Accelerating Sparse Matrix-Matrix Multiplication with GPU Tensor Cores
Department of Electronic and Computer Engineering, Universidad de Cordoba, Cordoba, Spain
arXiv:2009.14600 [cs.MS], (29 Sep 2020)
@article{Zachariadis_2020,
title={Accelerating sparse matrix–matrix multiplication with GPU Tensor Cores},
volume={88},
ISSN={0045-7906},
url={http://dx.doi.org/10.1016/j.compeleceng.2020.106848},
DOI={10.1016/j.compeleceng.2020.106848},
journal={Computers & Electrical Engineering},
publisher={Elsevier BV},
author={Zachariadis, Orestis and Satpute, Nitin and Gómez-Luna, Juan and Olivares, Joaquín},
year={2020},
month={Dec},
pages={106848}
}
Sparse general matrix-matrix multiplication (spGEMM) is an essential component in many scientific and data analytics applications. However, the sparsity pattern of the input matrices and the interaction of their patterns make spGEMM challenging. Modern GPUs include Tensor Core Units (TCUs), which specialize in dense matrix multiplication. Our aim is to re-purpose TCUs for sparse matrices. The key idea of our spGEMM algorithm, tSparse, is to multiply sparse rectangular blocks using the mixed precision mode of TCUs. tSparse partitions the input matrices into tiles and operates only on tiles which contain one or more elements. It creates a task list of the tiles, and performs matrix multiplication of these tiles using TCUs. To the best of our knowledge, this is the first time that TCUs are used in the context of spGEMM. We show that spGEMM, with our tiling approach, benefits from TCUs. Our approach significantly improves the performance of spGEMM in comparison to cuSPARSE, CUSP, RMerge2, Nsparse, AC-SpGEMM and spECK.
October 4, 2020 by hgpu
