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Improving the memory-system performance of sparse-matrix vector multiplication

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1 Author(s)
Toledo, S. ; Xerox Palo Alto Research Center, 3333 Coyote Hill Road, California 94304, USA

Sparse-matrix vector multiplication is an important kernel that often runs inefficiently on superscalar RISC processors. This paper describes techniques that increase instruction-level parallelism and improve performance. The techniques include reordering to reduce cache misses (originally due to Das et al.), blocking to reduce load instructions, and prefetching to prevent multiple load-store units from stalling simultaneously. The techniques improve performance from about 40 MFLOPS (on a well-ordered matrix) to more than 100 MFLOPS on a 266-MFLOPS machine. The techniques are applicable to other superscalar RISC processors as well, and have improved performance on a Sun UltraSPARC™ I workstation, for example.

Note: The Institute of Electrical and Electronics Engineers, Incorporated is distributing this Article with permission of the International Business Machines Corporation (IBM) who is the exclusive owner. The recipient of this Article may not assign, sublicense, lease, rent or otherwise transfer, reproduce, prepare derivative works, publicly display or perform, or distribute the Article.  

Published in:

IBM Journal of Research and Development  (Volume:41 ,  Issue: 6 )