---
title: Pannotia SSSP Test
tags:
    - x86
    - amdgpu
layout: default
permalink: resources/pannotia/sssp
shortdoc: >
    Resources to build a disk image with the GCN3 Pannotia SSSP workload.
---

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](https://gem5.googlesource.com/public/gem5/+/refs/heads/stable).

## Compilation and Running

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](https://www.gem5.org/documentation/general_docs/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.

## Compiling GCN3_X86/gem5.opt

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>
```

## Running SSSP on GCN3_X86/gem5.opt

The following command shows how to run the SSSP csr version:

# Assuming gem5 and gem5-resources are in your working directory
```
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:

# Assuming gem5, pannotia (input graphs, see below), and gem5-resources are in your working directory
```
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.

## Pre-built binary

A pre-built binary will be added soon.