Abstract
The increasing complexity, heterogeneity, and rapid evolution of modern computer architectures present obstacles for achieving high performance of scientific codes on different machines. Empirical performance tuning is a viable approach to obtain high-performing code variants based on their measured performance on the target machine. In previous work, we formulated the search for the best code variant as a numerical optimization problem. Two classes of algorithms are available to tackle this problem: global and local algorithms. We present an experimental study of some global and local search algorithms on a number of problems from the recently introduced SPAPT test suite. We show that local search algorithms are particularly attractive, where finding high-preforming code variants in a short computation time is crucial.
This paper has been created by UChicago Argonne, LLC, Operator of Argonne National Laboratory (“Argonne”). Argonne, a U.S. Department of Energy Office of Science laboratory, is operated under Contract No. DE-AC02-06CH11357.
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Balaprakash, P., Wild, S.M., Hovland, P.D. (2013). An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning. In: Daydé, M., Marques, O., Nakajima, K. (eds) High Performance Computing for Computational Science - VECPAR 2012. VECPAR 2012. Lecture Notes in Computer Science, vol 7851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38718-0_26
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DOI: https://doi.org/10.1007/978-3-642-38718-0_26
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