Performance optimization and evaluation for linear codes

Authors:
Pavel Tvrdík;Ivan Šimeček
Affiliations:
Department of Computer Science and Engineering, Czech Technical University, Prague;Department of Computer Science and Engineering, Czech Technical University, Prague
Venue:
NAA'04 Proceedings of the Third international conference on Numerical Analysis and its Applications
Year:
2004

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Characterizing the behavior of sparse algorithms on caches

Proceedings of the 1992 ACM/IEEE conference on Supercomputing
Software Optimization for High Performance Computers

Software Optimization for High Performance Computers

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Abstract

In this paper, we develop a probabilistic model for estimation of the numbers of cache misses during the sparse matrix-vector multiplication (for both general and symmetric matrices) and the Conjugate Gradient algorithm for 3 types of data caches: direct mapped, s-way set associative with random or with LRU replacement strategies. Using HW cache monitoring tools, we compare the predicted number of cache misses with real numbers on Intel x86 architecture with L1 and L2 caches. The accuracy of our analytical model is around 96%.