期刊
COMPUTERS & ELECTRICAL ENGINEERING
卷 88, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.compeleceng.2020.106848
关键词
Sparse matrix multiplication; GPU; Tensor Cores; Parallel computing; SpGEMM
类别
资金
- High Performance Soft-tissue Navigation (HIPERNAV - H2020-MSCA-ITN-2016)
- European Union [722068]
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 files and operates only on files which contain one or more elements. It creates a task list of the files, and performs matrix multiplication of these files 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 filing approach, benefits from TCUs. Our approach significantly improves the performance of spGEMM in comparison to cuSPARSE, CUSP, RMerge2, Nsparse, AC-SpGEMM and spECK.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据