d37be2621d
Add the Pannotia benchmarks to the weekly regression suite. These applications do a good job of testing the GPU support for irregular access patterns of various kinds. All inputs have been sized to use relatively small graphs to avoid increasing runtime too much. However, even with small input sizes Pannotia does run for a while. Note that the Pannotia benchmarks also use m5ops in them. Thus, this commit also adds support into the weekly regression for compiling the m5ops (for x86, since that is what the GPU model assumes for the CPU). Change-Id: I1f68b02b38ff24505a2894694b7544977024f8fa Reviewed-on: https://gem5-review.googlesource.com/c/public/gem5/+/51968 Tested-by: kokoro <noreply+kokoro@google.com> Maintainer: Matt Sinclair <mattdsinclair@gmail.com> Reviewed-by: Jason Lowe-Power <power.jg@gmail.com>
311 lines
15 KiB
Bash
Executable File
311 lines
15 KiB
Bash
Executable File
#!/bin/bash
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# Copyright (c) 2021 The Regents of the University of California
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# All Rights Reserved.
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#
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# Redistribution and use in source and binary forms, with or without
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# modification, are permitted provided that the following conditions are
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# met: redistributions of source code must retain the above copyright
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# notice, this list of conditions and the following disclaimer;
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# redistributions in binary form must reproduce the above copyright
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# notice, this list of conditions and the following disclaimer in the
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# documentation and/or other materials provided with the distribution;
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# neither the name of the copyright holders nor the names of its
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# contributors may be used to endorse or promote products derived from
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# this software without specific prior written permission.
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#
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# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
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# "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
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# LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
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# A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT
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# OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL,
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# SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT
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# LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE,
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# DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY
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# THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
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# (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
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# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
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set -e
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set -x
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dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
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gem5_root="${dir}/.."
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# We assume the lone argument is the number of threads. If no argument is
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# given we default to one.
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threads=1
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if [[ $# -gt 0 ]]; then
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threads=$1
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fi
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# Run the gem5 very-long tests.
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docker run -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}"/tests --rm gcr.io/gem5-test/ubuntu-20.04_all-dependencies \
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./main.py run --length very-long -j${threads} -t${threads}
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mkdir -p tests/testing-results
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# GPU weekly tests start here
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# before pulling gem5 resources, make sure it doesn't exist already
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}" gcr.io/gem5-test/gcn-gpu:latest bash -c \
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"rm -rf ${gem5_root}/gem5-resources"
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# delete Pannotia datasets and output files in case a failed regression run left
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# them around
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rm -f coAuthorsDBLP.graph 1k_128k.gr result.out
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# Pull gem5 resources to the root of the gem5 directory -- currently the
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# pre-built binares for LULESH are out-of-date and won't run correctly with
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# ROCm 4.0. In the meantime, we can build the binary as part of this script.
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# Moreover, DNNMark builds a library and thus doesn't have a binary, so we
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# need to build it before we run it.
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# Need to pull this first because HACC's docker requires this path to exist
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git clone -b develop https://gem5.googlesource.com/public/gem5-resources \
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"${gem5_root}/gem5-resources"
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# For the GPU tests we compile and run GCN3_X86 inside a gcn-gpu container.
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# HACC requires setting numerous environment variables to run correctly. To
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# avoid needing to set all of these, we instead build a docker for it, which
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# has all these variables pre-set in its Dockerfile
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# To avoid compiling gem5 multiple times, all GPU benchmarks will use this
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docker pull gcr.io/gem5-test/gcn-gpu:latest
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docker build -t hacc-test-weekly ${gem5_root}/gem5-resources/src/gpu/halo-finder
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}" hacc-test-weekly bash -c \
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"scons build/GCN3_X86/gem5.opt -j${threads} \
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|| rm -rf build && scons build/GCN3_X86/gem5.opt -j${threads}"
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# Some of the apps we test use m5ops (and x86), so compile them for x86
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# Note: setting TERM in the environment is necessary as scons fails for m5ops if
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# it is not set.
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/util/m5" hacc-test-weekly bash -c \
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"export TERM=xterm-256color ; scons build/x86/out/m5"
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# test LULESH
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# build LULESH
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/gem5-resources/src/gpu/lulesh" \
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-u $UID:$GID hacc-test-weekly bash -c \
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"make"
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# LULESH is heavily used in the HPC community on GPUs, and does a good job of
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# stressing several GPU compute and memory components
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}" hacc-test-weekly build/GCN3_X86/gem5.opt \
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configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root="${gem5_root}/gem5-resources/src/gpu/lulesh/bin" -c lulesh
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# test DNNMark
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# setup cmake for DNNMark
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark" \
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hacc-test-weekly bash -c "./setup.sh HIP"
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# make the DNNMark library
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark/build" \
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hacc-test-weekly bash -c "make -j${threads}"
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# generate cachefiles -- since we are testing gfx801 and 4 CUs (default config)
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# in tester, we want cachefiles for this setup
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark" \
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"-v${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
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hacc-test-weekly bash -c \
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"python3 generate_cachefiles.py cachefiles.csv --gfx-version=gfx801 \
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--num-cus=4"
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# generate mmap data for DNNMark (makes simulation much faster)
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark" hacc-test-weekly bash -c \
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"g++ -std=c++0x generate_rand_data.cpp -o generate_rand_data"
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docker run --rm -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark" hacc-test-weekly bash -c \
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"./generate_rand_data"
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# now we can run DNNMark!
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# DNNMark is representative of several simple (fast) layers within ML
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# applications, which are heavily used in modern GPU applications. So, we want
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# to make sure support for these applications are tested. Run three variants:
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# fwd_softmax, bwd_bn, fwd_pool; these tests ensure we run a variety of ML kernels,
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# including both inference and training
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -v \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
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-w "${gem5_root}/gem5-resources/src/gpu/DNNMark" hacc-test-weekly \
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"${gem5_root}/build/GCN3_X86/gem5.opt" "${gem5_root}/configs/example/apu_se.py" -n3 \
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--benchmark-root="${gem5_root}/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_softmax" \
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-c dnnmark_test_fwd_softmax \
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--options="-config ${gem5_root}/gem5-resources/src/gpu/DNNMark/config_example/softmax_config.dnnmark \
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-mmap ${gem5_root}/gem5-resources/src/gpu/DNNMark/mmap.bin"
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -v \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
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-w "${gem5_root}/gem5-resources/src/gpu/DNNMark" hacc-test-weekly \
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"${gem5_root}/build/GCN3_X86/gem5.opt" "${gem5_root}/configs/example/apu_se.py" -n3 \
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--benchmark-root="${gem5_root}/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_fwd_pool" \
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-c dnnmark_test_fwd_pool \
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--options="-config ${gem5_root}/gem5-resources/src/gpu/DNNMark/config_example/pool_config.dnnmark \
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-mmap ${gem5_root}/gem5-resources/src/gpu/DNNMark/mmap.bin"
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -v \
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"${gem5_root}/gem5-resources/src/gpu/DNNMark/cachefiles:/root/.cache/miopen/2.9.0" \
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-w "${gem5_root}/gem5-resources/src/gpu/DNNMark" hacc-test-weekly \
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"${gem5_root}/build/GCN3_X86/gem5.opt" "${gem5_root}/configs/example/apu_se.py" -n3 \
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--benchmark-root="${gem5_root}/gem5-resources/src/gpu/DNNMark/build/benchmarks/test_bwd_bn" \
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-c dnnmark_test_bwd_bn \
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--options="-config ${gem5_root}/gem5-resources/src/gpu/DNNMark/config_example/bn_config.dnnmark \
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-mmap ${gem5_root}/gem5-resources/src/gpu/DNNMark/mmap.bin"
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# test HACC
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# build HACC
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docker run --rm -v ${PWD}:${PWD} -w \
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"${gem5_root}/gem5-resources/src/gpu/halo-finder/src" -u $UID:$GID \
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hacc-test-weekly make hip/ForceTreeTest
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# Like LULESH, HACC is heavily used in the HPC community and is used to stress
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# the GPU memory system
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/halo-finder/src/hip \
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-c ForceTreeTest --options="0.5 0.1 64 0.1 1 N 12 rcb"
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# test Pannotia
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# Pannotia has 6 different benchmarks (BC, Color, FW, MIS, PageRank, SSSP), of
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# which 3 (Color, PageRank, SSSP) have 2 different variants. Since they are
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# useful for testing irregular GPU application behavior, we test each.
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# build BC
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docker run --rm -v ${PWD}:${PWD} \
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-w ${gem5_root}/gem5-resources/src/gpu/pannotia/bc -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; make gem5-fusion"
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# # get input dataset for BC test
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wget http://dist.gem5.org/dist/develop/datasets/pannotia/bc/1k_128k.gr
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# run BC
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=gem5-resources/src/gpu/pannotia/bc/bin -c bc.gem5 \
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--options="1k_128k.gr"
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# build Color Max
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/color -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; make gem5-fusion"
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# run Color (Max) (use same input dataset as BC for faster testing)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/color/bin \
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-c color_max.gem5 --options="1k_128k.gr 0"
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# build Color (MaxMin)
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/color -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; export VARIANT=MAXMIN ; make gem5-fusion"
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# run Color (MaxMin) (use same input dataset as BC for faster testing)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/color/bin \
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-c color_maxmin.gem5 --options="1k_128k.gr 0"
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# build FW
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/fw -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; make gem5-fusion"
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# run FW (use same input dataset as BC for faster testing)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/fw/bin \
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-c fw_hip.gem5 --options="1k_128k.gr"
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# build MIS
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/mis -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; make gem5-fusion"
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# run MIS (use same input dataset as BC for faster testing)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/mis/bin \
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-c mis_hip.gem5 --options="1k_128k.gr 0"
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# build Pagerank Default variant
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/pagerank -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; make gem5-fusion"
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# get PageRank input dataset
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wget http://dist.gem5.org/dist/develop/datasets/pannotia/pagerank/coAuthorsDBLP.graph
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# run PageRank (Default)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/pagerank/bin \
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-c pagerank.gem5 --options="coAuthorsDBLP.graph 1"
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# build PageRank SPMV variant
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/pagerank -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; export VARIANT=SPMV ; make gem5-fusion"
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# run PageRank (SPMV)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/pagerank/bin \
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-c pagerank_spmv.gem5 --options="coAuthorsDBLP.graph 1"
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# build SSSP CSR variant
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/sssp -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; make gem5-fusion"
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# run SSSP (CSR) (use same input dataset as BC for faster testing)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/sssp/bin \
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-c sssp.gem5 --options="1k_128k.gr 0"
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# build SSSP ELL variant
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docker run --rm -v ${gem5_root}:${gem5_root} -w \
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${gem5_root}/gem5-resources/src/gpu/pannotia/sssp -u $UID:$GID \
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hacc-test-weekly bash -c \
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"export GEM5_PATH=${gem5_root} ; export VARIANT=ELL ; make gem5-fusion"
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# run SSSP (ELL) (use same input dataset as BC for faster testing)
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docker run --rm -v ${gem5_root}:${gem5_root} -w ${gem5_root} -u $UID:$GID \
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hacc-test-weekly ${gem5_root}/build/GCN3_X86/gem5.opt \
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${gem5_root}/configs/example/apu_se.py -n3 --mem-size=8GB \
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--benchmark-root=${gem5_root}/gem5-resources/src/gpu/pannotia/sssp/bin \
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-c sssp_ell.gem5 --options="1k_128k.gr 0"
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# Delete the gem5 resources repo we created -- need to do in docker because of
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# cachefiles DNNMark creates
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docker run --rm --volume "${gem5_root}":"${gem5_root}" -w \
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"${gem5_root}" hacc-test-weekly bash -c \
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"rm -rf ${gem5_root}/gem5-resources"
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# delete Pannotia datasets we downloaded and output files it created
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rm -f coAuthorsDBLP.graph 1k_128k.gr result.out
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