TileSpMV

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Source code of the IPDPS '21 paper: "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs" by Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu, and Guangming Tan.

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# TileSpMV

**TileSpMV** is an open source code that uses a tiled structure to optimize sparse matrix-vector multiplication (SpMV) on GPUs.

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## Paper information

Yuyao Niu, Zhengyang Lu, Meichen Dong, Zhou Jin, Weifeng Liu and Guangming Tan, "TileSpMV: A Tiled Algorithm for Sparse Matrix-Vector Multiplication on GPUs," 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2021, pp. 68-78, DOI: https://doi.org/10.1109/IPDPS49936.2021.00016.

## Contact us

If you have any questions about running the code, please contact Yuyao Niu.

E-mail: yuyao.niu.phd@gmail.com

## Introduction

Sparse matrix-vector multiplication(SpMV) executes Ax=y, where A is a sparse matrix, x and y are dense vectors. TileSpMV implemented seven warp-level SpMV methods to calculate sparse tiles stored in different formats, and a format selection method is designed to select the best format and algorithm for each sparse tile to improve performance from the perspective of the local sparse structure of the matrix. In addition, nonzeros in very sparse tiles are extracted into a separate matrix for better performance. 
TileSpMV provides a version of CUDA on a high parallelism currently.

## Installation

NVIDIA GPU with compute capability at least 3.5 (NVIDIA Tesla K40 as tested) * NVIDIA nvcc CUDA compiler and cuSPARSE library, both of which are included with CUDA Toolkit (CUDA v11.1 as tested) 
The GPU test programs have been tested on Ubuntu 18.04/20.04, and are expected to run correctly under other Linux distributions.

## Execution of TileSpMV
Our test programs currently support input files encoded using the matrix market format. All matrix market datasets used in this evaluation are publicly available from the SuiteSparse Matrix Collection.

1. Set CUDA path in the Makefile

2. The command 'make' generates an executable file 'test' for double/single precision.
> **make**

3. Run SpMV code on matrix data with auto-tuning in double precision. The GPU compilation takes an optional d=<gpu-device, e.g., 0> parameter that specifies the GPU device to run if multiple GPU devices are available at the same time. 
> **./test -d 0 test.mtx**

## Release version
Oct 19,2021 Version Alpha

Jul 6, 2022 Version Beta

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