resources: updated pannotia documentation for compile commands (#15)

The docker command for compiling pannotia tests needs to have visibility
to m5ops.

README.md

@@ -380,7 +380,7 @@ git clone https://github.com/gem5/gem5-resources.git
 
 ```
 cd src/gpu/square
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make gfx8-apu
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu make gfx8-apu
 ```
 
 The compiled binary can be found in `src/gpu/square/bin`
@@ -409,7 +409,7 @@ To compile:
 
 ```
 cd src/gpu/hsa-agent-pkt
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make gfx8-apu
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu make gfx8-apu
 ```
 
 The compiled binary can be found in `src/gpu/hsa-agent-pkt/bin`
@@ -431,7 +431,7 @@ Certain apps aren't included due to complexities with either ROCm or Docker
 
 ```
 cd src/gpu/hip-samples
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu make
 ```
 
 Individual programs can be made by specifying the name of the program
@@ -469,7 +469,7 @@ and the other command-line arguments for use with heterosync.
 ## Compilation
 ```
 cd src/gpu/heterosync
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make release-gfx8
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu make release-gfx8
 ```
 
 The release-gfx8 target builds for gfx801, a GCN3-based APU, and gfx803, a
@@ -489,7 +489,7 @@ provided is for use with the gpu-compute model of gem5.
 ## Compilation and Running
 ```
 cd src/gpu/lulesh
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu make
 ```
 
 By default, the Makefile builds for gfx801, and is placed in the 
@@ -500,7 +500,7 @@ architecture. To build GCN3_X86:
 
 ```
 # Working directory is your gem5 directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
 ```
 
 The following command shows how to run lulesh
@@ -513,7 +513,7 @@ are equivalent to `--options="1.0e-2 10"`.
 
 ```
 # Assuming gem5 and gem5-resources are in your working directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --mem-size=8GB --benchmark-root=gem5-resources/src/gpu/lulesh/bin -clulesh
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --mem-size=8GB --benchmark-root=gem5-resources/src/gpu/lulesh/bin -clulesh
 ```
 
 ## Pre-built binary
@@ -584,8 +584,8 @@ commands do this by using `-v ${PWD}:${PWD}` in the docker run commands
 
 ```
 cd src/gpu/DNNMark
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu ./setup.sh HIP
-docker run --rm -v ${PWD}:${PWD} -w ${PWD}/build -u $UID:$GID gcr.io/gem5-test/gcn-gpu make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu ./setup.sh HIP
+docker run --rm -v ${PWD}:${PWD} -w ${PWD}/build -u $UID:$GID ghcr.io/gem5/gcn-gpu make
 ```
 
 DNNMark uses MIOpen kernels, which are unable to be compiled on-the-fly in gem5.
@@ -595,7 +595,7 @@ benchmarks for a gfx801 GPU with 4 CUs by default
 To generate the MIOpen kernels:
 ```
 cd src/gpu/DNNMark
-docker run --rm -v ${PWD}:${PWD} -v${PWD}/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} gcr.io/gem5-test/gcn-gpu python3 generate_cachefiles.py cachefiles.csv [--gfx-version={gfx801,gfx803}] [--num-cus=N]
+docker run --rm -v ${PWD}:${PWD} -v${PWD}/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} ghcr.io/gem5/gcn-gpu python3 generate_cachefiles.py cachefiles.csv [--gfx-version={gfx801,gfx803}] [--num-cus=N]
 ```
 
 Due to the large amounts of memory that need to be set up for DNNMark, we have
@@ -614,13 +614,13 @@ GCN3_X86 architecture.
 To build GCN3_X86:
 ```
 # Working directory is your gem5 directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
 ```
 
 To run one of the benchmarks (fwd softmax) in gem5:
 ```
 # Assuming gem5 and gem5-resources are sub-directories of the current directory
-docker run --rm -v ${PWD}:${PWD} -v ${PWD}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} gcr.io/gem5-test/gcn-gpu gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --benchmark-root=gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_softmax -cdnnmark_test_fwd_softmax --options="-config gem5-resources/src/gpu/DNNMark/config_example/softmax_config.dnnmark -mmap gem5-resources/src/gpu/DNNMark/mmap.bin"
+docker run --rm -v ${PWD}:${PWD} -v ${PWD}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} ghcr.io/gem5/gcn-gpu gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --benchmark-root=gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_softmax -cdnnmark_test_fwd_softmax --options="-config gem5-resources/src/gpu/DNNMark/config_example/softmax_config.dnnmark -mmap gem5-resources/src/gpu/DNNMark/mmap.bin"
 ```
 
 
@@ -635,7 +635,7 @@ a sample of the typical memory access patterns of FLAG.
 
 ```
 cd src/gpu/pennant
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu make
 ```
 
 By default, the binary is built for gfx801 and is placed in `src/gpu/pennant/build`.
@@ -648,7 +648,7 @@ command shows how to run the sample `noh`:
 
 ```
 # Assuming gem5 and gem5-resources are in your working directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --benchmark-root=gem5-resources/src/gpu/pennant/build -cpennant --options="gem5-resources/src/gpu/pennant/test/noh/noh.pnt"
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --benchmark-root=gem5-resources/src/gpu/pennant/build -cpennant --options="gem5-resources/src/gpu/pennant/test/noh/noh.pnt"
 ```
 
 The output gets placed in `src/gpu/pennant/test/noh/`, and the file `noh.xy`
@@ -841,7 +841,7 @@ We provide a docker image with a pre-loaded SPARC cross compiler. To use:
 
 ```
 cd src/insttest
-docker run --volume $(pwd):$(pwd) -w $(pwd) --rm gcr.io/gem5-test/sparc64-gnu-cross:latest make
+docker run --volume $(pwd):$(pwd) -w $(pwd) --rm ghcr.io/gem5/sparc64-gnu-cross:latest make
 ```
 
 The compiled binary can be found in `src/insttest/bin`.

src/gpu-fs/README.md

@@ -77,9 +77,9 @@ You can obtain the `vmlinux-5.4.0-105-generic` kernel using the following path f
 It is sometimes useful to build your own application and run in gem5. A docker is provided to allow users to build applications without needing to install ROCm locally. A pre-built docker image is available on gcr.io. This image can be pulled then used to build as follows:
 
 ```sh
-docker pull gcr.io/gem5-test/gpu-fs:latest
+docker pull ghcr.io/gem5/gpu-fs:latest
 cd /path/to/gem5-resources/src/gpu/square
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} gcr.io/gem5-test/gpu-fs:latest bash -c 'make clean; HCC_AMDGPU_TARGET=gfx900 make'
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} ghcr.io/gem5/gpu-fs:latest bash -c 'make clean; HCC_AMDGPU_TARGET=gfx900 make'
 ```
 
 Currently only Vega (gfx900) is supported for full system GPU simulation in gem5. It is therefore required to tell the compiler to build for this ISA using the HCC_AMDGPU_TARGET environment variable. Otherwise, the command to build the application is the same as if you were building locally.
@@ -130,4 +130,4 @@ You need to run something like the following (as root).
 # echo -1 > /proc/sys/kernel/perf_event_paranoid
 ```
 
-- If you encounter `Qemu stderr: qemu-system-x86_64: failed to initialize KVM: Permission denied`, the issue is likely related to permission on /dev/kvm
+- If you encounter `Qemu stderr: qemu-system-x86_64: failed to initialize KVM: Permission denied`, the issue is likely related to permission on /dev/kvm

src/gpu/DNNMark/README.md

@@ -27,8 +27,8 @@ won't be able to link against the library. The example commands do this by using
 `-v ${PWD}:${PWD}` in the docker run commands
 ```
 cd src/gpu/DNNMark
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 ./setup.sh HIP
-docker run --rm -v ${PWD}:${PWD} -w ${PWD}/build -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 ./setup.sh HIP
+docker run --rm -v ${PWD}:${PWD} -w ${PWD}/build -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 make
 ```
 
 DNNMark uses MIOpen kernels, which are unable to be compiled on-the-fly in gem5.
@@ -38,7 +38,7 @@ benchmarks for a gfx801 GPU with 4 CUs by default
 To generate the MIOpen kernels:
 ```
 cd src/gpu/DNNMark
-docker run --rm -v ${PWD}:${PWD} -v${PWD}/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} gcr.io/gem5-test/gcn-gpu:v22-1 python3 generate_cachefiles.py cachefiles.csv [--gfx-version={gfx801,gfx803}] [--num-cus=N]
+docker run --rm -v ${PWD}:${PWD} -v${PWD}/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} ghcr.io/gem5/gcn-gpu:v22-1 python3 generate_cachefiles.py cachefiles.csv [--gfx-version={gfx801,gfx803}] [--num-cus=N]
 ```
 
 Due to the large amounts of memory that need to be set up for DNNMark, we have
@@ -56,13 +56,13 @@ DNNMark is a GPU application, which requires that gem5 is built with the GCN3_X8
 To build GCN3_X86:
 ```
 # Working directory is your gem5 directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
 ```
 
 To run one of the benchmarks (fwd softmax) in gem5:
 ```
 # Assuming gem5 and gem5-resources are sub-directories of the current directory
-docker run --rm -v ${PWD}:${PWD} -v ${PWD}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} gcr.io/gem5-test/gcn-gpu:v22-1 gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --benchmark-root=gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_softmax -cdnnmark_test_fwd_softmax --options="-config gem5-resources/src/gpu/DNNMark/config_example/softmax_config.dnnmark -mmap gem5-resources/src/gpu/DNNMark/mmap.bin"
+docker run --rm -v ${PWD}:${PWD} -v ${PWD}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0 -w ${PWD} ghcr.io/gem5/gcn-gpu:v22-1 gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --benchmark-root=gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_softmax -cdnnmark_test_fwd_softmax --options="-config gem5-resources/src/gpu/DNNMark/config_example/softmax_config.dnnmark -mmap gem5-resources/src/gpu/DNNMark/mmap.bin"
 ```
 
 Information from the original DNNMark README included below.

src/gpu/halo-finder/Dockerfile

@@ -1,4 +1,4 @@
-FROM gcr.io/gem5-test/gcn-gpu:latest
+FROM ghcr.io/gem5/gcn-gpu:latest
 RUN apt-get update && apt-get -y install libopenmpi-dev libomp-dev
 
 ENV HCC_AMDGPU_TARGET="gfx801,gfx803,gfx900"

src/gpu/heterosync/README.md

@@ -21,7 +21,7 @@ command-line arguments for use with heterosync.
 ## Compilation
 ```
 cd src/gpu/heterosync
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 make release-gfx8
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 make release-gfx8
 ```
 
 The release-gfx8 target builds for gfx801, a GCN3-based APU, and gfx803, a
@@ -33,7 +33,7 @@ that are currently unsupported in gem5.
 HeteroSync has multiple applications that can be run (see below).  For example, to run sleepMutex with 10 ld/st per thread, 16 WGs, and 4 iterations of the critical section:
 
 ```
-docker run -u $UID:$GID --volume $(pwd):$(pwd) -w $(pwd) gcr.io/gem5-test/gcn-gpu:v22-1 gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n 3 -c bin/allSyncPrims-1kernel --options="sleepMutex 10 16 4"
+docker run -u $UID:$GID --volume $(pwd):$(pwd) -w $(pwd) ghcr.io/gem5/gcn-gpu:v22-1 gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n 3 -c bin/allSyncPrims-1kernel --options="sleepMutex 10 16 4"
 ```
 
 ## Pre-built binary

src/gpu/hip-samples/README.md

@@ -28,7 +28,7 @@ Compiling the HIP samples, compiling the GCN3_X86 gem5, and running the HIP samp
 
 ```
 cd src/gpu/hip-samples
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 make
 ```
 
 Individual programs can be made by specifying the name of the program

src/gpu/hsa-agent-pkt/README.md

@@ -29,7 +29,7 @@ To compile:
 
 ```
 cd src/gpu/hsa-agent-pkt
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 make gfx8-apu
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 make gfx8-apu
 ```
 
 The compiled binary can be found in `src/gpu/hsa-agent-pkt/bin`

src/gpu/lulesh/README.md

@@ -20,7 +20,7 @@ Compiling LULESH, compiling the GCN3_X86 gem5, and running LULESH on gem5 is dep
 ## Compilation and Running
 ```
 cd src/gpu/lulesh
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 make
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 make
 ```
 
 By default, the makefile builds for gfx801, and is placed in the `src/gpu/lulesh/bin` folder.
@@ -30,7 +30,7 @@ To build GCN3_X86:
 
 ```
 # Working directory is your gem5 directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 scons -sQ -j$(nproc) build/GCN3_X86/gem5.opt
 ```
 
 The following command shows how to run lulesh
@@ -42,7 +42,7 @@ to the run command. The default arguments are equivalent to `--options="1.0e-2 1
 
 ```
 # Assuming gem5 and gem5-resources are in your working directory
-docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu:v22-1 gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --mem-size=8GB --benchmark-root=gem5-resources/src/gpu/lulesh/bin -clulesh
+docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID ghcr.io/gem5/gcn-gpu:v22-1 gem5/build/GCN3_X86/gem5.opt gem5/configs/example/apu_se.py -n3 --mem-size=8GB --benchmark-root=gem5-resources/src/gpu/lulesh/bin -clulesh
 ```
 
 ## Pre-built binary

src/gpu/pannotia/README.md

@@ -10,3 +10,10 @@ shortdoc: >
 ---
 
 This folder and its subfolders contain each of the 9 Pannotia benchmarks (there are 6 folders because Color, and PageRank, SSSP each have 2 versions).  All of these benchmarks have been ported from the prior CUDA and OpenCL variants to HIP, and validated on a Vega-class AMD GPU.  See each application's README for details on how to compile and run them in gem5 using the GCN3 GPU model.
+
+## Compiling m5ops
+To compile the m5ops:
+```
+cd gem5/util/m5
+scons build/x86/out/m5
+```

src/gpu/pannotia/bc/Makefile.gem5-fusion

@@ -8,9 +8,9 @@ HIP_PATH ?= /opt/rocm/hip
 HIPCC = $(HIP_PATH)/bin/hipcc
 
 # these are needed for m5ops
-GEM5_PATH ?= /path/to/gem5
-CFLAGS += -I$(GEM5_PATH)/include -I../graph_parser
-LDFLAGS += -L$(GEM5_PATH)/util/m5/build/x86/out -lm5
+GEM5_ROOT ?= /path/to/gem5
+CFLAGS += -I$(GEM5_ROOT)/include -I../graph_parser
+LDFLAGS += -L$(GEM5_ROOT)/util/m5/build/x86/out -lm5
 
 BIN_DIR ?= ./bin