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.

广义稀疏矩阵-矩阵乘法 (SpGEMM) 在图分析、科学计算和深度学习领域引起了研究人员的广泛关注。在过去的几十年里，针对不同的应用程序和计算架构开发了许多优化技术。本文的目的是对 SpGEMM 的研究提供结构化和全面的概述。现有研究已根据目标架构和设计选择分为不同的类别。涵盖的主题包括典型应用程序、压缩格式、一般公式、关键问题和技术、面向体系结构的优化和编程模型。分析总结了不同算法的基本原理。这项调查充分揭示了 SpGEMM 研究到 2021 年的最新进展。此外，对现有实施方案进行了全面的性能比较。根据我们的发现，我们强调了未来的研究方向，鼓励在以后的研究中更好地设计和实施。