Single-Source Shortest Path (sssp) is a graph analytics application that is part of the Pannotia benchmark suite. It is designed to calculate the shortest paths between the source vertex and all the other vertices in a graph. The provided version is for use with the gpu-compute model of gem5. Thus, it has been ported from the prior CUDA and OpenCL variants to HIP, and validated on a Vega-class AMD GPU.
Compiling both SSSP variants, compiling the GCN3_X86/Vega_X86 versions of gem5, and running both SSSP variants on gem5 is dependent on the gcn-gpu docker image, util/dockerfiles/gcn-gpu/Dockerfile
on the gem5 stable branch.
SSSP has two variants: csr and ell. To compile the “csr” variant:
cd src/gpu/pannotia/sssp docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu make gem5-fusion
To compile the “ell” variant:
cd src/gpu/pannotia/sssp docker run --rm -v ${PWD}:${PWD} -w ${PWD} -u $UID:$GID gcr.io/gem5-test/gcn-gpu bash -c "export VARIANT=ELL ; make gem5-fusion"
If you use the Makefile.default file instead, the Makefile will generate code designed to run on the real GPU instead. Moreover, note that Makefile.gem5-fusion requires you to set the GEM5_ROOT variable (either on the command line or by modifying the Makefile), because the Pannotia applications have been updated to use m5ops. By default, for both variants the Makefile builds for gfx801 and gfx803, and the binaries are placed in the src/gpu/pannotia/sssp/bin folder. Moreover, by default the VARIANT variable SSSP's Makefile assumes the csr variant is being used, hence why this variable does not need to be set for compiling it.
SSSP is a GPU application, which requires that gem5 is built with the GCN3_X86 (or Vega_X86, although this has been less heavily tested) architecture. The test is run with the GCN3_X86 gem5 variant, compiled using the gcn-gpu docker image:
git clone https://gem5.googlesource.com/public/gem5 cd gem5 docker run -u $UID:$GID --volume $(pwd):$(pwd) -w $(pwd) gcr.io/gem5-test/gcn-gpu:latest scons build/GCN3_X86/gem5.opt -j <num cores>
The following command shows how to run the SSSP csr version:
wget http://dist.gem5.org/dist/develop/datasets/pannotia/bc/1k_128k.gr 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/pannotia/sssp/bin -c sssp_csr.gem5 --options="1k_128k.gr 0"
To run the SSSP ell version:
wget http://dist.gem5.org/dist/develop/datasets/pannotia/bc/1k_128k.gr 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/pannotia/sssp/bin -c sssp_ell.gem5 --options="1k_128k.gr 0"
Note that the datasets from the original Pannotia suite have been uploaded to: http://dist.gem5.org/dist/develop/datasets/pannotia. We recommend you start with the 1k_128k.gr input (http://dist.gem5.org/dist/develop/datasets/pannotia/bc/1k_128k.gr), as this is the smallest input that can be run with SSSP. Note that 1k_128k is not designed for SSSP specifically though -- the above link has larger graphs designed to run with SSSP that you should consider using for larger experiments.
A pre-built binary will be added soon.