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 paper 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.

翻译：通用稀疏矩阵-矩阵乘法（SpGEMM）在图形分析、科学计算和深度学习领域引起了研究者的广泛关注。过去几十年间，针对不同应用场景和计算架构已发展出众多优化技术。本文旨在对SpGEMM相关研究提供结构化且全面的综述。现有研究根据目标架构和设计选择被划分为不同类别，涵盖典型应用、压缩格式、通用形式化表示、关键问题与技术、面向架构的优化方法及编程模型等主题。我们分析并总结了不同算法的设计原理，充分揭示了截至2021年SpGEMM研究的最新进展。此外，本文还对现有实现方案进行了详尽的性能比较。基于研究结果，我们指明了未来研究方向，以期为后续研究中的更优设计与实现提供启示。