Title: An Experimental Study of Global and Local Search Algorithms in Empirical Performance Tuning
Authors: Prasanna Balaprakash, Stefan Wild, Paul Hovland
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. 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.
Keywords: autotuning; empirical tuning; global versus local optimization; performance-tuning
Thanks: This work was supported in part by the Office of Advanced Scientific Computing Research, Office of Science, U.S. Dept. of Energy, under Contract DE-AC02-06CH11357. We are grateful to the Laboratory Computing Resource Center at Argonne National Laboratory.
Status: Appears in Proceedings of the 10th International Meeting on High-Performance Computing for Computational Science (VECPAR 2012).
Link: [< a href="http://www.mcs.anl.gov/~wild/papers/2012/PBSWPH12.pdf">PDF]
BibTeX:
@inproceedings{PBSWPH12,
  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   = "Proceedings of the 10th International Meeting on High-Performance 
                 Computing for Computational Science (VECPAR 2012)",
  month       = "July",    
  year        = "2012",
  location    = "Kobe, Japan",
  note        = "Available at \url{http://www.mcs.anl.gov/~wild/papers/2012/PBSWPH12.pdf}"
}
	
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