SparseTIR Artifact

This repository contains scripts for setting up environments and reproducing results presented in the ASPLOS 2023 paper entitled SparseTIR: Composable Abstractions for Deep Learning. To access the core implementation of SparseTIR (i.e., the compiler), please visit the SparseTIR repository. Additionally, we have written a post called Retrospective on SparseTIR Artifact in which we discuss some issues we encountered when preparing this artifact. Please give it a read if you're interested.

Prerequisite

We require NVIDIA Container Toolkit to setup environments, please follow instructions from installation guide, below is the installation script for Debian/Ubuntu (extracted from official guide):

curl https://get.docker.com | sh \
  && sudo systemctl --now enable docker

distribution=$(. /etc/os-release;echo $ID$VERSION_ID) \
      && curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg \
      && curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
            sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
            sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

sudo apt-get update
sudo apt-get install -y nvidia-container-toolkit

sudo nvidia-ctk runtime configure --runtime=docker
sudo systemctl restart docker

User can try the following command to test whether the installation was successful or not:

docker run --rm --runtime=nvidia --gpus all nvidia/cuda:11.6.2-base-ubuntu20.04 nvidia-smi

Clone the Repository

git clone https://github.com/uwsampl/sparsetir-artifact.git --recursive
cd sparsetir-artifact

Setup Docker Image

Pull from Docker Hub

We provide a pre-built docker image available on Docker Hub which is compatible with Ampere architecture NVIDIA GPUs, user can pull it with:

docker image pull expye/sparsetir-ae:latest
docker tag expye/sparsetir-ae:latest sparsetir

Build from source

Otherwise, user need to build the docker image from source code. Before building the artifact, user need to set docker's default runtime to nvidia to enable GPU access when buildling docker images (reference), by editing the file /etc/docker/daemon.json with content:

{
    "runtimes": {
        "nvidia": {
            "path": "/usr/bin/nvidia-container-runtime",
            "runtimeArgs": []
         } 
    },
    "default-runtime": "nvidia" 
}

then restart docker daemon:

sudo systemctl restart docker

After these steps, user can run the following command to build docker container:

docker build -t sparsetir .

Run experiments

Below is the script to reproduce experiments in SparseTIR paper, each script would emit logging files and figures in pdf format.

# Run SpMM experiments
docker run -it --gpus all -v $(pwd)/spmm/:/root/spmm sparsetir /bin/bash -c 'cd spmm && bash run.sh'
# Run SDDMM experiments
docker run -it --gpus all -v $(pwd)/sddmm/:/root/sddmm sparsetir /bin/bash -c 'cd sddmm && bash run.sh'
# Run GraphSAGE training experiments
docker run -it --gpus all -v $(pwd)/e2e/:/root/e2e sparsetir /bin/bash -c 'cd e2e && bash run.sh'
# Run RGCN inference experiments
docker run -it --gpus all -v $(pwd)/rgcn/:/root/rgcn sparsetir /bin/bash -c 'cd rgcn && bash run.sh'
# Run Sparse Attention experiments
docker run -it --gpus all -v $(pwd)/sparse-attention/:/root/sparse-attention sparsetir /bin/bash -c 'cd sparse-attention && bash run.sh'
# Run PrunedBERT experiments
docker run -it --gpus all -v $(pwd)/pruned-bert/:/root/pruned-bert sparsetir /bin/bash -c 'cd pruned-bert && bash run.sh'
# Run Sparse Convolution experiments
docker run -it --gpus all -v $(pwd)/sparse-conv/:/root/sparse-conv sparsetir /bin/bash -c 'cd sparse-conv && bash run.sh'

User can use run-all.sh script to run all experiments:

bash run-all.sh

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