TY - JOUR
T1 - A Systematic Survey of General Sparse Matrix-matrix Multiplication
AU - Gao, Jianhua
AU - Ji, Weixing
AU - Chang, Fangli
AU - Han, Shiyu
AU - Wei, Bingxin
AU - Liu, Zeming
AU - Wang, Yizhuo
N1 - Publisher Copyright:
© 2023 Association for Computing Machinery.
PY - 2023/12/31
Y1 - 2023/12/31
N2 - General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning. Many optimization techniques have been developed for different applications and computing architectures over the past decades. The objective of this article is to provide a structured and comprehensive overview of the researches on SpGEMM. Existing researches have been grouped into different categories based on target architectures and design choices. Covered topics include typical applications, compression formats, general formulations, key problems and techniques, architecture-oriented optimizations, and programming models. The rationales of different algorithms are analyzed and summarized. This survey sufficiently reveals the latest progress of SpGEMM research to 2021. Moreover, a thorough performance comparison of existing implementations is presented. Based on our findings, we highlight future research directions, which encourage better design and implementations in later studies.
AB - General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning. Many optimization techniques have been developed for different applications and computing architectures over the past decades. The objective of this article is to provide a structured and comprehensive overview of the researches on SpGEMM. Existing researches have been grouped into different categories based on target architectures and design choices. Covered topics include typical applications, compression formats, general formulations, key problems and techniques, architecture-oriented optimizations, and programming models. The rationales of different algorithms are analyzed and summarized. This survey sufficiently reveals the latest progress of SpGEMM research to 2021. Moreover, a thorough performance comparison of existing implementations is presented. Based on our findings, we highlight future research directions, which encourage better design and implementations in later studies.
KW - Additional Key Words and PhrasesSpGEMM
KW - parallel architecture
KW - parallel computing
KW - sparse matrix
UR - https://www.scopus.com/pages/publications/85152603608
U2 - 10.1145/3571157
DO - 10.1145/3571157
M3 - Article
AN - SCOPUS:85152603608
SN - 0360-0300
VL - 55
JO - ACM Computing Surveys
JF - ACM Computing Surveys
IS - 12
M1 - 3571157
ER -