Abstract

Several fast sequential algorithms have been proposed in the past to multiply sparse matrices. These algorithms do not explicitly address the impact of caching on performance. We show that a rather simple sequential cache--efficient algorithm provides significantly better performance than existing algorithms for sparse matrix multiplication. We then describe a multithreaded implementation of this simple algorithm and show that its performance scales well with the number of threads and CPUs. For 10% sparse, 500 X 500 matrices, the multithreaded version running on 4--CPU systems provides more than a 41.1--fold speed increase over the well--known BLAS routine and a 14.6 fold and 44.6--fold speed increase over two other recent techniques for fast sparse matrix multiplication, both of which are relatively difficult to parallelize efficiently. Keywords: sparse matrix multiplication, caching, loop interchanging 1. Introduction The need to efficiently multiply two sparse matrices is critica...

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