SpArch: Efficient Architecture for Sparse Matrix Multiplication
Zhekai Zhang∗, Hanrui Wang∗, Song Han Thanks: $ˆ*$Equal Contributions. Affiliation: EECS Massachusetts Institute of Technology Cambridge, MA, US {zhangzk, hanrui, William J. Dally Affiliation: Electrical Engineering Stanford University / NVIDIA Stanford, CA, US
Abstract
Generalized Sparse Matrix-Matrix Multiplication (SpGEMM) is a ubiquitous task in various engineering and scientific applications. However, inner product based SpGEMM introduces redundant input fetches for mismatched nonzero operands, while outer product based approach [1] suffers from poor output locality due to numerous partial product matrices. Inefficiency in the reuse of either inputs or outputs data leads to extensive and expensive DRAM access.
原文 arXiv:2002.08947;中英对照 + 大白话阅读 https://aha.fim.ai/paper/2002.08947v1