Affiliation:
1. Cranfield University, Cranfield, United Kingdom
2. Università degli Studi di Roma “Tor Vergata”, Roma, Italy
Abstract
The multiplication of a sparse matrix by a dense vector (SpMV) is a centerpiece of scientific computing applications: it is the essential kernel for the solution of sparse linear systems and sparse eigenvalue problems by iterative methods. The efficient implementation of the sparse matrix-vector multiplication is therefore crucial and has been the subject of an immense amount of research, with interest renewed with every major new trend in high-performance computing architectures. The introduction of General-Purpose Graphics Processing Units (GPGPUs) is no exception, and many articles have been devoted to this problem.
With this article, we provide a review of the techniques for implementing the SpMV kernel on GPGPUs that have appeared in the literature of the last few years. We discuss the issues and tradeoffs that have been encountered by the various researchers, and a list of solutions, organized in categories according to common features. We also provide a performance comparison across different GPGPU models and on a set of test matrices coming from various application domains.
Funder
Amazon with the AWS in Education grant program 2014
INdAM
ISCRA grant program for 2014
CINECA for project IsC14_HyPSBLAS
Publisher
Association for Computing Machinery (ACM)
Subject
Applied Mathematics,Software
Cited by
76 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献