Fast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU

摘要

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.