||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. From a mathematical optimization standpoint, two classes of algorithms are available to tackle this
problem: global and local algorithms. In this paper, we investigate the
effectiveness of some global and local search algorithms for empirical performance tuning. We present an experimental study of these algorithms
on a number of problems from the recently introduced SPAPT test suite.
We show that local search algorithms are particularly attractive for empirical performance tuning, where finding high-preforming code variants
in a short computation time is crucial.
||Appears in High Performance Computing for Computational Science - VECPAR 2012,
10th International Conference, Kobe, Japan, July 17-20, 2012, Revised Selected Papers, Lecture Notes in Computer Science.
title = "An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning",
author = "Prasanna Balaprakash and Stefan M. Wild and Paul D. Hovland",
booktitle = "High Performance Computing for Computational Science - VECPAR 2012,
10th International Conference, Kobe, Japan, July 17-20, 2012, Revised Selected Papers.",
series = "Lecture Notes in Computer Science",
editors = "M.J. Dayd\'e, O. Marques, K. Nakajima",
year = "2013",
publisher = "Springer",
pages = "pp. 261--269",
doi = "10.1007/978-3-642-38718-0_26",
isbn = "978-3-642-38717-3"