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We've tested codes on NVIDIA GeForce RTX 2080 Ti GPU in Ubuntu 20.04 amd64 system. GPU should have Turing architecture.
Note: GPU architecture over Turing could have bug when tracing (NVBit bug).
We tested our code on Ubuntu 20.04 amd64 system, and used CUDA 11.3.1 and cuDNN 8.
Software pre-requisites for installing from the source should be satisfied for the following repositories:
You can install all dependencies by following this document.
Firstly, make sure CUDA is installed in your system:
export CUDA_INSTALL_PATH=/usr/local/cuda # set it to your CUDA installation path
nvcc --versionIn Ubuntu 20.04 amd64 system, following commands install package dependencies:
sudo apt-get update
sudo apt-get install -y --no-install-recommends python3-dev ca-certificates g++ python3-numpy gcc make git python3-setuptools python3-wheel python3-pip aria2 wget build-essential xutils-dev bison zlib1g-dev flex libglu1-mesa-dev git libssl-dev libxml2-dev libboost-all-dev vim python-setuptools python-dev ninja-build bc git-lfs libtinfo-dev htop libedit-devNext, install Python (>= 3.8) dependencies.
Note: you need to use specific PyTorch version (= 1.11.0). Later version could generate different node name that cannot be processed by the current version.
python3 -m pip install -U --force-reinstall pip
pip install torch==1.11.0+cu113 \
torchvision==0.12.0+cu113 \
torchaudio==0.11.0 --extra-index-url https://download.pytorch.org/whl/cu113
pip3 install pyyaml==5.1 onnx plotly psutil pandas decorator attrs scipyInstall CMake (>= 3.21):
sudo aria2c -q -d /tmp -o cmake-3.21.0-linux-x86_64.tar.gz \
https://github.com/Kitware/CMake/releases/download/v3.21.0/cmake-3.21.0-linux-x86_64.tar.gz
sudo tar -zxf /tmp/cmake-3.21.0-linux-x86_64.tar.gz --strip=1 -C /usrInstall Clang and LLVM (>= 12)
wget -c https://github.com/llvm/llvm-project/releases/download/llvmorg-13.0.0/clang+llvm-13.0.0-x86_64-linux-gnu-ubuntu-20.04.tar.xz
tar -xvf clang+llvm-13.0.0-x86_64-linux-gnu-ubuntu-20.04.tar.xz
sudo cp -rl clang+llvm-13.0.0-x86_64-linux-gnu-ubuntu-20.04/* /usr/local
rm -rf clang+llvm-13.0.0-x86_64-linux-gnu-ubuntu-20.04 \
clang+llvm-13.0.0-x86_64-linux-gnu-ubuntu-20.04.tar.xzInstall and build PIMFlow repositories from the source. We prepared installation script (docker/install.sh):
GIT_BRANCH="2023cgo-artifact"
cd "$HOME"
git clone -b "$GIT_BRANCH" https://github.com/yongwonshin/PIMFlow_tvm.git
TVM_DIR="$HOME/PIMFlow_tvm"
cd "$TVM_DIR"
git submodule init && git submodule update
BUILD_DIR="$TVM_DIR/build"
mkdir -p "$BUILD_DIR" && cd "$BUILD_DIR"
cp "$TVM_DIR/cmake/config.cmake" "$BUILD_DIR"
cmake .. -G Ninja -DCMAKE_CXX_COMPILER=$(which g++) -DCMAKE_C_COMPILER=$(which gcc)
ninja
cd "$HOME"
git clone -b "$GIT_BRANCH" https://github.com/yongwonshin/PIMFlow_accel-sim-framework.git
GPU_DIR="$HOME/PIMFlow_accel-sim-framework/gpu-simulator"
NVBIT_DIR="$HOME/PIMFlow_accel-sim-framework/util/tracer_nvbit"
cd "$GPU_DIR"
source setup_environment.sh
# Generate binary file: $GPU_DIR/bin/release/accel-sim.out
make -j
# Install nvbit
cd "$NVBIT_DIR" && ./install_nvbit.sh && make -j
cd "$HOME"
git clone -b "$GIT_BRANCH" https://github.com/yongwonshin/PIMFlow_ramulator.git
RAM_DIR="$HOME/PIMFlow_ramulator"
cd "$RAM_DIR"
# Generate binary file: $RAM_DIR/ramulator
make -j
cd "$HOME"
git clone -b "$GIT_BRANCH" https://github.com/yongwonshin/PIMFlow.git
PIMFLOW_DIR="$HOME/PIMFlow"
cd "$PIMFLOW_DIR"
pip install -e .
cd "$PIMFLOW_DIR/pim"
# Generate binary file: $PIMFLOW_DIR/pim/pim_codegen
make -j
# Extract network traces
cd "$PIMFLOW_DIR"
tar -xzf ./data/mobilenet-v2.tar.gz -C .
tar -xzf ./data/traces-mobilenet-v2-16-org.tar.gz -C .
tar -xzf ./data/traces-mobilenet-v2-16-Newton+.tar.gz -C .
tar -xzf ./data/traces-mobilenet-v2-16-Newton++.tar.gz -C .
tar -xzf ./data/traces-mobilenet-v2-16-Pipeline.tar.gz -C .
tar -xzf ./data/traces-mobilenet-v2-16-MDDP.tar.gz -C .
tar -xzf ./data/traces-mobilenet-v2-16-PIMFlow.tar.gz -C .
tar -xzf ./data/mobilenet-v2-csv.tar.gz -C ../Now, the directory should look like this:
. ($HOME)
./PIMFlow
./PIMFlow_tvm
./PIMFlow_accel-sim-framework
./PIMFlow_ramulator
Finally, you need to set the following environment variables, and include them to .bashrc for later session.
export TVM_HOME=/root/PIMFlow_tvm
export PYTHONPATH=/root/PIMFlow_tvm/pythonYou can manually peform profiling to find optimal execution mode and task size.
Note: it takes about 8 hours in server with 8x NVIDIA GeForce RTX 2080 Ti GPU and 2x Intel Xeon Gold 6248R CPU (24-core)
cd PIMFlow
./pimflow -m=profile -t=split -n=mobilenet-v2
./pimflow -m=profile -t=pipeline -n=mobilenet-v2Or, you can just use the profiled data we've prepared in PIMFlow/mobilenet-v2/ for MobileNet-V2.
Now, you can get the optimal solution using profiled data and get the speedup:
./pimflow -m=stat --conv_only -n=mobilenet-v2The output should look like this:
=== N_CHANNEL: 16, N_GWRITE: 4, ramulator_disable_gwrite_latency_hiding: False ===
newton++ (vs baseline): 1.365 (-388549.76000000024)
pipeline (vs baseline): 1.413 (-425128.2000000004)
split (vs baseline): 1.436 (-441899.4400000004)
all (vs baseline): 1.481 (-472070.72000000044)
====================
Next, you can get speedup by the following commands: Note: it takes about 8 hours in our system. policy option is either Newton+, Newton++, Pipeline, MDDP, or PIMFlow.
./pimflow -m=solve -n=mobilenet-v2
./pimflow -m=run --gpu_only -n=mobilenet-v2 # get gpu-only execution time
./pimflow -m=run -n=mobilenet-v2 # get pimflow execution time
./pimflow -m=stat -n=mobilenet-v2 --policy=PIMFlow # show end-to-end speedupOutput:
GPU CYCLE: 1445620
PIMFlow CYCLE: 1047831.4000000001
PIMFlow SPEEDUP: 1.38
You can replace "mobilenet-v2" with "efficientnet-v1-b0", "mnasnet-1.0", "resnet-50" or "vgg-16" for various network testing. We prepared very simple network "toy" for simple but fast test.