#!/bin/bash

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set -e
set -x

dir="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
gem5_root="${dir}/.."

# We assume the lone argument is the number of threads. If no argument is
# given we default to one.
threads=1
if [[ $# -gt 0 ]]; then
    threads=$1
fi

# Run the gem5 very-long tests.
docker run -u $UID:$GID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}"/tests --rm gcr.io/gem5-test/ubuntu-20.04_all-dependencies \
        ./main.py run --length very-long -j${threads} -t${threads}

# For the GPU tests we compile and run GCN3_X86 inside a gcn-gpu container.
docker pull gcr.io/gem5-test/gcn-gpu:latest
docker run --rm -u $UID:$GUID --volume "${gem5_root}":"${gem5_root}" -w \
    "${gem5_root}" gcr.io/gem5-test/gcn-gpu:latest bash -c \
    "scons build/GCN3_X86/gem5.opt -j${threads} \
        || (rm -rf build && scons build/GCN3_X86/gem5.opt -j${threads})"

# get LULESH
wget -qN http://dist.gem5.org/dist/develop/test-progs/lulesh/lulesh

mkdir -p tests/testing-results

# LULESH is heavily used in the HPC community on GPUs, and does a good job of
# stressing several GPU compute and memory components
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