A Systematic Survey of General Sparse Matrix-matrix Multiplication

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.

Automated summary

This article provides a structured and comprehensive overview of the research on General Sparse Matrix-Matrix Multiplication (SpGEMM). It categorizes existing research based on target architectures and design choices, covering topics such as applications, compression formats, formulations, optimizations, and programming models. The article analyzes and summarizes the rationales of different algorithms and presents a thorough performance comparison of existing implementations. Future research directions are also highlighted to encourage better design and implementations in later studies.