Fast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU

Abstract

We implement a promising algorithm for sparse-matrix sparse-vector multiplication (SpMSpV) on the GPU. An efficient k-way merge lies at the heart of finding a fast parallel SpMSpV algorithm. We examine the scalability of three approaches -- no sorting, merge sorting, and radix sorting -- in solving this problem. For breadth-first search (BFS), we achieve a 1.26x speedup over state-of-the-art sparse-matrix dense-vector (SpMV) implementations. The algorithm seems generalize able for single-source shortest path (SSSP) and sparse-matrix sparse-matrix multiplication, and other core graph primitives such as maximal independent set and bipartite matching.

DOI: 10.1109/IPDPSW.2015.77

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Cite this paper

@article{Yang2015FastSM, title={Fast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU}, author={Carl Yang and Yangzihao Wang and John D. Owens}, journal={2015 IEEE International Parallel and Distributed Processing Symposium Workshop}, year={2015}, pages={841-847} }