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Performance optimization and modeling of blocked sparse kernels
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DOI：10.1177/1094342007083801.png)
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摘要
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We present a method for automatically selecting optimal implementations of sparse matrix-vector operations. Our software AcCELS (Accelerated Compress-storage Elements for Linear Solvers) involves a setup phase that probes machine characteristics, and a run-time phase where stored characteristics are combined with a measure of the actual sparse matrix to find the optimal kernel implementation. We present a performance model that is shown to be accurate over a large range of matrices.
Keyword:
optimization
sparse
matrix-vector product
blocking
self-adaptivity
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