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Open Access Publications from the University of California

Fast Sparse Matrix and Sparse Vector Multiplication Algorithm on the GPU

  • Author(s): Yang, Carl
  • Wang, Yangzihao
  • Owens, John D.
  • et al.

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 generalizeable 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.

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