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LCPC
2004
Springer

Empirical Performance-Model Driven Data Layout Optimization

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Empirical Performance-Model Driven Data Layout Optimization
Abstract. Empirical optimizers like ATLAS have been very effective in optimizing computational kernels in libraries. The best choice of parameters such as tile size and degree of loop unrolling is determined by executing different versions of the computation. In contrast, optimizing compilers use a model-driven approach to program transformation. While the model-driven approach of optimizing compilers is generally orders of magnitude faster than ATLAS-like library generators, its effectiveness can be limited by the accuracy of the performance sed. Very often, simplified abstractions such as reuse distance are used instead of more accurate metrics like cache miss cost, in order to make modeldriven compiler optimization feasible. In this paper, we describe an approach, in which a class of computations is modeled in terms of its constituent operations that are empirically measured. The empirical measurement of the constituent operations allows modeling of the overall execution time of th...
Qingda Lu, Xiaoyang Gao, Sriram Krishnamoorthy, Ge
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where LCPC
Authors Qingda Lu, Xiaoyang Gao, Sriram Krishnamoorthy, Gerald Baumgartner, J. Ramanujam, P. Sadayappan
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