)] = \"gmean\"\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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tmuvBeQZYDvQluVS8zrvAvCyDMuup/AvfWsM3GOxa8vL9zct21WSCExGvA68DQzouHDMzM7P26+yxqMwsQ139l1hefvGaWdfT4lANZmZmZt1Nawbb/AIQJINtfpRpVD1QV/+lbWZm1p20mOBIOha4GXgZEMlVVd+MiEezDs6y58TKzMzyqNjBNg+LiKUAknYDHgGc4JiZmfVQXf0cumISnHfrkpvUKySXipt1O+6xMjPrGYodbHMa8FuSc3CGA7MlfQ0gIu7LMD4zMzOzVismwekF/BU4NJ1eAWwJDCNJeJzg9ABdvSvS2sefr5nlTYsJTkSc2RGBmJmZ2cd8SL19irmK6r9Jemo2ERFnZRKRmZmZWTsVc4jq4YLnvYCvAm9mE46ZWdv5F6/1RD7E3LhiDlFNKZyWdBcwK7OIzMzMzNqpLWNR7QHsUOpArDh5ydT9S9vMzLJUzDk475Kcg6P08X+Bf884LrNOlZdE0syspyrmEFXvjgjEzMzMrFSaTHAk7dvcCyPi+dKHY2ZmZtZ+zfXgXJ8+9gKqgBdIDlN9CagBhmQbmpmZlYIPuVpP9KmmZkTEYRFxGLAc2DciqiJiEDAQeKOjAjQzMzNrrSYTnAKfj4j5dRMRsQDYM7uQzMzMzNqnmARnnqRbJVWnf7cA81p6kaSJkt6StKCJ+ZL0c0lLJc1r6ZwfMzMzs2IVcx+cM4HzgYvS6RnATUW8bhIwHvhNE/OPJrmnzh7A4HSZg4tYrplZu/icFLP8U8Qnhpn6ZCVpS2CXiFjSqoVLFcDDEbF3I/P+C5geEXel00uA6ohY3twyKysr46qrrmpNGK3y/GtvF1Vv862LOw1p774D3K7bzW27c+fOBWCfffbp0Hab4nbdLsC+FdsVVa9YXX1989JuW5155plzIqKqYXmLh6gkHQfMBR5Lp/eRNLUEMe0ELCuYrk3LGovhXEk1kmrWrVtXgqbNzMwsz1rswZE0BzicpLdlYFo2PyJaTMla6MF5GLguImal008A/x4RNc0ts6qqKmpqmq3SLsUOIbDDwJ8XVa/YLm6363a7Y7vV1dUATJ8+vUPbbYrbdbtQ+iFeuvr65qXdtpLUth4cYF1ErG5Q1vJxrZa9AexcMN0fX35uZmZmJVBMgrNQ0qlAmaQ9JI0DnilB21OBM9KrqQ4AVrd0/o2ZmZlZMYq5iupCYDTwIXAn8DvgmpZeJOkuoBroK6kWuAooB4iIm4FpwDHAUuA9kqu1zMzMzNqtmME23wNGS/ph+rwoEXFKC/MDuKDY5ZmZmZkVq5irqP5R0iLgxXT6y5J+mXlkZmZmZm1UzDk4Y4AjgVUAEfECcEiWQZmZmZm1RzEJDhGxrEHRhgxiMTMzMyuJYk4yXibpH4GQVE4yZMPibMMyMzNrGw/FYVBcD855JCcD7wS8CeyDTw42MzOzLqyYq6hWAl/vgFjMzMzMSqKYq6g+J+khSSskvSXpQUmf64jgzMzMzNqimENUdwK/BXYEPgvcA9yVZVBmZmZm7VFMgrNVRNwWEevTv9uBXlkHZmZmZtZWxVxF9aik7wF3kwyyeRIwTdJ2ABHxdobxmZmZmbVaMQnOiPTxmw3KTyZJeHw+jpmZmXUpxVxFVdkRgZiZmZmVSpPn4EjaT9JnCqbPSK+g+nnd4SkzMzOzrqi5k4z/C/gIQNIhwHXAb4DVwITsQzMzMzNrm+YOUZUVnEB8EjAhIqYAUyTNzTwyMzMzszZqrgenTFJdAjQU+EPBvGJOTjYzMzPrFM0lKncBT0laCbwPzASQtDvJYSozMzOzLqnJBCcifijpCZI7GD8eEZHO+hRwYUcEZ2ZmZtYWzR5qiohnGyl7KbtwzMzMzNqvmKEazMzMzLoVJzhmZmaWO05wzMzMLHec4JiZmVnuOMExMzOz3HGCY2ZmZrnjBMfMzMxyJ9MER9JRkpZIWirpe43MHylphaS56d/ZWcZjZmZmPUNmY0pJKgN+AXwFqAVmS5oaEYsaVJ0cEaOyisPMzMx6nix7cPYHlkbEKxHxEXA3cHyG7ZmZmZkB2SY4OwHLCqZr07KG/kXSPEn3Stq5sQVJOldSjaSaFStWZBGrmZmZ5Uhnn2T8EFAREV8Cfg/8urFKETEhIqoioqpfv34dGqCZmZl1P1kmOG8AhT0y/dOyehGxKiI+TCdvBQZlGI+ZmZn1EFkmOLOBPSRVStocOBmYWlhB0o4Fk8cBizOMx8zMzHqIzK6iioj1kkYBvwPKgIkRsVDSfwI1ETEV+Lak44D1wNvAyKziMTMzs54jswQHICKmAdMalF1Z8Pxy4PIsYzAzM7Oep7NPMjYzMzMrOSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLHSc4ZmZmljtOcMzMzCx3nOCYmZlZ7jjBMTMzs9xxgmNmZma54wTHzMzMcscJjpmZmeWOExwzMzPLHSc4ZmZmljuZJjiSjpK0RNJSSd9rZP4Wkian85+TVJFlPGZmZtYzZJbgSCoDfgEcDXwROEXSFxtU+1fg/yJid2AM8OOs4jEzM7OeI8senP2BpRHxSkR8BNwNHN+gzvHAr9Pn9wJDJSnDmMzMzKwH2CzDZe8ELCuYrgUGN1UnItZLWg1sD6wsrCTpXODcdHKtpCWZRNw6fWkQZ2NEyfM1t+t2u1y77fhd0i3X1+26Xbfbqe02tGtjhVkmOCUTEROACZ0dRyFJNRFR5Xbdrtt1u27X7brdrifLQ1RvADsXTPdPyxqtI2kzoA+wKsOYzMzMrAfIMsGZDewhqVLS5sDJwNQGdaYC30ifnwj8ISIiw5jMzMysB8jsEFV6Ts0o4HdAGTAxIhZK+k+gJiKmAr8CbpO0FHibJAnqLjrrkJnbdbtu1+26Xbfbk9stitxhYmZmZnnjOxmbmZlZ7jjBMTMzs9xxglMESRWSFnR2e5IOlrRQ0lxJW3ZUPJYNSdtK+lZnx9GRmtm2L5a0VWfE1BEkfVvSYkl/a+SO7lm1+UxHtNOgzbUd3aZZU5zgdC9fB66NiH0i4v3ODqYzpEOA5MW2QI9KcJpxMZDbBIfkc/4KcA/J0DWZi4h/7Ih2zLoqJzjF20zSHemvsHslbSVpP0nPSHpB0h8l9c6wvW8DI4D/l5bvKGlG2puzQNLBJWwbSWdImpeu222S/l7S/en0C5JKvvNMf92/2Mj7/JqkH0t6HhjejuV/WtIjafwLJJ0k6TpJi9J1/Vlab3g6/wVJM9KykZIelDRd0p8lXVWCVb4O2C39DH8q6d8lzU/bva6V6/YfSga2nSXpLkmXpbGOkVSTvp/7Sbovjf+agteelm6/cyX9V10SKemm9LULJV1dUP81SVdLej6N9wutXO/Gtu3PAk9KerKVy2pRI9vybpKeTWO/JuteB0k3A58DXiW5LcZP0/d6t4zbXZs+ZrqvaKLtakkPF0yPlzSyxG3U7S8mSXop3aaOkPR0uo3vL6mfpN+n2/Ctkl6X1LdE7Te2P3lN0k/SbeuPknYvRVsN2t2kFzT9rv9A0jmSZqfxTFERPaLdbL/RehHhvxb+gAoggAPT6YnAd4FXgP3Ssm2AzTJs7zJgEnBiWvYdYHT6vAzoXcL13Qt4CeibTm8HTAYuLmivTwe9z5cBrwHfLcHy/wW4pWB6V2AJH19NuG36OB/YqUHZSGA5yVAiWwILgKoSrO+C9PnRwDPAVnXveSuWsx8wF+gF9Ab+nL5v04Efp3UuAt4EdgS2IBk6ZXtgT+AhoDyt90vgjMIY0s97OvCldPo14ML0+beAW0v0GffNYJtqbFt+GDglnT4PWFvqdhuJ4zWS29rXf4c7oM216WNm+4pm2qwGHi4oHw+MLHFbFcB6YADJj/U56fYkknEOH0jbvTytf1S67ZVkO+OT+5M+6edc916fUfgelHi9FxRMXwb8ANi+oOyauu9oM8vpNvuNtv65B6d4yyLi6fT57cCRwPKImA0QEWsiYn2G7R3UYP5s4ExJPwAGRMS7JWz7cOCeiFgJEBFvp2U3pdMbImJ1Cdsr1NR6Ty7BsucDX1HSG3QwyZ20PwB+JelrwHtpvaeBSZLOIfmS1vl9RKyK5PDgfXzyM2mPI4D/joj3oP49L9aBwIMR8UG6HTxUMK/u5przgYURsTwiPiRJzncGhgKDgNmS5qbTn0tfM0JJr9mfSBKFwkMr96WPc0h2uK3R0rZdSo1ty0NIDhUB3Jlh211FlvuKzvZqRMyPiI3AQuCJSP6DzifZLg8iGeiZiHgM+L8Str3J/qRgn3hXweOQErbXkr0lzZQ0n+R0hr1aqN/d9hut5gSneA1vGLSmg9vbZDoiZgCHkPyTniTpjIzj6ShNrfff2r3giJeAfUm+tNcA3ycZ9f5e4J+Bx9J65wFXkHyR50javoXYurIP08eNBc/rpjcj+bX760jO69onIj4fET+QVEnya25oRHwJeITkl17D5W6g9TcM7Y7vY7fVSfuK9Wz6/6VXUxXbqeE2Xbi9ZzrWYsP9iaQr62YVVsug6abe20nAqIgYAFxN+97zrrjfaDUnOMXbRVJdNn4q8Cywo6T9ACT1VjKeVlbtzSqcKWlX4K8RcQtwK8kXrVT+AAyv+8cuaTvgCeD8dLpMUp8Stleo2fVuD0mfBd6LiNuBn5Ls9PtExDTgEuDLab3dIuK5iLgSWMHHY6p9RdJ2Sq5gO4Gkp6c93iXpGgb4Pcmv7K3SGLZrxXKeBoZJ6iVpa5JkrVhPACdK2qGu3XTb2oYkqVwt6e9JDqGVSmOfceF7UUqNbcvPkhxegI6/e3pW69mkjPcVTXkd+KKkLSRtS/ILvzM8TXLuIpL+Cfi7Ui24kf1J3ft6UsHj/5SqvQJ/BXaQtL2kLfj4+94bWC6pnKQHpyXdbb/Rat1iNPEuYglwgaSJwCJgHMnOc1z6D+99ksMMpTphsWF7N5Ec56xTDfybpHVpmyX7VRbJkBo/BJ6StIGkq/EiYIKkfyXJvs8nmy9vY+t9YYmWPYDkBM+NwDrgUuBhSb1IfpFcmtb7qaQ90rIngBeAfYA/AlNIBo69PSJq2hNMRKxSckLkAuBRkm7hGkkfAdNIepiKWc5sSVOBeSQ7v/lAUYcQI2KRpCuAxyV9iuR9uSAinpX0J+BFYBntT+YKNfYZfwQ8JunNiDisVA01sS1fDNwuaTRJr11Wh1sbczdwi5ITq0+MiJc7oM1qMtpXNCUilkn6Lcm5aq+SvO+d4WrgLkmnk+yv/pckySyFhvuT80l6g/9O0jyS3opTStRWvYhYp2TIoz+S9Mq9mM76D+A5kh9lz9FCIt0N9xut5qEarMuQVEFyUt7enR1LQ0quAKmKiFGdHUtjJG0dEWvTHqAZwLkR8Xxnx9UVpe/R+xERkk4mOeH4+M6Oy0ov7eHYEMnYiEOAmyJinwzbe41kP7EyqzZKKe/7DffgmOXDBCU3kOtFcmw8NzupDAwCxksS8A5wVueGYxnaBfht2svwEXBOJ8fT1eR6v+EeHDMzM8sdn2RsZmZmueMEx8zMzHLHCY6ZmZnljhMcMzMzyx0nONYmSgbfvFPSK5LmSPofSV8tmH+jpDfSqxfqykZKWqFkcLZF6VAIDcsXKh1kM513gKTn0nmLldxuvrF47lAyaNwCSRPTm13VDfq3On39XH18t1EzKwFJIen6gunL6r6nSgaBfEMfD/R5XCPlLyoZoLHR/0eSNhTsG16Q9J2m6poV8kZirZZeXvsAMCMiPhcRg0juCNs/nf8p4KskN3o6tMHLJ6f3oagGfpTe7bK+PCL2Irmcs+5uoL8muTfDPsDewG+bCOsO4AskN9/aEji7YN7MgluK/2ebVtrMmvIh8DU1PUr3mPT7OxyYWJCc1JV/keR723BfUef9gn3DV0jujntVqYK3/HKCY21xOPBRRNxcVxARr0fEuHSymmTgu5to4k6eEfEW8DLJiN71lAx38Wk+HhRvB5JRvOsG+VzUxPKmRYrkDp/927ZqZtZK64EJJMOdNCkiFqd1GyZCm5Pch6XFgTDT/ca5wKj0h5ZZk5zgWFvsBTR3Q6hTSEbSvR84tu5wUSFJnyMZfXZpWnSSklFp3wC24+ORbccASyTdL+mb6bAKTUrbOp104MzUkLRr+1FJLY2wa2at9wvg62pmjDpJg0kGa1yRFl2SfueXAy9FxNxiGoqIV4Aykh8/Zk1ygmPtJukXaQIxW9LmwDHAAxGxhmRMlCMLqtclMncB34yIt9PyukNXnyEZE+XfANJDSlXA4yQDMxYmLo35Jcmhs5np9PPArhHxZZLxwx5oz7qa2Sel3/XfAN9uZHZdIvMz4KT4+O6ydYeodgA+nQ6bYVYyTnCsLRZSMCJxRFxAMlpwP5JkZltgfjouy0Fsepiq7lybwRFxf8MFpzu/h0hG+q4rezkibkrb+LKSUXR/l554eGtdPUlXpTFcWvDaNRGxNn0+DShv5lwBM2u7G4F/JTnEXGhM+p0/uOCHR72IWEfyw+UQSTsXXBBwXmONpL2/G4C3Shu+5Y0THGuLPwC9JJ1fULZV+ngKcHZEVEREBVAJfKXuqqgiHURyfg6Sji041r4HyY7tnYg4Mt1pnp3WO5skuTolIjbWLUjSZ+peL2l/km1+VetW18xakvbG/pYkySla+v08EHg5IpYVXBBwcyN1+wE3A+MLeoLMGuXBNq3V0lGYTwDGSPouyTH1v5Fc2TAGOK+g7t8kzQKGtbDYkyQdRJKA1AIj0/LT03beIzlB8esRsaGR198MvA78T5rP3Jce3joROF/SeuB94GTvGM0ycz0wqsi6l0g6DSgH5pEcXm7MlukhrnKSfcBtwA3tjNN6AA+2aWZmZrnjQ1RmZmaWO05wzMzMLHec4JiZmVnuOMExMzOz3HGCY2ZmZrnjBMfMzMxyxwmOmZmZ5c7/ByPefwJmyckoAAAAAElFTkSuQmCC",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = df_gap22_ram_prob['simSeconds'].astype(float)/df_gap22_cas['simSeconds'].astype(float)\n",
-    "gap_22_ram = df_gap22_ram['simSeconds'].astype(float)/df_gap22_cas['simSeconds'].astype(float)\n",
-    "geo_meanC = df_gap22_ram_prob['simSeconds'].astype(float)/df_gap22_cas['simSeconds'].astype(float)\n",
-    "geo_meanR = df_gap22_ram['simSeconds'].astype(float)/df_gap22_cas['simSeconds'].astype(float)\n",
-    "gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "gap_22_ram[len(gap_22_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "gap_25_prob = df_gap25_ram_prob['simSeconds'].astype(float)/df_gap25_cas['simSeconds'].astype(float)\n",
-    "gap_25_ram = df_gap25_ram['simSeconds'].astype(float)/df_gap25_cas['simSeconds'].astype(float)\n",
-    "geo_meanC = df_gap25_ram_prob['simSeconds'].astype(float)/df_gap25_cas['simSeconds'].astype(float)\n",
-    "geo_meanR = df_gap25_ram['simSeconds'].astype(float)/df_gap25_cas['simSeconds'].astype(float)\n",
-    "gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "gap_25_ram[len(gap_25_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "npb_C_prob = df_npbC_ram_prob['simSeconds'].astype(float)/df_npbC_cas['simSeconds'].astype(float)\n",
-    "npb_C_ram = df_npbC_ram['simSeconds'].astype(float)/df_npbC_cas['simSeconds'].astype(float)\n",
-    "geo_meanC = df_npbC_ram_prob['simSeconds'].astype(float)/df_npbC_cas['simSeconds'].astype(float)\n",
-    "geo_meanR = df_npbC_ram['simSeconds'].astype(float)/df_npbC_cas['simSeconds'].astype(float)\n",
-    "npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "npb_C_ram[len(npb_C_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "npb_D_prob = df_npbD_ram_prob['simSeconds'].astype(float)/df_npbD_cas['simSeconds'].astype(float)\n",
-    "npb_D_ram = df_npbD_ram['simSeconds'].astype(float)/df_npbD_cas['simSeconds'].astype(float)\n",
-    "geo_mean = df_npbD_ram_prob['simSeconds'].astype(float)/df_npbD_cas['simSeconds'].astype(float)\n",
-    "geo_mean = df_npbD_ram['simSeconds'].astype(float)/df_npbD_cas['simSeconds'].astype(float)\n",
-    "npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(geo_meanC)\n",
-    "npb_D_ram[len(npb_D_ram)] = statistics.geometric_mean(geo_meanR)\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,2.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   1/gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, 1/gap_22_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   1/npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   1/npb_C_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Speedup to C.L.\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,2.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 3\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   1/gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+barWidth/2, 1/gap_25_ram[i],   width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   1/npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "    plt.bar((offset+i)*tickSize+barWidth/2,   1/npb_D_ram[i],   width=1, color=cmap(2))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Speedup to C.L.\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 35,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "gap_22_prob = 100* df_gap22_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_gap22_ram_prob['numTotMisses'].astype(float)+df_gap22_ram_prob['numTotHits'].astype(float))\n",
-    "#gap_22_prob[len(gap_22_prob)] = statistics.geometric_mean(df_gap22_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "\n",
-    "gap_25_prob = 100* df_gap25_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_gap25_ram_prob['numTotMisses'].astype(float)+df_gap25_ram_prob['numTotHits'].astype(float))\n",
-    "#gap_25_prob[len(gap_25_prob)] = statistics.geometric_mean(df_gap25_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "npb_C_prob = 100* df_npbC_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_npbC_ram_prob['numTotMisses'].astype(float)+df_npbC_ram_prob['numTotHits'].astype(float))\n",
-    "#npb_C_prob[len(npb_C_prob)] = statistics.geometric_mean(df_npbC_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "\n",
-    "\n",
-    "npb_D_prob = 100* df_npbD_ram_prob['actDelayedDueToTagAct'].astype(float)/(df_npbD_ram_prob['numTotMisses'].astype(float)+df_npbD_ram_prob['numTotHits'].astype(float))\n",
-    "#npb_D_prob[len(npb_D_prob)] = statistics.geometric_mean(df_npbD_ram_prob['actDelayedDueToTagAct'].astype(float))\n",
-    "\n",
-    "################################## \n",
-    "# Multi bar Chart1\n",
-    "app_gap = df_gap22_ram_prob['app']\n",
-    "#app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbC_ram_prob['app']\n",
-    "#app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,1.2])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_22_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_C_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"ACT delayed (%)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###############################################################################\n",
-    "# Multi bar Chart2\n",
-    "app_gap = df_gap25_ram_prob['app']\n",
-    "#app_gap[len(app_gap)] = \"gmean\"\n",
-    "\n",
-    "app_npb = df_npbD_ram_prob['app']\n",
-    "#app_npb[len(app_npb)] = \"gmean\"\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,1.2])\n",
-    "barWidth = 1\n",
-    "tickSize = 2\n",
-    "\n",
-    "\n",
-    "for i,app in enumerate(app_gap):\n",
-    "    plt.bar(i*tickSize-barWidth/2,   gap_25_prob[i],   width=barWidth, color=cmap(1), label='TDRAM-Rd-Probe' if i==0 else None)\n",
-    "offset = i+1\n",
-    "for i,app in enumerate(app_npb): \n",
-    "    plt.bar((offset+i)*tickSize-barWidth/2,   npb_D_prob[i],   width=1, color=cmap(1))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.70, -0.01, \"NPB-D\")\n",
-    "\n",
-    "brLab = np.arange(len(app_gap)+len(app_npb))\n",
-    "plt.xticks(brLab*tickSize-0.5, list(app_gap)+list(app_npb))\n",
-    "\n",
-    "plt.axvline(x=(offset-0.5)*tickSize-0.5, color='black')\n",
-    "# plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"ACT delayed (%)\")\n",
-    "plt.legend(fontsize=9, ncol=2, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### *Flush Buffer Size Sensitivity Test*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 36,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_fb_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/8/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_fb_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/8/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_fb_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/8/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_fb_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/8/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_fb_16 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/16/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_fb_16 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/16/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_fb_16 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/16/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_fb_16 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/16/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_fb_32 = df_gap22_ram\n",
-    "df_npbC_fb_32 = df_npbC_ram\n",
-    "df_gap25_fb_32 = df_gap25_ram\n",
-    "df_npbD_fb_32 = df_npbD_ram\n",
-    "\n",
-    "df_gap22_fb_64 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/64/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_fb_64 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/64/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_fb_64 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/64/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_fb_64 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/fbSensitivity/rambus/64/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 37,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# gap22_fb_8 = \n",
-    "# npbC_fb_8 = \n",
-    "# gap25_fb_8 = \n",
-    "# npbD_fb_8 = \n",
-    "# gap22_fb_16 = \n",
-    "# npbC_fb_16 = \n",
-    "# gap25_fb_16 = \n",
-    "# npbD_fb_16 = \n",
-    "# gap22_fb_32 = \n",
-    "# npbC_fb_32 = \n",
-    "# gap25_fb_32 = \n",
-    "# npbD_fb_32 = \n",
-    "# gap22_fb_64 = \n",
-    "# npbC_fb_64 = \n",
-    "# gap25_fb_64 = \n",
-    "# npbD_fb_64 = "
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### *Tag Check Timing Parameters Sensitivity Test*"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/4ns/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_8 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/4ns/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_15 = df_gap22_ram\n",
-    "df_npbC_15 = df_npbC_ram\n",
-    "df_gap25_15 = df_gap25_ram\n",
-    "df_npbD_15 = df_npbD_ram\n",
-    "\n",
-    "df_gap22_22 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/11ns/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_22 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/11ns/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_22 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/11ns/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_22 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/tagCheckSensitivity/rambus/11ns/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tagChk_8clk = []\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_gap22_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_gap25_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_npbC_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_npbD_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "\n",
-    "\n",
-    "tagChk_15clk = []\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_gap22_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_gap25_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_npbC_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_npbD_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "\n",
-    "tagChk_22clk = []\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_gap22_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_gap25_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_npbC_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_npbD_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "\n",
-    "exeTime_8clk = []\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_gap22_8['simSeconds'].astype(float))/statistics.geometric_mean(df_gap22_orc['simSeconds'].astype(float)))\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_gap25_8['simSeconds'].astype(float))/statistics.geometric_mean(df_gap25_orc['simSeconds'].astype(float)))\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_npbC_8['simSeconds'].astype(float))/statistics.geometric_mean(df_npbC_orc['simSeconds'].astype(float)))\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_npbD_8['simSeconds'].astype(float))/statistics.geometric_mean(df_npbD_orc['simSeconds'].astype(float)))\n",
-    "\n",
-    "exeTime_15clk = []\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_gap22_15['simSeconds'].astype(float))/statistics.geometric_mean(df_gap22_orc['simSeconds'].astype(float)))\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_gap25_15['simSeconds'].astype(float))/statistics.geometric_mean(df_gap25_orc['simSeconds'].astype(float)))\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_npbC_15['simSeconds'].astype(float))/statistics.geometric_mean(df_npbC_orc['simSeconds'].astype(float)))\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_npbD_15['simSeconds'].astype(float))/statistics.geometric_mean(df_npbD_orc['simSeconds'].astype(float)))\n",
-    "\n",
-    "exeTime_22clk = []\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_gap22_22['simSeconds'].astype(float))/statistics.geometric_mean(df_gap22_orc['simSeconds'].astype(float)))\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_gap25_22['simSeconds'].astype(float))/statistics.geometric_mean(df_gap25_orc['simSeconds'].astype(float)))\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_npbC_22['simSeconds'].astype(float))/statistics.geometric_mean(df_npbC_orc['simSeconds'].astype(float)))\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_npbD_22['simSeconds'].astype(float))/statistics.geometric_mean(df_npbD_orc['simSeconds'].astype(float)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "suites = ['GAPBS-22', 'NPB-C', 'GAPBS-25', 'NPB-D']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "#plt.ylim([0,100])\n",
-    "\n",
-    "for i,app in enumerate(suites):\n",
-    "    plt.bar(i*4,   exeTime_8clk[i],   width=1, color=cmap3(0), label='8 cycles' if i==0 else None)\n",
-    "    plt.bar(i*4+1, exeTime_15clk[i],  width=1, color=cmap3(1), label='15 cycles' if i==0 else None)\n",
-    "    plt.bar(i*4+2, exeTime_22clk[i],  width=1, color=cmap3(2), label='22 cycles' if i==0 else None)\n",
-    "\n",
-    "plt.xticks(np.arange(4)*4+1, list(suites))\n",
-    "plt.ylabel(\"Normalized Execution Time\\n(to ideal cache)\")\n",
-    "plt.legend(fontsize=9, ncol=3, loc=(0,1))\n",
-    "plt.axhline(y=1, color='gray')\n",
-    "\n",
-    "axB = plt.twinx()\n",
-    "for i,app in enumerate(suites):\n",
-    "    axB.scatter(i*4,   tagChk_8clk[i], color='black')\n",
-    "    axB.scatter(i*4+1, tagChk_15clk[i], color='black')\n",
-    "    axB.scatter(i*4+2, tagChk_22clk[i], color='black')\n",
-    "plt.figtext(0.98, 0.15, \"Tag Check Latency (ns)\", rotation=-90)\n",
-    "plt.ylim([0,100])\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"figures/tagCheckSensitivity.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x144 with 2 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tagChk_8clk = []\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_gap22_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_gap25_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_npbC_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_8clk.append(statistics.geometric_mean(df_npbD_8['avgTimeTagCheckResRd'].astype(float)))\n",
-    "\n",
-    "\n",
-    "tagChk_15clk = []\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_gap22_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_gap25_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_npbC_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_15clk.append(statistics.geometric_mean(df_npbD_15['avgTimeTagCheckResRd'].astype(float)))\n",
-    "\n",
-    "tagChk_22clk = []\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_gap22_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_gap25_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_npbC_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "tagChk_22clk.append(statistics.geometric_mean(df_npbD_22['avgTimeTagCheckResRd'].astype(float)))\n",
-    "\n",
-    "exeTime_8clk = []\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_gap22_8['simSeconds'].astype(float))/statistics.geometric_mean(df_gap22_15['simSeconds'].astype(float)))\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_gap25_8['simSeconds'].astype(float))/statistics.geometric_mean(df_gap25_15['simSeconds'].astype(float)))\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_npbC_8['simSeconds'].astype(float))/statistics.geometric_mean(df_npbC_15['simSeconds'].astype(float)))\n",
-    "exeTime_8clk.append(statistics.geometric_mean(df_npbD_8['simSeconds'].astype(float))/statistics.geometric_mean(df_npbD_15['simSeconds'].astype(float)))\n",
-    "\n",
-    "exeTime_15clk = []\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_gap22_15['simSeconds'].astype(float))/statistics.geometric_mean(df_gap22_15['simSeconds'].astype(float)))\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_gap25_15['simSeconds'].astype(float))/statistics.geometric_mean(df_gap25_15['simSeconds'].astype(float)))\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_npbC_15['simSeconds'].astype(float))/statistics.geometric_mean(df_npbC_15['simSeconds'].astype(float)))\n",
-    "exeTime_15clk.append(statistics.geometric_mean(df_npbD_15['simSeconds'].astype(float))/statistics.geometric_mean(df_npbD_15['simSeconds'].astype(float)))\n",
-    "\n",
-    "exeTime_22clk = []\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_gap22_22['simSeconds'].astype(float))/statistics.geometric_mean(df_gap22_15['simSeconds'].astype(float)))\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_gap25_22['simSeconds'].astype(float))/statistics.geometric_mean(df_gap25_15['simSeconds'].astype(float)))\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_npbC_22['simSeconds'].astype(float))/statistics.geometric_mean(df_npbC_15['simSeconds'].astype(float)))\n",
-    "exeTime_22clk.append(statistics.geometric_mean(df_npbD_22['simSeconds'].astype(float))/statistics.geometric_mean(df_npbD_15['simSeconds'].astype(float)))\n",
-    "\n",
-    "\n",
-    "\n",
-    "suites = ['GAPBS-22', 'NPB-C', 'GAPBS-25', 'NPB-D']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,2)\n",
-    "#plt.ylim([0,100])\n",
-    "\n",
-    "for i,app in enumerate(suites):\n",
-    "    plt.bar(i*4,   exeTime_8clk[i],   width=1, color=cmap3(0), label='8 cycles' if i==0 else None)\n",
-    "    plt.bar(i*4+1, exeTime_15clk[i],  width=1, color=cmap3(1), label='15 cycles' if i==0 else None)\n",
-    "    plt.bar(i*4+2, exeTime_22clk[i],  width=1, color=cmap3(2), label='22 cycles' if i==0 else None)\n",
-    "\n",
-    "plt.xticks(np.arange(4)*4+1, list(suites))\n",
-    "plt.ylabel(\"Normalized Execution Time\\n(to baseline TDRAM)\")\n",
-    "plt.legend(fontsize=9, ncol=3, loc=(0,1))\n",
-    "plt.axhline(y=1, color='gray')\n",
-    "\n",
-    "\n",
-    "axB = plt.twinx()\n",
-    "for i,app in enumerate(suites):\n",
-    "    axB.scatter(i*4,   tagChk_8clk[i], color='black')\n",
-    "    axB.scatter(i*4+1, tagChk_15clk[i], color='black')\n",
-    "    axB.scatter(i*4+2, tagChk_22clk[i], color='black')\n",
-    "plt.figtext(0.98, 0.15, \"Tag Check Latency (ns)\", rotation=-90)\n",
-    "plt.ylim([0,100])\n",
-    "plt.tight_layout()\n",
-    "plt.savefig(\"figures/tagCheckSensitivity2.pdf\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Link Latency"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 46,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_gap22_noDC_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_noDC_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/noDC/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_noDC_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_noDC_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/noDC/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_cas_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_cas_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/cascade/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_cas_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/cascade/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_cas_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/cascade/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ram_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ram_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambus/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_ram_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ram_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambus/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ramOpt_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambusTagPr/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ramOpt_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambusTagPr/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_ramOpt_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambusTagPr/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ramOpt_50 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/50/rambusTagPr/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "#################\n",
-    "df_gap22_noDC_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_noDC_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/noDC/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_noDC_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_noDC_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/noDC/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_cas_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_cas_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/cascade/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_cas_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/cascade/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_cas_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/cascade/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ram_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ram_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambus/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_ram_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ram_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambus/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ramOpt_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambusTagPr/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ramOpt_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambusTagPr/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_ramOpt_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambusTagPr/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ramOpt_100 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/100/rambusTagPr/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "#################\n",
-    "df_gap22_noDC_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_noDC_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/noDC/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_noDC_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_noDC_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/noDC/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_cas_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/cascade/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_cas_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/cascade/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_cas_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_cas_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/cascade/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ram_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ram_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambus/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_ram_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ram_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambus/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "\n",
-    "df_gap22_ramOpt_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambusTagPr/1GB_8GB_g22_nC/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbC_ramOpt_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambusTagPr/1GB_8GB_g22_nC/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "df_gap25_ramOpt_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambusTagPr/1GB_85GB_g25_nD/GAPBS\", \"GAPBS\", [1,1,1,1,1,1])\n",
-    "df_npbD_ramOpt_250 = creatDataFrame(\"/home/babaie/projects/rambusDesign/1gigDRAMCache/dramCacheController/newResults/link/250/rambusTagPr/1GB_85GB_g25_nD/NPB\", \"NPB\",[1,1,1,1,1,1,1,1])\n",
-    "#################"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 71,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[0.04290200016773939, 0.05149472383647565, 0.06786525304623896]\n",
-      "[0.04290200016773939, 0.05149472383647565, 0.06786525304623896]\n"
-     ]
-    }
-   ],
-   "source": [
-    "gap22_noDC = []\n",
-    "gap22_noDC.append(statistics.geometric_mean(df_gap22_noDC_50['simSeconds'].astype(float)))\n",
-    "gap22_noDC.append(statistics.geometric_mean(df_gap22_noDC_100['simSeconds'].astype(float)))\n",
-    "gap22_noDC.append(statistics.geometric_mean(df_gap22_noDC_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbC_noDC = []\n",
-    "npbC_noDC.append(statistics.geometric_mean(df_npbC_noDC_50['simSeconds'].astype(float)))\n",
-    "npbC_noDC.append(statistics.geometric_mean(df_npbC_noDC_100['simSeconds'].astype(float)))\n",
-    "npbC_noDC.append(statistics.geometric_mean(df_npbC_noDC_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "gap25_noDC = []\n",
-    "gap25_noDC.append(statistics.geometric_mean(df_gap25_noDC_50['simSeconds'].astype(float)))\n",
-    "gap25_noDC.append(statistics.geometric_mean(df_gap25_noDC_100['simSeconds'].astype(float)))\n",
-    "gap25_noDC.append(statistics.geometric_mean(df_gap25_noDC_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbD_noDC = []\n",
-    "npbD_noDC.append(statistics.geometric_mean(df_npbD_noDC_50['simSeconds'].astype(float)))\n",
-    "npbD_noDC.append(statistics.geometric_mean(df_npbD_noDC_100['simSeconds'].astype(float)))\n",
-    "npbD_noDC.append(statistics.geometric_mean(df_npbD_noDC_250['simSeconds'].astype(float)))\n",
-    "######################################\n",
-    "gap22_cas = []\n",
-    "gap22_cas.append(statistics.geometric_mean(df_gap22_cas_50['simSeconds'].astype(float)))\n",
-    "gap22_cas.append(statistics.geometric_mean(df_gap22_cas_100['simSeconds'].astype(float)))\n",
-    "gap22_cas.append(statistics.geometric_mean(df_gap22_cas_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbC_cas = []\n",
-    "npbC_cas.append(statistics.geometric_mean(df_npbC_cas_50['simSeconds'].astype(float)))\n",
-    "npbC_cas.append(statistics.geometric_mean(df_npbC_cas_100['simSeconds'].astype(float)))\n",
-    "npbC_cas.append(statistics.geometric_mean(df_npbC_cas_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "gap25_cas = []\n",
-    "gap25_cas.append(statistics.geometric_mean(df_gap25_cas_50['simSeconds'].astype(float)))\n",
-    "gap25_cas.append(statistics.geometric_mean(df_gap25_cas_100['simSeconds'].astype(float)))\n",
-    "gap25_cas.append(statistics.geometric_mean(df_gap25_cas_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbD_cas = []\n",
-    "npbD_cas.append(statistics.geometric_mean(df_npbD_cas_50['simSeconds'].astype(float)))\n",
-    "npbD_cas.append(statistics.geometric_mean(df_npbD_cas_100['simSeconds'].astype(float)))\n",
-    "npbD_cas.append(statistics.geometric_mean(df_npbD_cas_250['simSeconds'].astype(float)))\n",
-    "######################################\n",
-    "gap22_ram = []\n",
-    "gap22_ram.append(statistics.geometric_mean(df_gap22_ram_50['simSeconds'].astype(float)))\n",
-    "gap22_ram.append(statistics.geometric_mean(df_gap22_ram_100['simSeconds'].astype(float)))\n",
-    "gap22_ram.append(statistics.geometric_mean(df_gap22_ram_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbC_ram = []\n",
-    "npbC_ram.append(statistics.geometric_mean(df_npbC_ram_50['simSeconds'].astype(float)))\n",
-    "npbC_ram.append(statistics.geometric_mean(df_npbC_ram_100['simSeconds'].astype(float)))\n",
-    "npbC_ram.append(statistics.geometric_mean(df_npbC_ram_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "gap25_ram = []\n",
-    "gap25_ram.append(statistics.geometric_mean(df_gap25_ram_50['simSeconds'].astype(float)))\n",
-    "gap25_ram.append(statistics.geometric_mean(df_gap25_ram_100['simSeconds'].astype(float)))\n",
-    "gap25_ram.append(statistics.geometric_mean(df_gap25_ram_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbD_ram = []\n",
-    "npbD_ram.append(statistics.geometric_mean(df_npbD_ram_50['simSeconds'].astype(float)))\n",
-    "npbD_ram.append(statistics.geometric_mean(df_npbD_ram_100['simSeconds'].astype(float)))\n",
-    "npbD_ram.append(statistics.geometric_mean(df_npbD_ram_250['simSeconds'].astype(float)))\n",
-    "######################################\n",
-    "gap22_ramOpt = []\n",
-    "gap22_ramOpt.append(statistics.geometric_mean(df_gap22_ramOpt_50['simSeconds'].astype(float)))\n",
-    "gap22_ramOpt.append(statistics.geometric_mean(df_gap22_ramOpt_100['simSeconds'].astype(float)))\n",
-    "gap22_ramOpt.append(statistics.geometric_mean(df_gap22_ramOpt_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbC_ramOpt = []\n",
-    "npbC_ramOpt.append(statistics.geometric_mean(df_npbC_ramOpt_50['simSeconds'].astype(float)))\n",
-    "npbC_ramOpt.append(statistics.geometric_mean(df_npbC_ramOpt_100['simSeconds'].astype(float)))\n",
-    "npbC_ramOpt.append(statistics.geometric_mean(df_npbC_ramOpt_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "gap25_ramOpt = []\n",
-    "gap25_ramOpt.append(statistics.geometric_mean(df_gap25_ramOpt_50['simSeconds'].astype(float)))\n",
-    "gap25_ramOpt.append(statistics.geometric_mean(df_gap25_ramOpt_100['simSeconds'].astype(float)))\n",
-    "gap25_ramOpt.append(statistics.geometric_mean(df_gap25_ramOpt_250['simSeconds'].astype(float)))\n",
-    "\n",
-    "npbD_ramOpt = []\n",
-    "npbD_ramOpt.append(statistics.geometric_mean(df_npbD_ramOpt_50['simSeconds'].astype(float)))\n",
-    "npbD_ramOpt.append(statistics.geometric_mean(df_npbD_ramOpt_100['simSeconds'].astype(float)))\n",
-    "npbD_ramOpt.append(statistics.geometric_mean(df_npbD_ramOpt_250['simSeconds'].astype(float)))\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 93,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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WduzYQWFhIbNmzeLee+/l/PPPLy62WSS22GZRUgFBsc3MzEyuvvrqMvuGoHBm8+bNDyrGuWTJEjIzM8nMzGTXrl0A/OxnP6NHjx7s3r27uM8uXbpgZmzYsIF27dpx9NFHc/TRR9OuXTs++eQTIEh2ANLT08nPz0dERKSmiDdMPO6ke+7+fvLDSR1VWWyztL5/+9vfUrduXfLy8mjatClLly6lVatWdO/e/VvT20+cOJHu3bsf1FZ0k3KLFi1YuXJlcQHNVatW0aJFC3Jzc1m+fDkA//znPznxxBMruKdERESSL94lqmXAamBbuBxbRtqBb1eRlGoptlla37/85S95+OGH6d+/P6eccgp169at0Ps5+eST+e///u/iBGj06NE0bNgQgOOPP56srCw+//xzxo8fX6H+RUREolBmsU0zuxEYRDCT8UzgVXffVXWhlU7FNg9P+jeSZFKxTREpcsjFNt19grt3B35GMJT7bTN7wczaRxemiIiISOWVe5Oxu28AZgFvAp2A06MOSkRERKQy4t1kfBowlKBe1EaCy1T3u/vuKortkLh78SzAUrOUdRlUREQkKvFuMl4PrCQ4e7OTYP6a64qSiDiT91W5Y489lu3bt1O/fn0lOTXQnj17iuf2ERERqQrxEpxfE4yWAjihxLoa9ZW8adOm5OXlsXXr1uoORcrQuHHj6g5BRESOIPGqid9d1jozOzeSaCqodu3atGjRorrDEBERkRoi4VINZnYWcGn4swP41pAsERERkZogboJjZul8k9QUAKcCHd3908gjExEREamgMoeJm9nfgDkESdB/ufs5wFdKbkRERKSmizcPzr+AukAjoGHYVqNuLhYREREpTbyZjC8E2gHLgbvN7BPgO2bWqYpiExEREamQuPfguHs+8Hvg92Z2MnAJMN7Mmrt7s6oIUERERORQlVuqoYi7b3H3icCPgd9FF5KIiIhI5cS7ybiZmU0xsz+b2dVmVsfMHgbW8c09OSIiIiI1TrxLVH8EFgIvA+cDy4AVwNnu/kX0oYmIiIhUTLwE57sxsxm/YWaDgZ+4e2H0YYmIiIhUXHkT/X0HKKpeuR2oZ2E1S3f/MuLYRERERCokXoJTj2CIeGx57vfD3w6cFlVQIiIiIpURr9hmehXGISIiIpI05V2iqgX0A74fNuUCb7j7/qgDExEREamoeMPEmwBrgJ8DpwBNgF8Aa8zslKoJT0REROTQxTuDcx/wpLtPiG00s+uBccCVEcYlIiIiUmHxEpwu7j68ZKO7P2Zm66ILSURERKRy4pVq2B1n3dfldWxm08xsi5mtLmN9ppnlm9mK8GdseX2KiIiIJCLuMHEzu7iUdgNOTKDv6cDjBDMil2Wxu/84gb5EREREEhYvwVkI9C9j3aLyOnb3RWaWXpGgRERERCoj3jw4I6rg9bua2QfA58At7r6mtI3MbCQwEqB58+ZVEJaIiIgczuLdgxO194FT3f0HwETgtbI2dPcp7t7R3Ts2bKhC5iIiIhJftSU47r7T3XeFj+cCtc2sQXXFIyIiIqmj2hIcM/teUeFOM+sUxrK9uuIRERGR1BG3VAOAmdUGrgPOC5sWApPdvaCc5z0HZAINzCwPuAuoDeDuk4FBwHVmtp9gSPpQd/cKvg8RqQa/G5iT9D5HzRqW9D5F5MhTboIDPEmQmDwRLl8etl0d70nufmk56x8nGEYuIiIiNUSyv7hU15eWRBKcc8MbgYu8E458EhEREamREklwDphZS3f/GMDMTgMORBuWiERhwGtZSe0vi7gnakUkYsk+piF1jutEEpxbgflmtoFgFuNTgaqYI0dERESkQspNcNz9bTNrDZwRNq1z973RhiUiIiJScYmcwQE4B0gPt29vZrh7vBpTIiIiItUmkWHizwAtgRV8c++NE7+IpoiIiEi1SeQMTkfgLM1RIyIiIoeLRGYyXg18L+pARERERJIlkTM4DYBcM/sHUHxzsbsPiCwqERERkUpIJMG5O+ogRERERJIpkWHiC6siEBEREZFkSXSYuIhUsS53vZH0Pk/OSHqXIiI1UiI3GYuIiIgcVhI6g2NmRwOnh4vr3L0gupBEREREKieRif4ygT8AnxLUompmZle6+6JIIxMRERGpoETO4DwM/Ke7rwMws9OB5wjKN4iIiIjUOIncg1O7KLkBcPePgNrRhSQiIiJSOYmcwVlmZk8BOeHyT4Bl0YUkIiKSmpI9OlIjI8uWSIJzHZANXB8uLwYmRRaRSBX43cCc8jc6RKNmDUt6nyIiUjGJJDjXuvsjwCNFDWZ2A/BoZFGJiIiIVEIi9+BcWUrb8CTHISIiIpI0ZZ7BMbNLgcuAFmY2O2ZVXeDLqAMTiTXgtayk9pfFpUntT0REapZ4l6jeBTYTVBN/OKb9K2BllEGJiIiIVEaZCY67fwZ8BnStunBEREQqJtmDBzRw4PCmWlQiIiKSciJLcMxsmpltMbPVZaw3M3vMzNab2Uoz6xBVLCIiInJkOZRim98HnKDY5r4EnjYdeBz4Yxnr+wGtw5/OwJPhbxERSXHJHjgAGjwgBzN3j7+BWRYwGfiYoNhmC2CUu79ebudm6cCf3b1tKet+Byxw9+fC5XVAprtvjtdnixYt/K677irvpSXFrN62Kqn9ffeLk5PaH8ApbRsltb/3P03+YMWjT9iU1P6qaz+uWLECgPbt2yf99aVqJPuYhuT/PSb7mIbkH9fJPqbh8NiPsUaMGLHc3TuWbE+02GZPd18PYGYtgTlAuQlOOZoAG2OW88K2byU4ZjYSGAnQpEmTSr7s4e/z1f9Kan9R//GJiIhUtUQSnK+KkpvQBoKh4lXG3acAUwA6duzow4cPr8qXr3GSPVJg+EM1f6RA0ufBWZz8Ai7J3o+Tk1yzBuDkVm8ntb/q2o+ZmZkATJgwIemvL1UjkktUSf57jOKzMdnHdbKPaTg89mOsESNGlNqeaLHNucALBPfgDAaWmtnFAO7+SgVj2gQ0i1luGraJiIiIVEoiCc6xwL+AHuHyVuA4oD9BwlPRBGc2MNrMZhLcXJxf3v03hyPdSCciIlL1yk1w3L30cz/lMLPngEyggZnlAXcBtcM+JwNzgQuA9cDXQIVeR2qmLsk+DZv8KyEiIpLCyk1wzOz3BGdqDuLuV8V7nrvHPc3gwfCt7PJeX0Qk1WkGXpHkS+QS1Z9jHh8LXAR8Hk04IiIiIpWXyCWql2OXw0tPSyKLSERERKSSEprJuITWQPJn9xIROUwkfdoCDRwQSbpE7sH5iuAeHAt/fwHcFnFcIiIiIhWWyCWqulURiIiIiEiylJnglFfd293fT344IiIiIpUX7wzOw+HvY4GOwAcEl6nOBpYBXaMNTUREagrNbSWHm6PKWuHuPd29J0Hxyw7u3tHdzwEyUEkFERERqcESGUV1hrsX17V399VmdmaEMVUrfUsRERE5/CWS4Kw0s6eAoqk2fwKsjC4kERERkcpJJMEZAVwH3BAuLwKejCwiERERkUpKZJj4HjObDMx193VVEJOIiIhIpZR5k3ERMxsArADmhcvtzWx2xHGJiIiIVFi5CQ5wF9AJ2AHg7iuAFtGFJCIiIlI5idyDU+Du+WYW2+YRxSMiklTJHhkJGh0pcjhIJMFZY2aXAWlm1hq4Hng32rBEREREKi6RS1Q/A9oAe4FngXzgxghjEhEREamUREZRfQ3cYWb3hY9FREREarRERlH9h5nlAh+Gyz8wsycij0xERESkghK5RDUe6AtsB3D3D4DzogxKREREpDISSXBw940lmg5EEIuIiIhIUiQyimqjmf0H4GZWm6Bkw9powxIRERGpuETO4FwLZANNgM+B9uGyiIiISI2UyCiqbQQVxEVEREQOC4mMojrNzP5kZlvNbIuZzTKz06oiOBEREZGKSOQS1bPAC0Bj4BTgReC5KIMSERERqYxEEpzj3f0Zd98f/uQAxybSuZmdb2brzGy9md1eyvrh4ZmhFeHP1Yf6BkRERERKSmQU1ethcjKToMjmJcBcM/sugLt/WdqTzCwNmAT8CMgDlprZbHfPLbHp8+4+uqJvQERERKSkRBKcIeHvUSXahxIkPGXdj9MJWO/uGwDMbCYwECiZ4IiIiIgkVSKjqFpUsO8mQOwEgXlA51K2+y8zOw/4CLiplEkFMbORwEiA5s2bVzAcEREROVKUeQ+OmZ1rZt+LWb4iHEH1WNHlqST4E5Du7mcDbwF/KG0jd5/i7h3dvWPDhg2T9NIiIiKSquLdZPw7YB9AeIblAeCPQD4wJYG+NwHNYpabhm3F3H27u+8NF58CzkksbBEREZGyxUtw0mJuIL4EmOLuL7v7r4BWCfS9FGhtZi3M7GiCe3Zmx25gZo1jFgegEhAiIiKSBPHuwUkzs1ruvh/oTXgPTALPA8Dd95vZaOANIA2Y5u5rzOzXwDJ3nw1cb2YDgP3Al8DwCr4PERERkWLxEpXngIVmtg3YDSwGMLNWBJepyuXuc4G5JdrGxjweA4w5xJhFRERE4iozwXH3+8zsbYIZjN90dw9XHQX8rCqCExEREamIuJea3P3vpbR9FF04IiIiIpWXSKkGERERkcOKEhwRERFJOUpwREREJOUowREREZGUowRHREREUo4SHBEREUk5SnBEREQk5SjBERERkZSjBEdERERSjhIcERERSTlKcERERCTlKMERERGRlKMER0RERFKOEhwRERFJOUpwREREJOUowREREZGUowRHREREUo4SHBEREUk5SnBEREQk5SjBERERkZSjBEdERERSjhIcERERSTlKcERERCTlKMERERGRlBNpgmNm55vZOjNbb2a3l7L+GDN7Plz/npmlRxmPiIiIHBkiS3DMLA2YBPQDzgIuNbOzSmz2U+D/3L0VMB54MKp4RERE5MgR5RmcTsB6d9/g7vuAmcDAEtsMBP4QPn4J6G1mFmFMIiIicgQwd4+mY7NBwPnufnW4fDnQ2d1Hx2yzOtwmL1z+ONxmW4m+RgIjw8UzgHWRBF29GgDbyt1KyqP9mBzaj8mh/Zgc2o+Vl8r78FR3b1iysVZ1RHKo3H0KMKW644iSmS1z947VHcfhTvsxObQfk0P7MTm0HyvvSNyHUV6i2gQ0i1luGraVuo2Z1QLqAdsjjElERESOAFEmOEuB1mbWwsyOBoYCs0tsMxu4Mnw8CHjHo7pmJiIiIkeMyC5Ruft+MxsNvAGkAdPcfY2Z/RpY5u6zgaeBZ8xsPfAlQRJ0pErpS3BVSPsxObQfk0P7MTm0HyvviNuHkd1kLCIiIlJdNJOxiIiIpBwlOCIiIpJylOBEzMw+NbNVZrbCzJaFbd81s7fM7P+Fv79T3XHWNGY2zcy2hHMlFbWVut8s8FhY8mOlmXWovshrDjNrZmbzzSzXzNaY2Q1h+91mtin8m1xhZhfEPGdMuB/XmVnf6ou+5tIxXTE6ppNDx3XilOBUjZ7u3j5mDoLbgbfdvTXwdrgsB5sOnF+iraz91g9oHf6MBJ6sohhruv3Az939LKALkB1TLmV8+DfZ3t3nAoTrhgJtCPb9E2HJFfk2HdOHbjo6ppNBx3WClOBUj9gSFX8ALoTiDHyamS0wsw1mdn3YXsfM5pjZB2a22swuqZ6wq467LyIYWRer1P0Wtv/RA38HTjKzxmaWGe7Ll8zsQzObUVQKxMweCL8BrTSzh6rgLVU5d9/s7u+Hj78C1gJN4jxlIDDT3fe6+yfAeqCTmaWb2Vozmxp+Y3zTzI4DMLPrY/bjzKjfUw2mY7ocOqaTQ8d14pTgRM+BN81suQUlJwAaufvm8PEXQKOY7b8P9CWo5XWXmdUmyLo/d/cfuHtbYF4VxV7TlLXfmgAbY7bL45sDPgO4kaDg62lANzOrD1wEtHH3s4F7I4672plZOsG+eC9sGh1+eE2LuZwSbz+2Bia5extgB/BfYfvtQEa4H6+N7h3UKDqmk0fHdCXouI5PCU70urt7B4JTrtlmdl7synBiw9ix+nPCTHsbsIXggF8F/MjMHjSzH7p7flUFX1OVst/K8g93z3P3QmAFkA7kA3uAp83sYuDrqOKsCczsBOBl4EZ330lwur8l0B7YDDycQDefuPuK8PFygv0IsBKYYWbDCE6dHwl0TEdAx/Sh0XFdPiU4EXP3TeHvLcCrBN/i/mVmjQHC31tinrI35vEBoJa7fwR0IPhQvNfMxlZF7DVQWfstXlmQ0vbnfoJ/h5eAH5PC357DswUvAzPc/RUAd/+Xux8I/4OYSrAv4BD3Y/g4C5hE8Pe51IKSKylNx3RS6ZiuAB3XiVGCE6HwOnvdosfAfwKrObhExZXArHL6OQX42t1zgP8h+KM7EpW132YDV1igC5Afc9r7W8JvPvXCm/BuAn4QYczVJrw34Wlgrbs/EtPeOGaziwj+JiHYj0PN7Bgza0Fw+vofcfo/Cmjm7vOB2whqyZ2Q3HdRs+iYTjod04dIx3XiDsus7DDSCHg1vAeuFvCsu88zs6XAC2b2U+AzYEg5/bQD/sfMCoEC4LoIY64RzOw5IBNoYGZ5wF3AA5S+3+YCFxDcPPc1MKKc7usCs8zsWMCAm5P+BmqGbsDlwCozWxG2/RK41MzaE1wO+BQYBRCWUnkByCU4LZ3t7gfCv9/SpAE5ZlaPYD8+5u47InknNYeO6QrSMZ00Oq4TpFINIiIiknJ0iUpERERSjhIcERERSTlKcERERCTlKMERERGRlKMER0RERFKOEhwRERFJOUpwpELMrJGZPWtBAcHlZvY3M7soZv0EM9sUThpV1DbczLaa2YqwkNs1pbSvsaCQ3vHhui5m9l64bq2Z3V1GPDPMbJ0FhQunhTN9YmY/CWuzrDKzd80sZScAE6kOZuZm9nDM8i1Fx6kFxUY3hcfvajMbUEr7h2b2ZOxnRYn+v2dmM83s4/CzZq6ZnV4lb04Oa0pw5JCFM2m+Bixy99Pc/RxgKMEU4EUzYV5EUOCtR4mnP+/u7Qkm/LrfzBrFtodF3/YBRdWV/wCMDJ/TFnihjLBmEBQ1bAccB1wdtn8C9HD3dsBvgCkVe9ciUoa9wMVm1qCM9ePD43cwMC0mkSlqP4vguC35WVH0WfMqsMDdW4afNWM4uJipSKmU4EhF9AL2ufvkogZ3/8zdJ4aLmcAaguJvl5bWQVjH52Pg1Nj2sOZJHeD/wqaTCQrHEdZZyS2jv7keIpiGvGnY/q67F/X196J2EUma/QRfHG6Kt5G7rw23LZkIHQ0cyzfHfKyeQEGJz5oP3H1xpSKWI4ISHKmINsD7cdZfCjxH8M0rq+hyUSwzOw04jWAqdoBLwmnHNwHfBf4Uto8H1pnZq2Y2KpyKvUzha11O6cX2fgq8Hu/5IlIhk4CfhNP7l8rMOgOFwNaw6abwmN8MfBRT1TpWW4Iq1yKHTAmOVJqZTTKzD8xsqZkdTVBD5jV33wm8B/SN2bwokXkOGOXuX4btRZeuvkdQYflWAHf/NdAReBO4jPKrBD9BcOnsoG94ZtaTIMG5rcJvVERKFR7rfwSuL2V1USLzEHCJf1MfqOgS1clAHTMbWhWxypFDCY5UxBpiqh+7ezbQG2hIkMycRFAI7lOgOwdfpiq616azu79asuPww+9PwHkxbR+7+5Pha/zAzOqb2RvhDYpPFW1nZneFMRxUaM/MzgaeAga6+/ZKvXMRKcsEgi8RdUq0jw+P+R+WdmnJ3QsIvricZ2bNwuN6hZldS/BZc07UgUtqUoIjFfEOcKyZxVZAPj78fSlwtbunu3s60AL4UdGoqAR1J7g/BzPLsm/K3rYGDgA73L1v+KF5dbjd1QTJ1aXuXljUkZk1B14BLnf3jw71jYpIYsKzsS8QJDkJC4/vbsDH7r4xPK7bh/fdvAMcY2YjY7Y/28x+mMzYJTUpwZFDFp5luRDoYWafmNk/CEY73QWcD8yJ2fbfwBKgfzndXhJ+a1sJZBCMeILgfpp14SnuZ4CfuPuBUp4/mWBkxd/CfsaG7WOB+sATYfuyQ37DIpKoh/n2TcRlKbp0tRpII7i8fJDws+YioE84THwNMA74IjnhSiqzby6HioiIiKQGncERERGRlKMER0RERFKOEhwRERFJOUpwREREJOUowREREZGUowRHREREUo4SHBEREUk5/x8ACjNM4siXlQAAAABJRU5ErkJggg==",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "image/png": 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",
-      "text/plain": [
-       "<Figure size 576x216 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Multi bar Chart1\n",
-    "latencies = ['50ns','100ns','250ns']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,1.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 4\n",
-    "\n",
-    "for i,lat in enumerate(latencies):\n",
-    "    plt.bar(i*tickSize,   gap22_noDC[i]/gap22_cas[i],     width=barWidth, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+1, gap22_noDC[i]/gap22_ram[i],     width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+2, gap22_noDC[i]/gap22_ramOpt[i],  width=barWidth, color=cmap(3), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "offset = i*tickSize+3\n",
-    "for i,lat in enumerate(latencies): \n",
-    "    plt.bar(offset+i*tickSize+1, npbC_noDC[i]/npbC_cas[i],     width=barWidth, color=cmap(1))\n",
-    "    plt.bar(offset+i*tickSize+2, npbC_noDC[i]/npbC_ram[i],     width=barWidth, color=cmap(2))\n",
-    "    plt.bar(offset+i*tickSize+3, npbC_noDC[i]/npbC_ramOpt[i],  width=barWidth, color=cmap(3))\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-22\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-C\")\n",
-    "\n",
-    "latencies = ['50ns','100ns','250ns','50ns','100ns','250ns']\n",
-    "brLab = np.arange(len(latencies))\n",
-    "plt.xticks(brLab*tickSize+1, latencies)\n",
-    "\n",
-    "plt.axvline(x=11, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Speedup to no DRAM $\")\n",
-    "plt.legend(fontsize=9, ncol=1, loc='upper left')\n",
-    "plt.tight_layout()\n",
-    "\n",
-    "###########################################################\n",
-    "\n",
-    "# Multi bar Chart1\n",
-    "latencies = ['50ns','100ns','250ns']\n",
-    "\n",
-    "fig = plt.figure()\n",
-    "fig.set_size_inches(8,3)\n",
-    "plt.ylim([0,1.5])\n",
-    "barWidth = 1\n",
-    "tickSize = 4\n",
-    "\n",
-    "for i,lat in enumerate(latencies):\n",
-    "    plt.bar(i*tickSize,   gap25_noDC[i]/gap25_cas[i],     width=barWidth, color=cmap(1), label='Cascade Lake' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+1, gap25_noDC[i]/gap25_ram[i],     width=barWidth, color=cmap(2), label='TDRAM-baseline' if i==0 else None)\n",
-    "    plt.bar(i*tickSize+2, gap25_noDC[i]/gap25_ramOpt[i],  width=barWidth, color=cmap(3), label='TDRAM-Rd-Prob' if i==0 else None)\n",
-    "offset = i*tickSize+3\n",
-    "for i,lat in enumerate(latencies): \n",
-    "    plt.bar(offset+i*tickSize+1, npbD_noDC[i]/npbD_cas[i],     width=barWidth, color=cmap(1))\n",
-    "    plt.bar(offset+i*tickSize+2, npbD_noDC[i]/npbD_ram[i],     width=barWidth, color=cmap(2))\n",
-    "    plt.bar(offset+i*tickSize+3, npbD_noDC[i]/npbD_ramOpt[i],  width=barWidth, color=cmap(3))\n",
-    "\n",
-    "\n",
-    "\n",
-    "plt.figtext(0.25, -0.01, \"GAPBS-25\")\n",
-    "plt.figtext(0.7, -0.01, \"NPB-D\")\n",
-    "\n",
-    "latencies = ['50ns','100ns','250ns','50ns','100ns','250ns']\n",
-    "brLab = np.arange(len(latencies))\n",
-    "plt.xticks(brLab*tickSize+1, latencies)\n",
-    "\n",
-    "plt.axvline(x=11, color='black')\n",
-    "plt.axhline(y=1, color='grey')\n",
-    "\n",
-    "plt.ylabel(\"Speedup to no DRAM $\")\n",
-    "plt.legend(fontsize=9, ncol=1, loc='upper left')\n",
-    "plt.tight_layout()"
-   ]
   }
  ],
  "metadata": {
@@ -2761,7 +1317,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.10"
+   "version": "3.10.6"
   },
   "orig_nbformat": 4,
   "vscode": {
diff --git a/realAppRun.sh b/realAppRun.sh
index f707b1eada..b8e883a6fb 100755
--- a/realAppRun.sh
+++ b/realAppRun.sh
@@ -1,747 +1,3 @@
-# link latency
-
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   CascadeLakeNoPartWrs 1 1 1250 0 &
-build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/cascade/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 CascadeLakeNoPartWrs 1 1 1250 0 &
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   CascadeLakeNoPartWrs 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/cascade/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 CascadeLakeNoPartWrs 1 1 625 0 &
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   CascadeLakeNoPartWrs 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/cascade/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 CascadeLakeNoPartWrs 1 1 250 0 &
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 0 0 0 4ns &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/tagCheckSensitivity/rambus/4ns/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 0 0 0 4ns &
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 0 0 0 8 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/8/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 0 0 0 8 &
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 0 0 0 64 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/64/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 0 0 0 64 &
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 0 0 0 16 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/fbSensitivity2/rambus/16/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 0 0 0 16 &
-##############################################
-##############################################
-
-# link latency
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   CascadeLakeNoPartWrs 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/cascade/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 CascadeLakeNoPartWrs 1 1 125 0 &
-
-#######################################
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 125 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 125 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/noDC/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 125 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 625 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 625 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/noDC/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 625 1 &
-
-##**************************
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 250 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/noDC/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 250 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 1250 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/noDC/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 1250 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 2500 1 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 2500 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/noDC/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 2500 1 &
-
-
-###############################
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 125 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 125 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/50/rambus/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 125 0 &
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 625 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 625 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/250/rambus/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 625 0 &
-
-###***************************************
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 250 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/100/rambus/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 250 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 1250 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 1250 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/500/rambus/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 1250 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 1 2500 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 1 2500 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/link/1000/rambus/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 1 2500 0 &
-
-
-###################################################
-###################################################
-
-# set-associative
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/2/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 2 0 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 2 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/2/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 2 0 0 0 &
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/8/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 8 0 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 8 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/8/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 8 0 0 0 &
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/16/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 16 0 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 16 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/16/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 16 0 0 0 &
-
-
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-asso/rambus/32/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 32 0 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 32 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/set-assoc/rambus/32/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 32 0 0 0 &
-
-####################################################################
-####################################################################
-
-# baselines
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 0 0 0 &
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Oracle 1 0 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   Rambus 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 Rambus 1 0 0 1 &
-
-#######################################
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/cascade/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 CascadeLakeNoPartWrs 1 0 0 0 &
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Rambus 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/rambus/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Rambus 1 0 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Oracle 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/oracle/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Oracle 1 0 0 0 &
-
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   Oracle 1 0 0 1 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=newResults/baseline/noDC/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 Oracle 1 0 0 1 &
-
-
-#############################################################
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/bt      configs-npb-gapbs/restore_both.py   bt.D.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/cg      configs-npb-gapbs/restore_both.py   cg.D.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/ft      configs-npb-gapbs/restore_both.py   ft.D.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/is      configs-npb-gapbs/restore_both.py   is.D.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/lu      configs-npb-gapbs/restore_both.py   lu.D.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/mg      configs-npb-gapbs/restore_both.py   mg.D.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/sp      configs-npb-gapbs/restore_both.py   sp.D.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/NPB/ua      configs-npb-gapbs/restore_both.py   ua.D.x  CascadeLakeNoPartWrs 1 0 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-25   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-25  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-25   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-25   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-25   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_85GB_g25_nD/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-25 CascadeLakeNoPartWrs 1 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/bt      configs-npb-gapbs/restore_both.py   bt.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/cg      configs-npb-gapbs/restore_both.py   cg.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/ft      configs-npb-gapbs/restore_both.py   ft.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/is      configs-npb-gapbs/restore_both.py   is.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/lu      configs-npb-gapbs/restore_both.py   lu.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/mg      configs-npb-gapbs/restore_both.py   mg.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/sp      configs-npb-gapbs/restore_both.py   sp.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/NPB/ua      configs-npb-gapbs/restore_both.py   ua.C.x  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/GAPBS/bc    configs-npb-gapbs/restore_both.py   bc-22   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/GAPBS/bfs   configs-npb-gapbs/restore_both.py   bfs-22  CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/GAPBS/cc    configs-npb-gapbs/restore_both.py   cc-22   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/GAPBS/pr    configs-npb-gapbs/restore_both.py   pr-22   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/GAPBS/tc    configs-npb-gapbs/restore_both.py   tc-22   CascadeLakeNoPartWrs 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=pcAnalysis/3hour_1halfChkpt/1GB_8GB_g22_nC/GAPBS/sssp  configs-npb-gapbs/restore_both.py   sssp-22 CascadeLakeNoPartWrs 1 0 0 &
-
-#########################################
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/bt      configs-npb-gapbs/npb_checkpoint.py     bt.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/cg      configs-npb-gapbs/npb_checkpoint.py     cg.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/ft      configs-npb-gapbs/npb_checkpoint.py     ft.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/is      configs-npb-gapbs/npb_checkpoint.py     is.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/lu      configs-npb-gapbs/npb_checkpoint.py     lu.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/mg      configs-npb-gapbs/npb_checkpoint.py     mg.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/sp      configs-npb-gapbs/npb_checkpoint.py     sp.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/NPB/ua      configs-npb-gapbs/npb_checkpoint.py     ua.C.x   C  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/GAPBS/bc    configs-npb-gapbs/gapbs_checkpoint.py   bc    22 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/GAPBS/bfs   configs-npb-gapbs/gapbs_checkpoint.py   bfs   22 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/GAPBS/cc    configs-npb-gapbs/gapbs_checkpoint.py   cc    22 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/GAPBS/pr    configs-npb-gapbs/gapbs_checkpoint.py   pr    22 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/GAPBS/tc    configs-npb-gapbs/gapbs_checkpoint.py   tc    22 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_8GB_g22_nC_1halfSec/GAPBS/sssp  configs-npb-gapbs/gapbs_checkpoint.py   sssp  22 CascadeLakeNoPartWrs 0 0 &
-
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/bt      configs-npb-gapbs/npb_checkpoint.py     bt.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/cg      configs-npb-gapbs/npb_checkpoint.py     cg.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/ft      configs-npb-gapbs/npb_checkpoint.py     ft.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/is      configs-npb-gapbs/npb_checkpoint.py     is.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/lu      configs-npb-gapbs/npb_checkpoint.py     lu.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/mg      configs-npb-gapbs/npb_checkpoint.py     mg.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/sp      configs-npb-gapbs/npb_checkpoint.py     sp.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/NPB/ua      configs-npb-gapbs/npb_checkpoint.py     ua.D.x   D  CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/GAPBS/bc    configs-npb-gapbs/gapbs_checkpoint.py   bc    25 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/GAPBS/bfs   configs-npb-gapbs/gapbs_checkpoint.py   bfs   25 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/GAPBS/cc    configs-npb-gapbs/gapbs_checkpoint.py   cc    25 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/GAPBS/pr    configs-npb-gapbs/gapbs_checkpoint.py   pr    25 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/GAPBS/tc    configs-npb-gapbs/gapbs_checkpoint.py   tc    25 CascadeLakeNoPartWrs 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re  --outdir=chkpt1GigDC/1GB_85GB_g25_nD_1halfSec/GAPBS/sssp  configs-npb-gapbs/gapbs_checkpoint.py   sssp  25 CascadeLakeNoPartWrs 0 0 &
-=======
 # script                              #App    #Policy   #Assoc  #EnableLinkLatency #LinkLatency #EnableBypassDRAM$
 # configs-npb-gapbs/restore_both.py   bt.D.x  Rambus    1       0                  0            0
 
@@ -777,4 +33,4 @@ build/X86_MESI_Two_Level/gem5.opt -re  --outdir=/home/babaie/projects/rambusDesi
 
 # build/X86_MESI_Two_Level/gem5.opt -re --outdir=m5out-stat/rdH  traffGen_def.py random 100 1 1 0 &
 # build/X86_MESI_Two_Level/gem5.opt -re --outdir=m5out-stat/rdMC traffGen_def.py random 100 1 0 0 &
-# build/X86_MESI_Two_Level/gem5.opt -re --outdir=m5out-stat/rdMD traffGen_def.py random 100 1 0 1 &
+# build/X86_MESI_Two_Level/gem5.opt -re --outdir=m5out-stat/rdMD traffGen_def.py random 100 1 0 1 &
\ No newline at end of file
diff --git a/src/cpu/testers/dr_trace_player/trace_player.cc b/src/cpu/testers/dr_trace_player/trace_player.cc
index 9bb4b97d39..4ee814f94f 100644
--- a/src/cpu/testers/dr_trace_player/trace_player.cc
+++ b/src/cpu/testers/dr_trace_player/trace_player.cc
@@ -86,7 +86,6 @@ DRTracePlayer::tryExecuteInst(DRTraceReader::TraceRef &cur_ref)
         // End of trace for this player exit the simulation
         // TODO: Move this to when the last instruction is completed
         exitSimLoopNow("End of DRTrace");
-        return;
     }
 
     DPRINTF(DRTrace, "Exec reference pc: %0#x, addr: %0#x, size: %d, "
diff --git a/src/cpu/testers/dr_trace_player/trace_reader.cc b/src/cpu/testers/dr_trace_player/trace_reader.cc
index e509415174..afaa3950da 100644
--- a/src/cpu/testers/dr_trace_player/trace_reader.cc
+++ b/src/cpu/testers/dr_trace_player/trace_reader.cc
@@ -171,10 +171,6 @@ DRTraceReader::getNextTraceReference(unsigned player_id)
         case TRACE_TYPE_PREFETCHT1:
         case TRACE_TYPE_PREFETCHT2:
         case TRACE_TYPE_PREFETCHNTA:
-            if (raw_entry.addr == 0) {
-                warn("Encountered a prefetch req with Addr = 0 \n");
-                return getNextTraceReference(player_id);
-	     }
             ref.addr = raw_entry.addr;
             ref.size = raw_entry.size;
             ref.isValid = true;
diff --git a/src/mem/DRAMInterface.py b/src/mem/DRAMInterface.py
index b6f3f5109b..201e5b0961 100644
--- a/src/mem/DRAMInterface.py
+++ b/src/mem/DRAMInterface.py
@@ -187,13 +187,13 @@ class DRAMInterface(MemInterface):
     tXSDLL = Param.Latency("0ns", "Self-refresh exit latency DLL")
 
     tTAGBURST = Param.Latency("0ns", "tRL_FAST")
-    tRLFAST   = Param.Latency("0ns", "tRL_FAST")
+    tRL_FAST   = Param.Latency("0ns", "tRL_FAST")
     tHM2DQ    = Param.Latency("0ns", "tHM2DQ")
     tRTW_int  = Param.Latency("0ns", "tRTW_int")
     tRFBD     = Param.Latency("0ns", "tRFBD")
     tRCD_FAST = Param.Latency("0ns", "tRCD_FAST")
+    tRC_FAST  = Param.Latency("0ns", "tRCD_FAST")
     flushBuffer_high_thresh_perc = Param.Percent(0, "Threshold to force writes")
-    flush_buffer_size = Param.Unsigned(0, "flush buffer size")
 
     # number of data beats per clock. with DDR, default is 2, one per edge
     # used in drampower.cc
@@ -1304,11 +1304,12 @@ class TDRAM(DRAMInterface):
 
     # new
     tTAGBURST = "1ns"
-    tRLFAST = "1ns"
+    tRL_FAST = "1ns"
     tHM2DQ = "1ns"
     tRTW_int = "2ns"
     tRFBD = "2ns"
     tRCD_FAST = "7.5ns"
+    tRC_FAST = "10.5ns"
     enable_read_flush_buffer = True
     flushBuffer_high_thresh_perc = 80
 
@@ -1390,14 +1391,13 @@ class TDRAM_32(DRAMInterface):
 
     # new
     tTAGBURST = "0.5ns"
-    tRLFAST = "7.5ns"
+    tRL_FAST = "7.5ns"
     tHM2DQ = "0ns"
     tRTW_int = "1ns"
     tRFBD = "1ns"
     tRCD_FAST = "7.5ns"
     enable_read_flush_buffer = True
     flushBuffer_high_thresh_perc = 80
-    flush_buffer_size = 32
 
     tRP = "14ns"
 
diff --git a/src/mem/PolicyManager.py b/src/mem/PolicyManager.py
index 8c98ae022b..2854e389b0 100644
--- a/src/mem/PolicyManager.py
+++ b/src/mem/PolicyManager.py
@@ -5,7 +5,7 @@
 from m5.objects.AbstractMemory import AbstractMemory
 from m5.objects.DRAMInterface import *
 
-class Policy(Enum): vals = ['CascadeLakeNoPartWrs', 'Oracle', 'BearWriteOpt', 'Rambus']
+class Policy(Enum): vals = ['CascadeLakeNoPartWrs', 'Oracle', 'BearWriteOpt', 'Rambus', 'RambusTagProbOpt']
 class ReplPolicySetAssoc(Enum): vals = ['bip_rp', 'brrip_rp', 'dueling_rp', 'fifo_rp', 'lfu_rp', 'lru_rp', 'mru_rp', 'random_rp', 'second_chance_rp', 'ship_rp', 'tree_plru_rp', 'weighted_lru_rp']
 
 class PolicyManager(AbstractMemory):
diff --git a/src/mem/abstract_mem.hh b/src/mem/abstract_mem.hh
index 05f65d6188..572e28ff03 100644
--- a/src/mem/abstract_mem.hh
+++ b/src/mem/abstract_mem.hh
@@ -46,6 +46,7 @@
 #ifndef __MEM_ABSTRACT_MEMORY_HH__
 #define __MEM_ABSTRACT_MEMORY_HH__
 
+#include "enums/Policy.hh"
 #include "mem/backdoor.hh"
 #include "mem/port.hh"
 #include "params/AbstractMemory.hh"
@@ -225,6 +226,8 @@ class AbstractMemory : public ClockedObject
 
     void initState() override;
 
+    enums::Policy locMemPolicy;
+    
     virtual Tick get_tRP() { panic("AbstractMemory get_tRP should not be executed from here.\n");
                              return false;}
     virtual Tick get_tRCD_RD() { panic("AbstractMemory get_tRCD_RD should not be executed from here.\n");
diff --git a/src/mem/dram_interface.cc b/src/mem/dram_interface.cc
index 5e6be4bb66..4fa3d97016 100644
--- a/src/mem/dram_interface.cc
+++ b/src/mem/dram_interface.cc
@@ -48,6 +48,7 @@
 #include "debug/DRAMPower.hh"
 #include "debug/DRAMState.hh"
 #include "debug/MemCtrl.hh"
+#include "enums/Policy.hh"
 #include "sim/system.hh"
 
 namespace gem5
@@ -61,7 +62,6 @@ namespace memory
 std::pair<MemPacketQueue::iterator, Tick>
 DRAMInterface::chooseNextFRFCFS(MemPacketQueue& queue, Tick min_col_at) const
 {
-    DPRINTF(DRAM, "in dram->chooseNextFRFCFS func\n");
     std::vector<uint32_t> earliest_banks(ranksPerChannel, 0);
 
     // Has minBankPrep been called to populate earliest_banks?
@@ -365,6 +365,7 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
     Tick act_at = MaxTick;
     // get the rank
     Rank& rank_ref = *ranks[mem_pkt->rank];
+
     assert(rank_ref.inRefIdleState());
 
     // are we in or transitioning to a low-power state and have not scheduled
@@ -394,20 +395,7 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
         }
 
         // next we need to account for the delay in activating the page
-        Tick act_tick;
-        if (mem_pkt->isLocMem) {
-            if (polMan->locMemPolicy == enums::RambusTagProbOpt) {
-                act_tick = std::max(std::max(bank_ref.tagActAllowedAt, bank_ref.actAllowedAt), curTick());
-
-                if (bank_ref.tagActAllowedAt > bank_ref.actAllowedAt && bank_ref.tagActAllowedAt > curTick()) {
-                    stats.actDelayedDueToTagAct++;
-                }
-            } else {
-                act_tick = std::max(bank_ref.actAllowedAt, curTick());
-            }
-        } else {
-            act_tick = std::max(bank_ref.actAllowedAt, curTick());
-        }
+        Tick act_tick = std::max(bank_ref.actAllowedAt, curTick());
 
         // Record the activation and deal with all the global timing
         // constraints caused be a new activation (tRRD and tXAW)
@@ -449,18 +437,21 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
         }
     }
 
-    DPRINTF(DRAM, "Schedule RD/WR burst at tick %d\n", cmd_at);
+        DPRINTF(DRAM, "Schedule RD/WR burst at tick %d\n", cmd_at);
 
     // update the packet ready time
     Tick stall_delay = 0;
     if(mem_pkt->isTagCheck) {
-        assert(mem_pkt->isLocMem);
 
+        assert(mem_pkt->isLocMem);
+        
         // Calculating the tag check ready time
         if (mem_pkt->pkt->owIsRead) {
-            mem_pkt->tagCheckReady = cmd_at - tRCD_RD + tRCD_FAST + tRLFAST;
+            assert((cmd_at + tRCD_FAST + tRL_FAST) > tRCD_RD);
+            mem_pkt->tagCheckReady = (cmd_at + tRCD_FAST + tRL_FAST) - tRCD_RD;
         } else {
-            mem_pkt->tagCheckReady = cmd_at - tRCD_RD - tRTW_int + tRCD_FAST + tRLFAST;
+            assert((cmd_at + tRCD_FAST + tRL_FAST) > (tRCD_RD + tRTW_int));
+            mem_pkt->tagCheckReady = (cmd_at + tRCD_FAST + tRL_FAST) - (tRCD_RD + tRTW_int);
         }
         stats.tagResBursts++;
 
@@ -470,15 +461,10 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
             stats.tagBursts++;
         }
 
-        if (polMan->locMemPolicy == enums::RambusTagProbOpt) {
-            assert((mem_pkt->tagCheckReady + tRC_FAST) > (tRL_FAST + tRCD_FAST));
-            bank_ref.tagActAllowedAt = (mem_pkt->tagCheckReady + tRC_FAST) - (tRL_FAST + tRCD_FAST);
-        }
-
         // Calculating the data ready time
         if (mem_pkt->pkt->owIsRead) {
 
-            mem_pkt->readyTime = cmd_at + std::max(tRL, tRLFAST + tHM2DQ) + tBURST;
+            mem_pkt->readyTime = cmd_at + std::max(tRL,  tRL_FAST + tHM2DQ) + tBURST;
 
             // Rd Miss Clean
             if (mem_pkt->pkt->owIsRead && !mem_pkt->pkt->isHit && !mem_pkt->pkt->isDirty) {
@@ -566,7 +552,7 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
 
                 tempFlushBuffer.push_back(std::make_pair(pushBackFBTick, mem_pkt->pkt->dirtyLineAddr));
 
-                if ((tempFlushBuffer.size() + flushBuffer.size()) >= (flushBufferSize * flushBufferHighThreshold) &&
+                if ((tempFlushBuffer.size() + flushBuffer.size()) >= (banksPerRank * flushBufferHighThreshold) &&
                      !readFlushBufferEvent.scheduled() &&
                      !flushBuffer.empty()) {
                     
@@ -620,17 +606,9 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
         }
 
     } else {
-        // assert(mem_pkt->tagCheckReady == MaxTick);
+        assert(mem_pkt->tagCheckReady == MaxTick);
         if (mem_pkt->isRead()) {
             mem_pkt->readyTime = cmd_at + tRL + tBURST;
-            if (mem_pkt->isLocMem) {
-                if(polMan->locMemPolicy == enums::RambusTagProbOpt &&
-                   !mem_pkt->pkt->isHit &&
-                   mem_pkt->pkt->isDirty) {
-                    // a probed Rd Miss Dirty
-                    mem_pkt->pkt->hasDirtyData = true;
-                }
-            }
         } else {
             mem_pkt->readyTime = cmd_at + tWL + tBURST;
         }
@@ -651,8 +629,9 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
         }
     }
 
+
     DPRINTF(DRAMT, "curr pkt, addr: %d, isRd: %d, isTC: %d, bank %d, row %d, act: %d, RdAlw: %d, WrAlw: %d, cmd: %d, rdy: %d\n", 
-    mem_pkt->getAddr(), mem_pkt->isRead(), mem_pkt->isTagCheck, (unsigned) mem_pkt->bank, (unsigned) mem_pkt->row,
+    mem_pkt->addr, mem_pkt->pkt->isRead(), mem_pkt->pkt->isTagCheck, (unsigned) mem_pkt->bank, (unsigned) mem_pkt->row,
     act_at/1000, bank_ref.rdAllowedAt/1000, bank_ref.wrAllowedAt/1000, cmd_at/1000, mem_pkt->readyTime/1000);
 
     rank_ref.lastBurstTick = cmd_at;
@@ -853,15 +832,6 @@ DRAMInterface::doBurstAccess(MemPacket* mem_pkt, Tick next_burst_at,
     return std::make_pair(cmd_at, cmd_at + burst_gap);
 }
 
-void
-DRAMInterface::updateTagActAllowed(unsigned rankNumber, unsigned bankNumber, Tick BSlotTagBankBusyAt) 
-{
-    assert(BSlotTagBankBusyAt!=MaxTick);
-    ranks[rankNumber]->banks[bankNumber].tagActAllowedAt = BSlotTagBankBusyAt;
-    DPRINTF(DRAM, "updateTagFunc tagActAllowedAt change, rank/bank %d/%d -- tagActAllowedAt: %d\n",
-        rankNumber, bankNumber, BSlotTagBankBusyAt);
-}
-
 void
 DRAMInterface::addRankToRankDelay(Tick cmd_at)
 {
@@ -892,10 +862,10 @@ DRAMInterface::DRAMInterface(const DRAMInterfaceParams &_p)
       tRFC(_p.tRFC), tREFI(_p.tREFI), tRRD(_p.tRRD), tRRD_L(_p.tRRD_L),
       tPPD(_p.tPPD), tAAD(_p.tAAD),
       tXAW(_p.tXAW), tXP(_p.tXP), tXS(_p.tXS),
-      tTAGBURST(_p.tTAGBURST), tRLFAST(_p.tRLFAST), tHM2DQ(_p.tHM2DQ),
+      tTAGBURST(_p.tTAGBURST),  tRL_FAST(_p. tRL_FAST), tHM2DQ(_p.tHM2DQ),
       tRTW_int(_p.tRTW_int), tRFBD(_p.tRFBD), tRCD_FAST(_p.tRCD_FAST),
+      tRC_FAST(_p.tRC_FAST),
       flushBufferHighThreshold(_p.flushBuffer_high_thresh_perc / 100.0),
-      flushBufferSize(_p.flush_buffer_size),
       clkResyncDelay(_p.tBURST_MAX),
       dataClockSync(_p.data_clock_sync),
       burstInterleave(tBURST != tBURST_MIN),
@@ -2278,6 +2248,8 @@ DRAMInterface::DRAMStats::DRAMStats(DRAMInterface &_dram)
              "Maximum flush buffer length when enqueuing"),
     ADD_STAT(refSchdRFB, statistics::units::Count::get(),
              "Maximum flush buffer length when enqueuing"),
+    ADD_STAT( actDelayedDueToTagAct, statistics::units::Count::get(),
+             " "),
     ADD_STAT(perBankRdBursts, statistics::units::Count::get(),
              "Per bank write bursts"),
     ADD_STAT(perBankWrBursts, statistics::units::Count::get(),
diff --git a/src/mem/dram_interface.hh b/src/mem/dram_interface.hh
index b390f93f39..44d9b7447d 100644
--- a/src/mem/dram_interface.hh
+++ b/src/mem/dram_interface.hh
@@ -509,13 +509,13 @@ class DRAMInterface : public MemInterface
     const Tick tXP;
     const Tick tXS;
     const Tick tTAGBURST;
-    const Tick tRLFAST;
+    const Tick tRL_FAST;
     const Tick tHM2DQ;
     const Tick tRTW_int;
     const Tick tRFBD;
     const Tick tRCD_FAST;
+    const Tick tRC_FAST;
     float flushBufferHighThreshold;
-    unsigned flushBufferSize;
     const Tick clkResyncDelay;
     const bool dataClockSync;
     const bool burstInterleave;
@@ -602,6 +602,7 @@ class DRAMInterface : public MemInterface
         statistics::Scalar totPktsPushedFB;
         statistics::Scalar maxFBLenEnq;
         statistics::Scalar refSchdRFB;
+        statistics::Scalar actDelayedDueToTagAct;
 
         /** DRAM per bank stats */
         statistics::Vector perBankRdBursts;
@@ -871,7 +872,7 @@ class DRAMInterface : public MemInterface
     Tick getTRCDFAST() override { return tRCD_FAST;}
     
     void updateTagActAllowed(unsigned rankNumber, unsigned bankNumber, Tick BSlotTagBankBusyAt) override;
-
+    
     DRAMInterface(const DRAMInterfaceParams &_p);
 };
 
diff --git a/src/mem/mem_ctrl.cc b/src/mem/mem_ctrl.cc
index bda202c972..4ee2c28d4b 100644
--- a/src/mem/mem_ctrl.cc
+++ b/src/mem/mem_ctrl.cc
@@ -264,6 +264,7 @@ MemCtrl::addToReadQueue(PacketPtr pkt,
             mem_pkt = mem_intr->decodePacket(pkt, addr, size, true,
                                                     mem_intr->pseudoChannel);
             mem_pkt->isTagCheck = pkt->isTagCheck;
+            mem_pkt->isLocMem = pkt->isLocMem;
 
             // Increment read entries of the rank (dram)
             // Increment count to trigger issue of non-deterministic read (nvm)
@@ -280,13 +281,9 @@ MemCtrl::addToReadQueue(PacketPtr pkt,
             readQueue[mem_pkt->qosValue()].push_back(mem_pkt);
 
             // log packet
-            DPRINTF(MemCtrl, "logRequest rd: %d %d %x\n",
-            pkt->requestorId(),
-            pkt->qosValue(), mem_pkt->addr);
-
             logRequest(MemCtrl::READ, pkt->requestorId(),
                        pkt->qosValue(), mem_pkt->addr, 1);
-            
+
             mem_intr->readQueueSize++;
 
             // Update stats
@@ -344,6 +341,7 @@ MemCtrl::addToWriteQueue(PacketPtr pkt, unsigned int pkt_count,
             mem_pkt = mem_intr->decodePacket(pkt, addr, size, false,
                                                     mem_intr->pseudoChannel);
             mem_pkt->isTagCheck = pkt->isTagCheck;
+            mem_pkt->isLocMem = pkt->isLocMem;
 
             // Default readyTime to Max if nvm interface;
             //will be reset once read is issued
@@ -360,13 +358,9 @@ MemCtrl::addToWriteQueue(PacketPtr pkt, unsigned int pkt_count,
             isInWriteQueue.insert(burstAlign(addr, mem_intr));
           
             // log packet
-            DPRINTF(MemCtrl, "logRequest wr: %d %d %x\n",
-            pkt->requestorId(),
-            pkt->qosValue(), mem_pkt->addr);
-
             logRequest(MemCtrl::WRITE, pkt->requestorId(),
                        pkt->qosValue(), mem_pkt->addr, 1);
-            
+
             mem_intr->writeQueueSize++;
 
             //assert(totalWriteQueueSize == isInWriteQueue.size());
@@ -461,7 +455,7 @@ MemCtrl::recvTimingReq(PacketPtr pkt)
     if (pkt->isWrite()) {
         assert(size != 0);
         if (writeQueueFull(pkt_count)) {
-            DPRINTF(MemCtrl, "Write queue full, not accepting, readQ size: %d, writeQ size: %d\n", readQueue[pkt->qosValue()].size(), writeQueue[pkt->qosValue()].size());
+            DPRINTF(MemCtrl, "Write queue full, not accepting\n");
             // remember that we have to retry this port
             retryWrReq = true;
             stats.numWrRetry++;
@@ -481,7 +475,7 @@ MemCtrl::recvTimingReq(PacketPtr pkt)
         assert(pkt->isRead());
         assert(size != 0);
         if (readQueueFull(pkt_count)) {
-            DPRINTF(MemCtrl, "Read queue full, not accepting, readQ size: %d, writeQ size: %d\n", readQueue[pkt->qosValue()].size(), writeQueue[pkt->qosValue()].size());
+            DPRINTF(MemCtrl, "Read queue full, not accepting\n");
             // remember that we have to retry this port
             retryRdReq = true;
             stats.numRdRetry++;
@@ -577,7 +571,7 @@ MemCtrl::chooseNext(MemPacketQueue& queue, Tick extra_col_delay,
                                                 MemInterface* mem_intr)
 {
     // This method does the arbitration between requests.
-    DPRINTF(MemCtrl, "in chooseNext func\n");
+
     MemPacketQueue::iterator ret = queue.end();
 
     if (!queue.empty()) {
@@ -620,7 +614,6 @@ std::pair<MemPacketQueue::iterator, Tick>
 MemCtrl::chooseNextFRFCFS(MemPacketQueue& queue, Tick extra_col_delay,
                                 MemInterface* mem_intr)
 {
-    DPRINTF(MemCtrl, "in chooseNextFRFCFS func\n");
     auto selected_pkt_it = queue.end();
     Tick col_allowed_at = MaxTick;
 
@@ -713,6 +706,7 @@ MemCtrl::sendTagCheckRespond(MemPacket* mem_pkt)
     PacketPtr tagCheckResPkt = getPacket(mem_pkt->addr, 8, MemCmd::ReadReq);
 
     tagCheckResPkt->isTagCheck = mem_pkt->pkt->isTagCheck;
+    tagCheckResPkt->isLocMem = mem_pkt->pkt->isLocMem;
     tagCheckResPkt->owIsRead = mem_pkt->pkt->owIsRead;
     tagCheckResPkt->isHit = mem_pkt->pkt->isHit;
     tagCheckResPkt->isDirty = mem_pkt->pkt->isDirty;
@@ -934,6 +928,7 @@ MemCtrl::doBurstAccess(MemPacket* mem_pkt, MemInterface* mem_intr)
     // conservative estimate of when we have to schedule the next
     // request to not introduce any unecessary bubbles. In most cases
     // we will wake up sooner than we have to.
+    assert(mem_intr->nextBurstAt > mem_intr->commandOffset());
     mem_intr->nextReqTime = mem_intr->nextBurstAt - mem_intr->commandOffset();
 
     // Update the common bus stats
@@ -1002,9 +997,6 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
                         EventFunctionWrapper& resp_event,
                         EventFunctionWrapper& next_req_event,
                         bool& retry_wr_req) {
-    DPRINTF(MemCtrl, "processNextReqEvent: readQueueSize: %d, writeQueueSize:%d, readQ: %d, writeQ: %d, respQ: %d\n",
-                     mem_intr->readQueueSize, mem_intr->writeQueueSize, readQueue[0].size(), writeQueue[0].size(),
-                     respQueue.size());
     if (considerOldestWrite) {
         updateOldestWriteAge();
     }
@@ -1094,7 +1086,7 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
 
                 prio--;
 
-                DPRINTF(MemCtrl,
+                DPRINTF(QOS,
                         "Checking READ queue [%d] priority [%d elements]\n",
                         prio, queue->size());
 
@@ -1123,59 +1115,25 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
 
             auto mem_pkt = *to_read;
 
-            DPRINTF(MemCtrl, "Read pkt chosen before doburst: %x\n", mem_pkt->getAddr());
-
             Tick cmd_at = doBurstAccess(mem_pkt, mem_intr);
 
-            if (mem_pkt->isLocMem) {
-                // && polMan->locMemPolicy == RambusTagProb
-                assert(mem_pkt->BSlotBusyUntil!=MaxTick);
-                assert(!mem_pkt->probedRdMC);
-
-                if (mem_pkt->probedRdH) {
-                    assert(mem_pkt->tagCheckReady != MaxTick);
-                    assert(!mem_pkt->probedRdMD);
-                    assert (mem_pkt->tagCheckReady > (dram->getTRCDFAST() + dram->getTRLFAST()));
-                    assert(cmd_at > (mem_pkt->tagCheckReady - dram->getTRCDFAST() - dram->getTRLFAST()));
-                    
-                    stats.deltaAbSlotRdH += 
-                    (cmd_at - (mem_pkt->tagCheckReady - dram->getTRCDFAST() - dram->getTRLFAST()));
-                
-                } else if (mem_pkt->probedRdMD) {
-                    assert(mem_pkt->tagCheckReady != MaxTick);
-                    assert(!mem_pkt->probedRdH);
-                    assert (mem_pkt->tagCheckReady > (dram->getTRCDFAST() + dram->getTRLFAST()));
-                    assert(cmd_at > (mem_pkt->tagCheckReady - dram->getTRCDFAST() - dram->getTRLFAST()));
-                    
-                    stats.deltaAbSlotRdMD +=
-                    (cmd_at - (mem_pkt->tagCheckReady - dram->getTRCDFAST() - dram->getTRLFAST()));
-                }
-            }
-
-            assert((*to_read)->getAddr() == mem_pkt->getAddr());
-
             if (mem_pkt->isTagCheck) {
                 DPRINTF(MemCtrl, "read times: %x, %s: tag: %d  data: %d \n", mem_pkt->addr, mem_pkt->pkt->cmdString(), mem_pkt->tagCheckReady, mem_pkt->readyTime);
                 sendTagCheckRespond(mem_pkt);
             }
 
             DPRINTF(MemCtrl,
-            "Command for %x, issued at %lld.\n", mem_pkt->addr, cmd_at);
+            "Command for %d, issued at %lld.\n", mem_pkt->addr, cmd_at);
 
             // sanity check
             assert(pktSizeCheck(mem_pkt, mem_intr));
             assert(mem_pkt->readyTime >= curTick());
 
             // log the response
-            DPRINTF(MemCtrl, "logResponse rd1: %d %d %x %d\n",
-            (*to_read)->requestorId(),
-            mem_pkt->qosValue(), mem_pkt->getAddr(),
-            mem_pkt->readyTime - mem_pkt->entryTime);
-
             logResponse(MemCtrl::READ, (*to_read)->requestorId(),
                         mem_pkt->qosValue(), mem_pkt->getAddr(), 1,
                         mem_pkt->readyTime - mem_pkt->entryTime);
-            
+
             mem_intr->readQueueSize--;
 
             // Insert into response queue. It will be sent back to the
@@ -1202,24 +1160,8 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
             }
 
             // remove the request from the queue
-            // the iterator is no longer valid .          
+            // the iterator is no longer valid .
             readQueue[mem_pkt->qosValue()].erase(to_read);
-
-            // Tag probing B slot comes here.
-            if (mem_pkt->isLocMem) {
-                assert(mem_pkt->BSlotBusyUntil != MaxTick);
-
-                DPRINTF(MemCtrl, "Rd--> Start probing for B slot: Aslot addr: %x , end of tag bank busy for B slot: %d\n",
-                        mem_pkt->getAddr(), mem_pkt->BSlotBusyUntil);
-                bool found = findCandidateForBSlot(mem_pkt);
-                DPRINTF(MemCtrl, "Rd--> B slot result: found flag: %d\n",found);
-
-                if (found) {
-                    stats.foundCandidBSlot++;
-                } else {
-                    stats.noCandidBSlot++;
-                }
-            }
         }
 
         // switching to writes, either because the read queue is empty
@@ -1266,6 +1208,9 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
         if (!write_found) {
             DPRINTF(MemCtrl, "No Writes Found - exiting\n");
             return;
+
+            DPRINTF(DRAMT, "No Writes Found - exiting\n");
+            return;
         }
 
         auto mem_pkt = *to_write;
@@ -1273,12 +1218,13 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
         // sanity check
         assert(pktSizeCheck(mem_pkt, mem_intr));
 
-        DPRINTF(MemCtrl, "Write pkt chosen before doburst: %x\n", mem_pkt->getAddr());
-
         Tick cmd_at = doBurstAccess(mem_pkt, mem_intr);
 
         DPRINTF(MemCtrl,
-        "Command for %x, issued at %lld.\n", mem_pkt->addr, cmd_at);
+        "Command for %d, issued at %lld.\n", mem_pkt->addr, cmd_at);
+
+        DPRINTF(DRAMT,
+        "Command for %d, issued at %lld.\n", mem_pkt->addr, cmd_at);
 
         if (mem_pkt->isTagCheck) {
                 DPRINTF(MemCtrl, "write times: %x, %s: tag: %d  data: %d \n", mem_pkt->addr, mem_pkt->pkt->cmdString(), mem_pkt->tagCheckReady, mem_pkt->readyTime);
@@ -1289,36 +1235,15 @@ MemCtrl::processNextReqEvent(MemInterface* mem_intr,
         isInWriteQueue.erase(burstAlign(mem_pkt->addr, mem_intr));
 
         // log the response
-        DPRINTF(MemCtrl, "logResponse wr1: %d %d %x %d\n",
-                mem_pkt->requestorId(),
-                mem_pkt->qosValue(), mem_pkt->getAddr(),
-                mem_pkt->readyTime - mem_pkt->entryTime);
-
         logResponse(MemCtrl::WRITE, mem_pkt->requestorId(),
                     mem_pkt->qosValue(), mem_pkt->getAddr(), 1,
                     mem_pkt->readyTime - mem_pkt->entryTime);
-        
+
         mem_intr->writeQueueSize--;
 
         // remove the request from the queue - the iterator is no longer valid
         writeQueue[mem_pkt->qosValue()].erase(to_write);
 
-        // Tag probing B slot comes here.
-        if (mem_pkt->isLocMem) {
-            assert(mem_pkt->BSlotBusyUntil != MaxTick);
-
-            DPRINTF(MemCtrl, "WR--> Start probing for B slot: Aslot addr: %x , end of tag bank busy for B slot: %d\n",
-                    mem_pkt->getAddr(), mem_pkt->BSlotBusyUntil);
-            bool found = findCandidateForBSlot(mem_pkt);
-            DPRINTF(MemCtrl, "WR--> B slot result: found flag: %d\n",found);
-
-            if (found) {
-                stats.foundCandidBSlot++;
-            } else {
-                stats.noCandidBSlot++;
-            }
-        }
-
         delete mem_pkt;
 
         // If we emptied the write queue, or got sufficiently below the
@@ -1520,6 +1445,8 @@ MemCtrl::handleTCforBSlotPkt(MemPacketQueue::iterator BslotPktIt, Tick BSlotTagB
 
         return;
     }
+
+
 }
 
 MemCtrl::CtrlStats::CtrlStats(MemCtrl &_ctrl)
@@ -1568,6 +1495,17 @@ MemCtrl::CtrlStats::CtrlStats(MemCtrl &_ctrl)
              "Reads before turning the bus around for writes"),
     ADD_STAT(wrPerTurnAround, statistics::units::Count::get(),
              "Writes before turning the bus around for reads"),
+    
+    ADD_STAT(noCandidBSlot, statistics::units::Count::get(),
+             " "),
+    ADD_STAT(foundCandidBSlot, statistics::units::Count::get(),
+             " "),
+    ADD_STAT(foundCandidBSlotRH, statistics::units::Count::get(),
+             " "),
+    ADD_STAT(foundCandidBSlotRMC, statistics::units::Count::get(),
+             " "),
+    ADD_STAT(foundCandidBSlotRMD, statistics::units::Count::get(),
+             " "),
 
     ADD_STAT(bytesReadWrQ, statistics::units::Byte::get(),
              "Total number of bytes read from write queue"),
@@ -1612,15 +1550,7 @@ MemCtrl::CtrlStats::CtrlStats(MemCtrl &_ctrl)
              "Per-requestor read average memory access latency"),
     ADD_STAT(requestorWriteAvgLat, statistics::units::Rate<
                 statistics::units::Tick, statistics::units::Count>::get(),
-             "Per-requestor write average memory access latency"),
-
-    ADD_STAT(deltaAbSlotRdH,  statistics::units::Tick::get(), "stat"),
-    ADD_STAT(deltaAbSlotRdMD,  statistics::units::Tick::get(), "stat"),
-
-    ADD_STAT(avgDeltaAbSlotRdH,  statistics::units::Rate<
-                statistics::units::Tick, statistics::units::Count>::get(), "stat"),
-    ADD_STAT(avgDeltaAbSlotRdMD,  statistics::units::Rate<
-                statistics::units::Tick, statistics::units::Count>::get(), "stat")
+             "Per-requestor write average memory access latency")
 {
 }
 
@@ -1652,9 +1582,6 @@ MemCtrl::CtrlStats::regStats()
     avgWrBWSys.precision(8);
     avgGap.precision(2);
 
-    avgDeltaAbSlotRdH.precision(2);
-    avgDeltaAbSlotRdMD.precision(2);
-
     // per-requestor bytes read and written to memory
     requestorReadBytes
         .init(max_requestors)
@@ -1721,11 +1648,6 @@ MemCtrl::CtrlStats::regStats()
     requestorWriteRate = requestorWriteBytes / simSeconds;
     requestorReadAvgLat = requestorReadTotalLat / requestorReadAccesses;
     requestorWriteAvgLat = requestorWriteTotalLat / requestorWriteAccesses;
-
-    avgDeltaAbSlotRdH = (deltaAbSlotRdH/foundCandidBSlotRH)/1000;
-    avgDeltaAbSlotRdMD = (deltaAbSlotRdMD/foundCandidBSlotRMD)/1000;
-
-
 }
 
 void
diff --git a/src/mem/mem_ctrl.hh b/src/mem/mem_ctrl.hh
index 1e3d6ef8f6..d98cc6a5ef 100644
--- a/src/mem/mem_ctrl.hh
+++ b/src/mem/mem_ctrl.hh
@@ -163,11 +163,7 @@ class MemPacket
      */
     bool isTagCheck = false;
     Tick tagCheckReady = MaxTick;
-    bool isLocMem = false;
-    Tick BSlotBusyUntil = MaxTick;
-    bool probedRdH = false;
-    bool probedRdMC = false;
-    bool probedRdMD = false;
+
 
 
     /**
@@ -634,6 +630,12 @@ class MemCtrl : public qos::MemCtrl
         statistics::Histogram rdPerTurnAround;
         statistics::Histogram wrPerTurnAround;
 
+        statistics::Scalar noCandidBSlot;
+        statistics::Scalar foundCandidBSlot;
+        statistics::Scalar foundCandidBSlotRH;
+        statistics::Scalar foundCandidBSlotRMC;
+        statistics::Scalar foundCandidBSlotRMD;
+
         statistics::Scalar bytesReadWrQ;
         statistics::Scalar bytesReadSys;
         statistics::Scalar bytesWrittenSys;
@@ -663,12 +665,6 @@ class MemCtrl : public qos::MemCtrl
         // per-requestor raed and write average memory access latency
         statistics::Formula requestorReadAvgLat;
         statistics::Formula requestorWriteAvgLat;
-
-        statistics::Scalar deltaAbSlotRdH;
-        statistics::Scalar deltaAbSlotRdMD;
-
-        statistics::Formula avgDeltaAbSlotRdH;
-        statistics::Formula avgDeltaAbSlotRdMD;
     };
 
     CtrlStats stats;
@@ -835,12 +831,6 @@ class MemCtrl : public qos::MemCtrl
 
     void updateOldestWriteAge();
 
-    bool findCandidateForBSlot(MemPacket* AslotPkt);
-
-    void handleTCforBSlotPkt(MemPacketQueue::iterator BslotPktIt, Tick BSlotTagBankBusyUntil);
-
-    MemPacketQueue::iterator searchReadQueueForBSlot(MemPacketQueue& queue, MemPacket* AslotPkt);
-
     Port &getPort(const std::string &if_name,
                   PortID idx=InvalidPortID) override;
 
diff --git a/src/mem/mem_interface.hh b/src/mem/mem_interface.hh
index ee7f75c61b..6daa6f4bf8 100644
--- a/src/mem/mem_interface.hh
+++ b/src/mem/mem_interface.hh
@@ -97,13 +97,14 @@ class MemInterface : public AbstractMemory
         Tick wrAllowedAt;
         Tick preAllowedAt;
         Tick actAllowedAt;
+        Tick tagActAllowedAt;
 
         uint32_t rowAccesses;
         uint32_t bytesAccessed;
 
         Bank() :
             openRow(NO_ROW), bank(0), bankgr(0),
-            rdAllowedAt(0), wrAllowedAt(0), preAllowedAt(0), actAllowedAt(0),
+            rdAllowedAt(0), wrAllowedAt(0), preAllowedAt(0), actAllowedAt(0), tagActAllowedAt(0),
             rowAccesses(0), bytesAccessed(0)
         { }
     };
@@ -423,6 +424,7 @@ class MemInterface : public AbstractMemory
 
     virtual void updateTagActAllowed(unsigned rankNumber, unsigned bankNumber, Tick BSlotTagAllowedAt)
         { panic("MemInterface updateTagActAllowed should not be executed from here.\n"); }
+       
 
     typedef MemInterfaceParams Params;
     MemInterface(const Params &_p);
diff --git a/src/mem/packet.hh b/src/mem/packet.hh
index d2b0ad0b74..57f46f56f7 100644
--- a/src/mem/packet.hh
+++ b/src/mem/packet.hh
@@ -298,6 +298,7 @@ class Packet : public Printable, public Extensible<Packet>
     typedef gem5::Flags<FlagsType> Flags;
 
     bool isTagCheck = false;
+    bool isLocMem = false;
     bool owIsRead = false;
     bool isHit = false;
     bool isDirty = false;
diff --git a/src/mem/policy_manager.cc b/src/mem/policy_manager.cc
index f4dc81cd30..73e23dee6d 100644
--- a/src/mem/policy_manager.cc
+++ b/src/mem/policy_manager.cc
@@ -20,7 +20,7 @@ PolicyManager::PolicyManager(const PolicyManagerParams &p):
     farReqPort(name() + ".far_req_port", *this),
     locBurstSize(p.loc_burst_size),
     farBurstSize(p.far_burst_size),
-    locMemPolicy(p.loc_mem_policy),
+    // locMemPolicy(p.loc_mem_policy),
     locMem(p.loc_mem),
     replacementPolicy(p.replacement_policy),
     dramCacheSize(p.dram_cache_size),
@@ -52,8 +52,6 @@ PolicyManager::PolicyManager(const PolicyManagerParams &p):
 {
     panic_if(orbMaxSize<8, "ORB maximum size must be at least 8.\n");
 
-    locMemPolicy = p.loc_mem_policy;
-
     locMem->setPolicyManager(this);
 
     unsigned numOfSets = dramCacheSize/(blockSize * assoc);
@@ -143,6 +141,19 @@ PolicyManager::findInORB(Addr addr)
     return found;
 }
 
+unsigned
+PolicyManager::findDupInORB(Addr addr)
+{
+    unsigned count=0;
+    for (const auto& e : ORB) {
+        if (e.second->owPkt->getAddr() == addr) {
+
+           count++;
+        }
+    }
+    return count;
+}
+
 void
 PolicyManager::init()
 {
@@ -420,9 +431,8 @@ PolicyManager::processTagCheckEvent()
 {
     // sanity check for the chosen packet
     auto orbEntry = ORB.at(pktTagCheck.front());
-    assert(orbEntry->pol == enums::Rambus || orbEntry->pol == enums::RambusTagProbOpt);
+    assert(orbEntry->pol == enums::Rambus);
     assert(orbEntry->validEntry);
-    findInORB(orbEntry->owPkt->getAddr());
     assert(orbEntry->state == tagCheck);
     assert(!orbEntry->issued);
 
@@ -440,6 +450,7 @@ PolicyManager::processTagCheckEvent()
     }
 
     tagCheckPktPtr->isTagCheck = true;
+    tagCheckPktPtr->isLocMem = true;
     tagCheckPktPtr->owIsRead = orbEntry->owPkt->isRead();
     tagCheckPktPtr->isHit = orbEntry->isHit;
     tagCheckPktPtr->isDirty = orbEntry->prevDirty;
@@ -451,24 +462,23 @@ PolicyManager::processTagCheckEvent()
         tagCheckPktPtr->isHit = alwaysHit;
     }
 
-    if (tagCheckPktPtr->owIsRead && !tagCheckPktPtr->isHit) {
+    if (tagCheckPktPtr->owIsRead && !tagCheckPktPtr->isHit && !tagCheckPktPtr->isDirty) {
+        assert(tagCheckPktPtr->dirtyLineAddr == -1);
+    }
 
-        if (!tagCheckPktPtr->isDirty) {
-            assert(tagCheckPktPtr->dirtyLineAddr == -1);
-        } else {
-            assert(tagCheckPktPtr->dirtyLineAddr != -1);
-        }
+    if (tagCheckPktPtr->owIsRead && !tagCheckPktPtr->isHit && tagCheckPktPtr->isDirty) {
+        assert(tagCheckPktPtr->dirtyLineAddr != -1);
     }
 
     if (locReqPort.sendTimingReq(tagCheckPktPtr)) {
-        DPRINTF(PolicyManager, "Tag check req sent for adr: %lld\n", tagCheckPktPtr->getAddr());
+        DPRINTF(PolicyManager, "tag check req sent for adr: %lld\n", tagCheckPktPtr->getAddr());
         orbEntry->state = waitingTCtag;
         orbEntry->issued = true;
         orbEntry->tagCheckIssued = curTick();
         pktTagCheck.pop_front();
         polManStats.sentTagCheckPort++;
     } else {
-        DPRINTF(PolicyManager, "Sending tag check failed for adr: %lld\n", tagCheckPktPtr->getAddr());
+        DPRINTF(PolicyManager, "sending tag check failed for adr: %lld\n", tagCheckPktPtr->getAddr());
         retryTagCheck = true;
         delete tagCheckPktPtr;
         polManStats.failedTagCheckPort++;
@@ -493,7 +503,7 @@ PolicyManager::processLocMemReadEvent()
     PacketPtr rdLocMemPkt = getPacket(pktLocMemRead.front(),
                                    blockSize,
                                    MemCmd::ReadReq);
-
+    rdLocMemPkt->isLocMem = true;
     if (locReqPort.sendTimingReq(rdLocMemPkt)) {
         DPRINTF(PolicyManager, "loc mem read is sent : %lld--> %d, %d, %d, %d, %d, %d\n", rdLocMemPkt->getAddr(), ORB.size(), pktLocMemRead.size(),
         pktLocMemWrite.size(), pktFarMemRead.size(), pktFarMemWrite.size(), CRB.size());
@@ -519,7 +529,7 @@ PolicyManager::processLocMemWriteEvent()
 {
     // sanity check for the chosen packet
     auto orbEntry = ORB.at(pktLocMemWrite.front());
-    DPRINTF(PolicyManager, "loc mem write START : %lld--> %d, %d, %d, %d, %d, %d, %d:\n", orbEntry->owPkt->getAddr(), ORB.size(), pktLocMemRead.size(),
+    DPRINTF(PolicyManager, "loc mem read START : %lld--> %d, %d, %d, %d, %d, %d, %d:\n", orbEntry->owPkt->getAddr(), ORB.size(), pktLocMemRead.size(),
         pktLocMemWrite.size(), pktFarMemRead.size(), pktFarMemWrite.size(), CRB.size(), orbEntry->state);
     assert(orbEntry->validEntry);
     assert(orbEntry->state == locMemWrite);
@@ -528,6 +538,7 @@ PolicyManager::processLocMemWriteEvent()
     PacketPtr wrLocMemPkt = getPacket(pktLocMemWrite.front(),
                                    blockSize,
                                    MemCmd::WriteReq);
+    wrLocMemPkt->isLocMem = true;
     assert(!wrLocMemPkt->isTagCheck);
 
     if (locReqPort.sendTimingReq(wrLocMemPkt)) {
@@ -626,26 +637,11 @@ PolicyManager::locMemRecvTimingResp(PacketPtr pkt)
 {
     DPRINTF(PolicyManager, "locMemRecvTimingResp : %d: %s\n", pkt->getAddr(), pkt->cmdString());
 
-    // either read miss dirty data,
-    // or read miss clean FB data,
-    // or stall and send from FB
-    if ((locMemPolicy == enums::Rambus || locMemPolicy == enums::RambusTagProbOpt)
-        && !pkt->isTagCheck && pkt->hasDirtyData) {
+    if (locMemPolicy == enums::Rambus && !pkt->isTagCheck && pkt->hasDirtyData) {
         DPRINTF(PolicyManager, "locMemRecvTimingResp: rd miss data async %d:\n", pkt->getAddr());
         assert(pkt->owIsRead);
         assert(!pkt->isHit);
         handleDirtyCacheLine(pkt->dirtyLineAddr);
-        if (pkt->isDirty && locMemPolicy == enums::RambusTagProbOpt) {
-            auto orbEntry = ORB.at(pkt->getAddr());
-            assert(!orbEntry->rcvdLocRdResp);
-            orbEntry->rcvdLocRdResp = true;
-            if (orbEntry->rcvdLocRdResp && orbEntry->rcvdFarRdResp) {
-                orbEntry->state = locMemWrite;
-                orbEntry->locWrEntered = curTick();
-                orbEntry->issued = false;
-                handleNextState(orbEntry);
-            }
-        }
         delete pkt;
         return true;
     }
@@ -659,13 +655,13 @@ PolicyManager::locMemRecvTimingResp(PacketPtr pkt)
 
     if(pkt->isTagCheck) {
 
-        assert(orbEntry->pol == enums::Rambus || orbEntry->pol == enums::RambusTagProbOpt);
+        assert(orbEntry->pol == enums::Rambus);
         assert(orbEntry->state == waitingTCtag);
 
         if (pkt->hasDirtyData) {
             assert(orbEntry->owPkt->isRead());
             assert(!orbEntry->isHit);
-            if (!orbEntry->prevDirty) { // rd miss clean with FB dirty data
+            if (!orbEntry->prevDirty) { // clean
                 assert(orbEntry->dirtyLineAddr == -1);
                 assert(!orbEntry->handleDirtyLine);
                 orbEntry->handleDirtyLine = true;
@@ -678,14 +674,7 @@ PolicyManager::locMemRecvTimingResp(PacketPtr pkt)
 
         // Rd Miss Dirty
         if (orbEntry->owPkt->isRead() && !orbEntry->isHit && orbEntry->prevDirty) {
-            if (locMemPolicy == enums::Rambus) {
-                // This assert is true only for Rambus policy.
-                // for RambusTagProbOpt it can be either true or false,
-                // since a Rd MD TC packet may or may not be probed
-                // and will carry a dirty flag or not. If it is probed,
-                // this flag will be set later! not when TC is sent!
-                assert(pkt->hasDirtyData);
-            }
+            assert(pkt->hasDirtyData);
             assert(orbEntry->handleDirtyLine);
             assert(orbEntry->dirtyLineAddr != -1);
         }
@@ -714,7 +703,7 @@ PolicyManager::locMemRecvTimingResp(PacketPtr pkt)
                     orbEntry->locRdExit = curTick();
             }
 
-            if (orbEntry->pol == enums::Rambus || orbEntry->pol == enums::RambusTagProbOpt) {
+            if (orbEntry->pol == enums::Rambus) {
                 assert(orbEntry->state == waitingLocMemReadResp);
                 assert(orbEntry->isHit);
                 assert(!pkt->hasDirtyData);
@@ -741,7 +730,7 @@ PolicyManager::locMemRecvTimingResp(PacketPtr pkt)
                 orbEntry->locWrExit = curTick();
             }
 
-            if (orbEntry->pol == enums::Rambus || orbEntry->pol == enums::RambusTagProbOpt) {
+            if (orbEntry->pol == enums::Rambus) {
                 if (orbEntry->state == waitingLocMemWriteResp) {
                     assert(orbEntry->owPkt->isRead());
                     assert(!orbEntry->isHit);
@@ -782,6 +771,7 @@ PolicyManager::farMemRecvTimingResp(PacketPtr pkt)
     DPRINTF(PolicyManager, "farMemRecvTimingResp : %lld , %s \n", pkt->getAddr(), pkt->cmdString());
 
     if (pkt->isRead()) {
+
         auto orbEntry = ORB.at(pkt->getAddr());
 
         DPRINTF(PolicyManager, "farMemRecvTimingResp : continuing to far read resp: %d\n",
@@ -789,12 +779,6 @@ PolicyManager::farMemRecvTimingResp(PacketPtr pkt)
 
         assert(orbEntry->state == waitingFarMemReadResp);
 
-        if (locMemPolicy == enums::RambusTagProbOpt && 
-            !orbEntry->isHit && orbEntry->prevDirty) {
-            assert(!orbEntry->rcvdFarRdResp);
-            orbEntry->rcvdFarRdResp = true;
-        }
-
         orbEntry->farRdExit = curTick();
 
         // IMPORTANT:
@@ -824,7 +808,6 @@ void
 PolicyManager::locMemRecvReqRetry()
 {
     // assert(retryLocMemRead || retryLocMemWrite);
-    DPRINTF(PolicyManager, "locMemRecvReqRetry start: %d, %d , %d \n", retryTagCheck, retryLocMemRead, retryLocMemWrite);
     bool schedTC = false;
     bool schedRd = false;
     bool schedWr = false;
@@ -832,7 +815,7 @@ PolicyManager::locMemRecvReqRetry()
     if (retryTagCheck) {
 
         if (!tagCheckEvent.scheduled() && !pktTagCheck.empty()) {
-            assert(locMemPolicy == enums::Rambus || locMemPolicy == enums::RambusTagProbOpt);
+            assert(locMemPolicy == enums::Rambus);
             schedule(tagCheckEvent, curTick());
         }
         retryTagCheck = false;
@@ -860,7 +843,7 @@ PolicyManager::locMemRecvReqRetry()
 
             // TODO: there are cases where none of retryLocMemRead and retryLocMemWrite
             // are true, yet locMemRecvReqRetry() is called. I should fix this later.
-            if ((locMemPolicy == enums::Rambus || locMemPolicy == enums::RambusTagProbOpt) && !tagCheckEvent.scheduled() && !pktTagCheck.empty()) {
+            if (locMemPolicy == enums::Rambus && !tagCheckEvent.scheduled() && !pktTagCheck.empty()) {
                 schedule(tagCheckEvent, curTick());
             }
             if (!locMemReadEvent.scheduled() && !pktLocMemRead.empty()) {
@@ -871,7 +854,7 @@ PolicyManager::locMemRecvReqRetry()
             }
     }
 
-    DPRINTF(PolicyManager, "locMemRecvReqRetry end: %d, %d , %d \n", schedTC, schedRd, schedWr);
+    DPRINTF(PolicyManager, "locMemRecvReqRetry: %d, %d , %d \n", schedTC, schedRd, schedWr);
 }
 
 void
@@ -999,9 +982,6 @@ PolicyManager::setNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1127,9 +1107,6 @@ PolicyManager::setNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1246,9 +1223,6 @@ PolicyManager::setNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1378,9 +1352,6 @@ PolicyManager::setNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1418,136 +1389,6 @@ PolicyManager::setNextState(reqBufferEntry* orbEntry)
             // do nothing
             return;
     }
-
-    ////////////////////////////////////////////////////////////////////////////////////////////////////////////////
-    // RambusTagProbOpt
-    // start --> read tag
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == start) {
-            orbEntry->state = tagCheck;
-            orbEntry->tagCheckEntered = curTick();
-            return;
-    }
-
-    // tag ready
-    // read && hit --> wait for data
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingTCtag &&
-        orbEntry->owPkt->isRead() &&
-        orbEntry->isHit) {
-            // do nothing
-            return;
-    }
-
-    // tag ready
-    // read && miss --> don't wait for dirty data (MC with FB>0/MD), transition to far read
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingTCtag &&
-        orbEntry->owPkt->isRead() &&
-        !orbEntry->isHit) {
-            orbEntry->state = farMemRead;
-            orbEntry->farRdEntered = curTick();
-            return;
-    }
-
-    // tag ready
-    // write --> done
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingTCtag &&
-        orbEntry->owPkt->isWrite()) {
-            // done, do nothing and return;
-            return;
-    }
-
-    // tag ready && read && hit --> DONE
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingLocMemReadResp) {
-            assert(orbEntry->isHit);
-            assert(orbEntry->owPkt->isRead());
-            // done
-            // do nothing
-            return;
-    }
-
-    // tag ready && read && hit --> DONE
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == locRdRespReady) {
-            assert(orbEntry->isHit);
-            assert(orbEntry->owPkt->isRead());
-            // done
-            // do nothing
-            return;
-    }
-
-    // far read resp ready && read && miss --> loc write
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingFarMemReadResp) {
-
-            assert(orbEntry->owPkt->isRead());
-            assert(!orbEntry->isHit);
-
-            PacketPtr copyOwPkt = new Packet(orbEntry->owPkt,
-                                             false,
-                                             orbEntry->owPkt->isRead());
-
-            accessAndRespond(orbEntry->owPkt,
-                             frontendLatency + backendLatency + backendLatency);
-            ORB.at(copyOwPkt->getAddr()) = new reqBufferEntry(
-                                                orbEntry->validEntry,
-                                                orbEntry->arrivalTick,
-                                                orbEntry->tagDC,
-                                                orbEntry->indexDC,
-                                                orbEntry->wayNum,
-                                                copyOwPkt,
-                                                orbEntry->pol,
-                                                orbEntry->state,
-                                                orbEntry->issued,
-                                                orbEntry->isHit,
-                                                orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
-                                                orbEntry->dirtyLineAddr,
-                                                orbEntry->handleDirtyLine,
-                                                orbEntry->tagCheckEntered,
-                                                orbEntry->tagCheckIssued,
-                                                orbEntry->tagCheckExit,
-                                                orbEntry->locRdEntered,
-                                                orbEntry->locRdIssued,
-                                                orbEntry->locRdExit,
-                                                orbEntry->locWrEntered,
-                                                orbEntry->locWrIssued,
-                                                orbEntry->locWrExit,
-                                                orbEntry->farRdEntered,
-                                                orbEntry->farRdIssued,
-                                                orbEntry->farRdExit);
-            delete orbEntry;
-
-            orbEntry = ORB.at(copyOwPkt->getAddr());
-
-            polManStats.totPktRespTime += ((curTick() - orbEntry->arrivalTick)/1000);
-            polManStats.totPktRespTimeRd += ((curTick() - orbEntry->arrivalTick)/1000);
-
-            if (orbEntry->prevDirty && orbEntry->rcvdLocRdResp && orbEntry->rcvdFarRdResp) {
-                orbEntry->state = locMemWrite;
-                orbEntry->locWrEntered = curTick();
-            } else if (!orbEntry->prevDirty) {
-                orbEntry->state = locMemWrite;
-                orbEntry->locWrEntered = curTick();
-            }
-
-            return;
-    }
-
-    // loc write received
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingLocMemWriteResp) {
-            assert(orbEntry->owPkt->isRead());
-            assert(!orbEntry->isHit);
-            // done
-            // do nothing
-            return;
-    }
 }
 
 void
@@ -1597,9 +1438,6 @@ PolicyManager::handleNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1717,9 +1555,6 @@ PolicyManager::handleNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1834,9 +1669,6 @@ PolicyManager::handleNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -1960,9 +1792,6 @@ PolicyManager::handleNextState(reqBufferEntry* orbEntry)
                                                 orbEntry->issued,
                                                 orbEntry->isHit,
                                                 orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
                                                 orbEntry->dirtyLineAddr,
                                                 orbEntry->handleDirtyLine,
                                                 orbEntry->tagCheckEntered,
@@ -2050,144 +1879,6 @@ PolicyManager::handleNextState(reqBufferEntry* orbEntry)
 
             return;
     }
-
-    ////////////////////////////////////////////////////////////////////////
-    // RambusTagProbOpt
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == tagCheck) {
-
-        pktTagCheck.push_back(orbEntry->owPkt->getAddr());
-
-        polManStats.avgTagCheckQLenEnq = pktTagCheck.size();
-
-        if (!tagCheckEvent.scheduled() && !retryTagCheck) {
-            schedule(tagCheckEvent, curTick());
-        }
-        return;
-    }
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == waitingLocMemReadResp) {
-            return;
-    }
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == locRdRespReady) {
-            assert(orbEntry->owPkt->isRead());
-            assert(orbEntry->isHit);
-            // DONE
-            // send the respond to the requestor
-
-            PacketPtr copyOwPkt = new Packet(orbEntry->owPkt,
-                                             false,
-                                             orbEntry->owPkt->isRead());
-
-            accessAndRespond(orbEntry->owPkt,
-                             frontendLatency + backendLatency);
-
-            ORB.at(copyOwPkt->getAddr()) = new reqBufferEntry(
-                                                orbEntry->validEntry,
-                                                orbEntry->arrivalTick,
-                                                orbEntry->tagDC,
-                                                orbEntry->indexDC,
-                                                orbEntry->wayNum,
-                                                copyOwPkt,
-                                                orbEntry->pol,
-                                                orbEntry->state,
-                                                orbEntry->issued,
-                                                orbEntry->isHit,
-                                                orbEntry->conflict,
-                                                orbEntry->prevDirty,
-                                                orbEntry->rcvdLocRdResp,
-                                                orbEntry->rcvdFarRdResp,
-                                                orbEntry->dirtyLineAddr,
-                                                orbEntry->handleDirtyLine,
-                                                orbEntry->tagCheckEntered,
-                                                orbEntry->tagCheckIssued,
-                                                orbEntry->tagCheckExit,
-                                                orbEntry->locRdEntered,
-                                                orbEntry->locRdIssued,
-                                                orbEntry->locRdExit,
-                                                orbEntry->locWrEntered,
-                                                orbEntry->locWrIssued,
-                                                orbEntry->locWrExit,
-                                                orbEntry->farRdEntered,
-                                                orbEntry->farRdIssued,
-                                                orbEntry->farRdExit);
-            delete orbEntry;
-
-            orbEntry = ORB.at(copyOwPkt->getAddr());
-
-            polManStats.totPktRespTime += ((curTick() - orbEntry->arrivalTick)/1000);
-            polManStats.totPktRespTimeRd += ((curTick() - orbEntry->arrivalTick)/1000);
-
-            // clear ORB
-            resumeConflictingReq(orbEntry);
-
-            return;
-    }
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->owPkt->isWrite()) {
-            // DONE
-            // respond for writes is already sent to the requestor.
-            // clear ORB
-            assert(orbEntry->state == waitingTCtag);
-
-            resumeConflictingReq(orbEntry);
-
-            return;
-    }
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == farMemRead) {
-
-            assert(orbEntry->owPkt->isRead());
-            assert(!orbEntry->isHit);
-
-            // do a read from far mem
-            pktFarMemRead.push_back(orbEntry->owPkt->getAddr());
-
-            polManStats.avgFarRdQLenEnq = pktFarMemRead.size();
-
-            if (!farMemReadEvent.scheduled() && !retryFarMemRead) {
-                schedule(farMemReadEvent, curTick());
-            }
-            return;
-
-    }
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        orbEntry->state == locMemWrite) {
-
-            assert(orbEntry->owPkt->isRead());
-            assert(!orbEntry->isHit);
-
-            pktLocMemWrite.push_back(orbEntry->owPkt->getAddr());
-
-            polManStats.avgLocWrQLenEnq = pktLocMemWrite.size();
-
-
-            if (!locMemWriteEvent.scheduled() && !retryLocMemWrite) {
-                schedule(locMemWriteEvent, curTick());
-            }
-            return;
-
-    }
-
-    if (orbEntry->pol == enums::RambusTagProbOpt &&
-        // orbEntry->owPkt->isRead() &&
-        // !orbEntry->isHit &&
-        orbEntry->state == waitingLocMemWriteResp) {
-            // DONE
-            // clear ORB
-            assert(orbEntry->owPkt->isRead());
-            assert(!orbEntry->isHit);
-            resumeConflictingReq(orbEntry);
-
-            return;
-    }
 }
 
 void
@@ -2209,7 +1900,6 @@ PolicyManager::handleRequestorPkt(PacketPtr pkt)
                                 pkt,
                                 locMemPolicy, start,
                                 false, false, false,
-                                false, false, false,
                                 -1, false,
                                 MaxTick, MaxTick, MaxTick,
                                 MaxTick, MaxTick, MaxTick,
@@ -2264,9 +1954,6 @@ PolicyManager::handleRequestorPkt(PacketPtr pkt)
                                             orbEntry->issued,
                                             orbEntry->isHit,
                                             orbEntry->conflict,
-                                            orbEntry->prevDirty,
-                                            orbEntry->rcvdLocRdResp,
-                                            orbEntry->rcvdFarRdResp,
                                             orbEntry->dirtyLineAddr,
                                             orbEntry->handleDirtyLine,
                                             orbEntry->tagCheckEntered,
@@ -2342,14 +2029,12 @@ PolicyManager::checkConflictInORB(PacketPtr pkt)
             sameIndex.push_back(e->first);
         }
     }
-
     if (sameIndex.size() == assoc) {
         for (int i=0; i<assoc; i++) {
             ORB.at(sameIndex.at(i))->conflict = true;
         }
         return true;
     }
-
     return false;
 }
 
@@ -2777,7 +2462,7 @@ PolicyManager::handleDirtyCacheLine(Addr dirtyLineAddr)
 void
 PolicyManager::logStatsPolMan(reqBufferEntry* orbEntry)
 {
-    if (locMemPolicy == enums::Rambus || locMemPolicy == enums::RambusTagProbOpt) {
+    if (locMemPolicy == enums::Rambus) {
         assert(orbEntry->arrivalTick != MaxTick);
         assert(orbEntry->tagCheckEntered != MaxTick);
         assert(orbEntry->tagCheckExit != MaxTick);
@@ -2790,15 +2475,6 @@ PolicyManager::logStatsPolMan(reqBufferEntry* orbEntry)
             polManStats.totPktLifeTimeRd += ((curTick() - orbEntry->arrivalTick)/1000);
             polManStats.totPktORBTimeRd += ((curTick() - orbEntry->tagCheckEntered)/1000);
             polManStats.totTimeTagCheckResRd += ((orbEntry->tagCheckExit - orbEntry->tagCheckEntered)/1000);
-            
-            if (orbEntry->isHit) {
-                polManStats.totTimeTagCheckResRdH += ((orbEntry->tagCheckExit - orbEntry->tagCheckEntered)/1000);
-            } else if (!orbEntry->isHit && !orbEntry->prevDirty) {
-                polManStats.totTimeTagCheckResRdMC += ((orbEntry->tagCheckExit - orbEntry->tagCheckEntered)/1000);
-            } else if (!orbEntry->isHit && orbEntry->prevDirty) {
-                polManStats.totTimeTagCheckResRdMD += ((orbEntry->tagCheckExit - orbEntry->tagCheckEntered)/1000);
-            }
-        
         } else {
             polManStats.totPktRespTime += ((curTick() - orbEntry->arrivalTick)/1000);
             polManStats.totPktRespTimeWr += ((curTick() - orbEntry->arrivalTick)/1000);
@@ -3046,9 +2722,6 @@ PolicyManager::PolicyManagerStats::PolicyManagerStats(PolicyManager &_polMan)
     ADD_STAT(totTimeTagCheckRes,  statistics::units::Tick::get(), "stat"),
     ADD_STAT(totTimeTagCheckResRd,  statistics::units::Tick::get(), "stat"),
     ADD_STAT(totTimeTagCheckResWr,  statistics::units::Tick::get(), "stat"),
-    ADD_STAT(totTimeTagCheckResRdH,  statistics::units::Tick::get(), "stat"),
-    ADD_STAT(totTimeTagCheckResRdMC,  statistics::units::Tick::get(), "stat"),
-    ADD_STAT(totTimeTagCheckResRdMD,  statistics::units::Tick::get(), "stat"),
     ADD_STAT(totTimeInLocRead,  statistics::units::Tick::get(), "stat"),
     ADD_STAT(totTimeInLocWrite,  statistics::units::Tick::get(), "stat"),
     ADD_STAT(totTimeInFarRead,  statistics::units::Tick::get(), "stat"),
@@ -3080,12 +2753,6 @@ PolicyManager::PolicyManagerStats::PolicyManagerStats(PolicyManager &_polMan)
                 statistics::units::Tick, statistics::units::Count>::get(), "stat"),
     ADD_STAT(avgTimeTagCheckResWr,  statistics::units::Rate<
                 statistics::units::Tick, statistics::units::Count>::get(), "stat"),
-    ADD_STAT(avgTimeTagCheckResRdH,  statistics::units::Rate<
-                statistics::units::Tick, statistics::units::Count>::get(), "stat"),
-    ADD_STAT(avgTimeTagCheckResRdMC,  statistics::units::Rate<
-                statistics::units::Tick, statistics::units::Count>::get(), "stat"),
-    ADD_STAT(avgTimeTagCheckResRdMD,  statistics::units::Rate<
-                statistics::units::Tick, statistics::units::Count>::get(), "stat"),
     ADD_STAT(avgTimeInLocRead,  statistics::units::Rate<
                 statistics::units::Tick, statistics::units::Count>::get(), "stat"),
     ADD_STAT(avgTimeInLocWrite,  statistics::units::Rate<
@@ -3151,9 +2818,6 @@ PolicyManager::PolicyManagerStats::regStats()
     avgTimeTagCheckRes.precision(2);
     avgTimeTagCheckResRd.precision(2);
     avgTimeTagCheckResWr.precision(2);
-    avgTimeTagCheckResRdH.precision(2);
-    avgTimeTagCheckResRdMC.precision(2);
-    avgTimeTagCheckResRdMD.precision(2);
     avgTimeInLocRead.precision(2);
     avgTimeInLocWrite.precision(2);
     avgTimeInFarRead.precision(2);
@@ -3187,7 +2851,7 @@ PolicyManager::PolicyManagerStats::regStats()
     avgPktRespTimeRd = (totPktRespTimeRd) / (readReqs);
     avgPktRespTimeWr = (totPktRespTimeWr) / (writeReqs);
 
-    if (polMan.locMemPolicy == enums::Rambus || polMan.locMemPolicy == enums::RambusTagProbOpt) {
+    if (polMan.locMemPolicy == enums::Rambus) {
         avgTimeTagCheckRes = (totTimeTagCheckRes) / (readReqs + writeReqs);
         avgTimeInLocRead = (totTimeInLocRead) / (numRdHit);
     } else {
@@ -3198,10 +2862,6 @@ PolicyManager::PolicyManagerStats::regStats()
     avgTimeTagCheckResRd = (totTimeTagCheckResRd) / (readReqs);
     avgTimeTagCheckResWr = (totTimeTagCheckResWr) / (writeReqs);
 
-    avgTimeTagCheckResRdH = (totTimeTagCheckResRdH) / (numRdHit);
-    avgTimeTagCheckResRdMC = (totTimeTagCheckResRdMC) / (numRdMissClean);
-    avgTimeTagCheckResRdMD = (totTimeTagCheckResRdMD) / (numRdMissDirty);
-
 
     avgTimeInLocWrite = (totTimeInLocWrite) / (numRdMissClean + numRdMissDirty + writeReqs);
     avgTimeInFarRead = (totTimeInFarRead) / (numRdMissClean + numRdMissDirty);
@@ -3316,7 +2976,6 @@ PolicyManager::unserialize(CheckpointIn &cp)
             }
         }
     }
-    //std::cout << "Counters: " << num_entries << " , " << countInvalid << " , " << countValid << "\n";
 }
 
 int
@@ -3341,5 +3000,5 @@ PolicyManager::recvReadFlushBuffer(Addr addr)
     return false;
 }
 
-} // namespace memory 
+} // namespace memory
 } // namespace gem5
diff --git a/src/mem/policy_manager.hh b/src/mem/policy_manager.hh
index 9d4b5c6f61..eb34842da9 100644
--- a/src/mem/policy_manager.hh
+++ b/src/mem/policy_manager.hh
@@ -112,7 +112,7 @@ class PolicyManager : public AbstractMemory
     unsigned locBurstSize;
     unsigned farBurstSize;
 
-    enums::Policy locMemPolicy;
+    // enums::Policy locMemPolicy;
 
     /**
      * The following are basic design parameters of the unified
@@ -233,18 +233,18 @@ class PolicyManager : public AbstractMemory
         bool issued;
         bool isHit;
         bool conflict;
-        bool prevDirty;
+        //bool fbDirtyData = false;
+
+        Addr dirtyLineAddr;
+        bool handleDirtyLine;
+        bool prevDirty = false;
         // rcvdRdResp is only used for read misses,
         // since the data response from a tag check 
         // may be received too late 
         // (after rd from far mem & write to loc).
         // Note: writes responds are  very quick, 
         // just an ack with a frontend latency only.
-        bool rcvdLocRdResp;
-        bool rcvdFarRdResp;
-        Addr dirtyLineAddr;
-        bool handleDirtyLine;
-
+        bool rcvdRdResp = false;
 
         // recording the tick when the req transitions into a new stats.
         // The subtract between each two consecutive states entrance ticks,
@@ -270,7 +270,6 @@ class PolicyManager : public AbstractMemory
           PacketPtr _owPkt,
           enums::Policy _pol, reqState _state,
           bool _issued, bool _isHit, bool _conflict,
-          bool _prevDirty, bool _rcvdLocRdResp, bool _rcvdFarRdResp,
           Addr _dirtyLineAddr, bool _handleDirtyLine,
           Tick _tagCheckEntered, Tick _tagCheckIssued, Tick _tagCheckExit,
           Tick _locRdEntered, Tick _locRdIssued, Tick _locRdExit,
@@ -282,7 +281,6 @@ class PolicyManager : public AbstractMemory
         owPkt( _owPkt),
         pol(_pol), state(_state),
         issued(_issued), isHit(_isHit), conflict(_conflict),
-        prevDirty(_prevDirty), rcvdLocRdResp(_rcvdLocRdResp), rcvdFarRdResp(_rcvdFarRdResp),
         dirtyLineAddr(_dirtyLineAddr), handleDirtyLine(_handleDirtyLine),
         tagCheckEntered(_tagCheckEntered), tagCheckIssued(_tagCheckIssued), tagCheckExit(_tagCheckExit),
         locRdEntered(_locRdEntered), locRdIssued(_locRdIssued), locRdExit(_locRdExit),
@@ -381,6 +379,7 @@ class PolicyManager : public AbstractMemory
     PacketPtr getPacket(Addr addr, unsigned size, const MemCmd& cmd, Request::FlagsType flags = 0);
     Tick accessLatency();
     bool findInORB(Addr addr);
+    unsigned findDupInORB(Addr addr);
 
     unsigned countTagCheckInORB();
     unsigned countLocRdInORB();
@@ -484,9 +483,6 @@ class PolicyManager : public AbstractMemory
       statistics::Scalar totTimeTagCheckRes;
       statistics::Scalar totTimeTagCheckResRd;
       statistics::Scalar totTimeTagCheckResWr;
-      statistics::Scalar totTimeTagCheckResRdH;
-      statistics::Scalar totTimeTagCheckResRdMC;
-      statistics::Scalar totTimeTagCheckResRdMD;
       statistics::Scalar totTimeInLocRead;
       statistics::Scalar totTimeInLocWrite;
       statistics::Scalar totTimeInFarRead;
@@ -506,9 +502,6 @@ class PolicyManager : public AbstractMemory
       statistics::Formula avgTimeTagCheckRes;
       statistics::Formula avgTimeTagCheckResRd;
       statistics::Formula avgTimeTagCheckResWr;
-      statistics::Formula avgTimeTagCheckResRdH;
-      statistics::Formula avgTimeTagCheckResRdMC;
-      statistics::Formula avgTimeTagCheckResRdMD;
       statistics::Formula avgTimeInLocRead;
       statistics::Formula avgTimeInLocWrite;
       statistics::Formula avgTimeInFarRead;
diff --git a/src/python/gem5/simulate/exit_event.py b/src/python/gem5/simulate/exit_event.py
index 8ee18fb371..94ecd94d45 100644
--- a/src/python/gem5/simulate/exit_event.py
+++ b/src/python/gem5/simulate/exit_event.py
@@ -76,8 +76,6 @@ def translate_exit_status(cls, exit_string: str) -> "ExitEvent":
             return ExitEvent.EXIT
         elif exit_string == "exiting with last active thread context":
             return ExitEvent.EXIT
-        elif exit_string == "End of DRTrace":
-            return ExitEvent.EXIT
         elif exit_string == "simulate() limit reached":
             return ExitEvent.MAX_TICK
         elif exit_string == "Tick exit reached":
diff --git a/traffGen_def.py b/traffGen_def.py
index dd88a899c4..19468d670e 100644
--- a/traffGen_def.py
+++ b/traffGen_def.py
@@ -55,7 +55,7 @@
 
 system.loc_mem_ctrl = MemCtrl()
 system.loc_mem_ctrl.dram = TDRAM_32(range=AddrRange('3GiB'), in_addr_map=False, null=True)
-system.mem_ctrl.loc_mem_policy = 'Rambus'
+system.mem_ctrl.loc_mem_policy = 'RambusTagProbOpt'
 
 system.mem_ctrl.loc_mem = system.loc_mem_ctrl.dram
 system.loc_mem_ctrl.static_frontend_latency = "1ns"