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Maximizing sparse matrix-vector product performance on RISC based MIMD computers
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DOI：10.1006/jpdc.1996.0115.png)
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Abstract
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The matrix-vector product kernel can represent most of the computation in a gradient iterative solver. Thus, an efficient solver requires that the matrix-vector product kernel be fast. We show that standard approaches with Fortran or C may not deliver good performance and present a strategy involving managing the cache to improve the performance. As an example, using this approach we demonstrate that it is possible to achieve 2.5 times better performance over a Fortran implementation with an assembly coded kernel on an Intel i860. These issues are of general interest for all. computer architectures but are particularly important for users of MIMD computers to achieve a useful fraction of the advertised peak performance Of these machines. (C) 1996 Academic Press, Inc.
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