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drgpu_entry.py
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drgpu_entry.py
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#!/bin/python3
from gather import *
import argparse
import os
from unit_hunt import *
from dot_graph import *
from suggestions import *
from read_reports import *
from source_code_analysis import add_source_code_nodes
def work(report, dot_graph_name, memoryconfig):
global memory_metrics
read_config(memoryconfig, config)
analysis = Analysis()
# {stat_name: stat, } type:{str: Stat}
all_stats = analysis.all_stats
if dot_graph_name is None:
(_, dot_graph_name) = os.path.split(report.path)
(dot_graph_name, _) = os.path.splitext(dot_graph_name)
# read reports and filter all useful stats
fill_stats(all_stats, report)
if report.source_report_path:
fill_source_report(report, analysis)
hw_tree = Node('Idle')
hw_tree.suffix_label = ' of total cycles'
retireIPC = all_stats.get('retireIPC', None)
if retireIPC:
root_percentage = retireIPC.value / config.quadrants_per_SM
else:
print("Could not get stat retireIPC")
root_percentage = 0
hw_tree.percentage = 1 - root_percentage
hw_tree.prefix_label = get_kernel_name(all_stats['kernel_name'].value) + "\n"
hw_tree.suffix_label = ''
best_possible = 100 * (
1.0 - 1.0 / (np.ceil(all_stats['activewarps_per_activecycle'].value / config.quadrants_per_SM)))
hw_tree.suffix_label += r" (lowest possible: %i%% for %i active warps)" % (
best_possible, all_stats['activewarps_per_activecycle'].value)
max_val = 0
sol_unit = ""
for unit in ['SM', 'L1', 'L2', 'Dram', 'Compute_Memory']:
next_val = all_stats['sol_' + unit.lower()].value
if next_val > max_val:
sol_unit = unit
max_val = next_val
hw_tree.suffix_label += r"\nUtil/SOL: %.2f%% (%s)" % (max_val, sol_unit)
hw_tree.suffix_label += r"\nIssue IPC: %.2f" % (all_stats["issueIPC"].value)
# first level
tmpstats = warp_cant_issue(all_stats)
add_sub_branch(tmpstats, hw_tree, 1)
if report.source_report_path is not None:
add_source_code_nodes(tmpstats, hw_tree, analysis)
# pipe utilization is the subbranch of shadow_pipe_throttle
tmpstats = pipe_utilization(all_stats)
target_node = find_node(hw_tree, "warp_cant_issue_pipe_throttle")
if not target_node:
print("Could not find the target node: warp_cant_issue_pipe_throttle")
else:
add_pipe_throttle_branch(tmpstats, target_node)
# instruction distribution is the subbranch of wait
tmpstats = instruction_distribution(all_stats)
target_node = find_node(hw_tree, "warp_cant_issue_wait")
if not target_node:
print("Could not find the target node: warp_cant_issue_wait")
else:
add_sub_branch(tmpstats, target_node, 1)
# warp_cant_issue_dispatch_stall
tmpstats = cant_dispatch(all_stats)
target_node = find_node(hw_tree, "warp_cant_issue_dispatch")
if not target_node:
print("Could not find the target node: warp_cant_issue_dispatch")
else:
add_sub_branch(tmpstats, target_node, 1)
target_node = find_node(hw_tree, "warp_cant_issue_lg_throttle")
if not target_node:
print("Could not find the target node: warp_cant_issue_lg_throttle")
else:
add_lg_throttle_branch(all_stats, target_node)
# target_node = find_node(hw_tree, "warp_cant_issue_barrier")
# if not target_node:
# print("Could not find the target node: warp_cant_issue_barrier")
# else:
# add_sub_branch(tmpstats, target_node, 1)
# warp_cant_issue_long_scoreboard memory
bottleneck_unit, bottleneck_stats, memory_metrics = long_scoreboard_throughput(all_stats, memory_metrics)
long_scoreboard_node = find_node(hw_tree, "warp_cant_issue_long_scoreboard")
latency_stats = long_scoreboard_latency(all_stats, memory_metrics)
add_sub_branch_for_longscoreboard_latency(latency_stats, long_scoreboard_node, all_stats, memory_metrics)
add_sub_branch_for_longscoreboard_throughput(all_stats, bottleneck_unit, bottleneck_stats, long_scoreboard_node, 1)
shared_mem_stats = common_function_pattern(all_stats, 'shared_ld_(\d+)b_executed')
add_shared_memory_info(all_stats, shared_mem_stats, memory_metrics)
target_node = find_node(hw_tree, "warp_cant_issue_mio_throttle")
add_branch_for_mio_throttle(all_stats, shared_mem_stats, memory_metrics, target_node)
target_node = find_node(hw_tree, "warp_cant_issue_short_scoreboard")
add_branch_for_short_scoreboard(all_stats, shared_mem_stats, memory_metrics, target_node)
# suggestions part
pipe_suggest(hw_tree, all_stats)
barrier_suggest(hw_tree, all_stats)
branch_solving_suggest(hw_tree, all_stats)
dispatch_stall_suggest(hw_tree, all_stats)
drain_suggest(hw_tree, all_stats)
# imc_miss_suggest(hw_tree, all_stats)
lg_credit_throttle_suggest(hw_tree, all_stats)
memory_suggest(hw_tree, all_stats, bottleneck_unit, memory_metrics)
membar_suggest(hw_tree, all_stats)
mio_throttle_suggest(hw_tree, all_stats, shared_mem_stats)
short_scoreboard_suggest(hw_tree, all_stats, shared_mem_stats)
wait_suggestion(hw_tree, all_stats)
build_dot_graph(hw_tree, "dots/" + dot_graph_name)
print("save to dots/" + dot_graph_name + ".svg")
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('-i', '--report-path', metavar='The path of main report.',
required=True, dest='report_path', action='store')
parser.add_argument('-o', '--output', metavar='Set the output file to save decision tree.',
required=False, dest='output', action='store')
parser.add_argument('-s', '--source', metavar='The path of source mapping report from NCU. NCU model only.',
required=False, dest='source', action='store')
parser.add_argument('-c', '--memoryconfig',
metavar='The path of memory config file or only file name in mem_config folder',
required=False, dest='memoryconfig', action='store')
parser.add_argument('-id', '--id',
metavar='The id of the kernel you want to analyze in exported csv files.',
required=False, dest='kernel_id', action='store')
args = parser.parse_args()
report_path = args.report_path
if not args.memoryconfig:
memoryconfig = sys.path[0] + '/mem_config/gtx1650.ini'
print(
"You didn't specify running platform for this report. DrGPU will use gtx1650.ini as default GPU configuration.")
else:
memoryconfig = args.memoryconfig
if not memoryconfig.endswith('.ini'):
memoryconfig += '.ini'
if not memoryconfig.startswith('/'):
memoryconfig = sys.path[0] + "/mem_config/" + memoryconfig
kernel_id = 0
if args.kernel_id:
kernel_id = int(args.kernel_id)
print(report_path)
print(args.source)
report = Report(report_path, args.source, kernel_id)
work(report, args.output, memoryconfig)