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EECS Publication

Performance Optimization and Modeling of Blocked Sparse Kernels

Alfredo Buttari, Victor Eijkhout, Julien Langou, Salvatore Filippone

We present a method for automatically selecting optimal implementations of sparse matrixvector 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.

Published  2004-12-01 05:00:00  as  ut-cs-04-543 (ID:204)


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