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owensgroup / merge-spmm

Code for paper "Design Principles for Sparse Matrix Multiplication on the GPU" accepted to Euro-Par 2018
Apache License 2.0
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Design Principles for Sparse Matrix Multiplication on the GPU: Sparse Matrix-Dense Matrix Multiplication

by Carl Yang, Aydin Buluc, John D. Owens

Accepted as Distinguished Paper at EuroPar 2018

Abstract

We implement two novel algorithms for sparse-matrix dense- matrix multiplication (SpMM) on the GPU. Our algorithms expect the sparse input in the popular compressed-sparse-row (CSR) format and thus do not require expensive format conversion. While previous SpMM work concentrates on thread-level parallelism, we additionally focus on latency hiding with instruction-level parallelism and load-balancing. We show, both theoretically and experimentally, that the proposed SpMM is a better fit for the GPU than previous approaches. We identify a key memory access pattern that allows efficient access into both input and output matrices that is crucial to getting excellent performance on SpMM. By combining these two ingredients—(i) merge-based load-balancing and (ii) row-major coalesced memory access—we demonstrate a 3.6× peak speedup and a 23.5% geomean speedup over state-of-the-art SpMM implementations on real-world datasets.

spmm-europar18-preprint.pdf

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes.

Prerequisites

This software has been tested on the following dependencies:

CUDA 8.0

If CUDA 8.0 is not already installed on your system, you will need to download CUDA 8.0 here. Follow the onscreen instructions and select the operating system and vendor that suits your needs. Download the 1.4GB file. You do not need to download the optional Patch 2.

After generating a download link, the commands I typed were the following:

wget https://developer.nvidia.com/compute/cuda/8.0/Prod2/local_installers/cuda_8.0.61_375.26_linux-run
chmod +x cuda_8.0.61_375.26_linux-run
sudo ./cuda_8.0.61_375.26_linux-run

You will need to select:

graphics driver: yes
OpenGL: yes
nvidia-xconfig: no
CUDA 8.0: yes
symbolic link: yes
CUDA samples: yes

Once installation has finished, check that your installation has completed by typing:

nvidia-smi

If installation was successful, you should be able to see information about your GPU printed onscreen. Check that the right information has been added to your system path by typing:

vi ~/.bashrc

If not already present, you should append to the bottom:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/usr/local/cuda-8.0/lib64
export CUDA_HOME=/usr/local/cuda-8.0

Additional instructions on installing CUDA can be found here.

Boost 1.58

You will need to install or compile Boost 1.58 program options using the same compiler as you do our software. To only install Boost program options, type:

wget http://sourceforge.net/projects/boost/files/boost/1.58.0/boost_1_58_0.tar.gz
tar -xvzf boost_1_58_0.tar.gz
cd boost_1_58_0
./bootstrap.sh --prefix=path/to/installation/prefix
./b2 --with-program_options

CMake 3.11.1

If not already installed, you will need to install CMake by typing:

sudo apt-get install cmake

g++ 4.9.3

You will need g++-4.9. Install by typing:

sudo apt-get install gcc-4.9 g++-4.9

ModernGPU 1.1

This excellent software by Sean Baxter will be automatically downloaded as a Git submodule.

Installing

A step by step series of examples that tell you have to get a development env running.

  1. First, we must download the software:
git clone --recursive https://github.com/owensgroup/merge-spmm.git
cd merge-spmm
  1. Also, we must compile a dependency.
cd ext/merge-spmv
make gpu_spmv sm=350
cd ../../
  1. Then, we must compile the software.
cmake .
make -j16
  1. Next, we must download the datasets. In order of increasing size, they are listed below and can be downloaded automatically.

Small - 10 small row length matrices (400MB)

cd dataset/europar/lowd
make
cd ../../../

Large - 10 large row length matrices (1650MB)

cd dataset/europar/highd
make
cd ../../../

Super large - 172 matrices (4000MB)

cd dataset/europar/large
sh DownloadFigure6.sh
sh Extract.sh
cd ../../../
  1. The figures in the paper can be reconstructed by typing:
sh Figure1a-spmm-4.sh
sh Figure1a-spmv.sh
sh Figure1b.sh
sh Figure5a.sh
sh Figure5b.sh
sh Figure6.sh

Authors

License

This project is licensed under the Apache License - see the LICENSE.md file for details

Acknowledgments