A comparison of search heuristics for empirical code optimization

Keith Seymour, Haihang You, Jack Dongarra

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

    Abstract

    This paper describes the application of various search techniques to the problem of automatic empirical code optimization. The search process is a critical aspect of auto-tuning systems because the large size of the search space and the cost of evaluating the candidate implementations makes it infeasible to find the true optimum point by brute force. We evaluate the effectiveness of Nelder-Mead Simplex, Genetic Algorithms, Simulated Annealing, Particle Swarm Optimization, Orthogonal search, and Random search in terms of the performance of the best candidate found under varying time limits. © 2008 IEEE.
    Original languageEnglish
    Title of host publicationProceedings - IEEE International Conference on Cluster Computing, ICCC|Proc. IEEE Int. Conf. Cluster Comput. ICCC
    PublisherIEEE
    Pages421-429
    Number of pages8
    ISBN (Print)9781424426409
    DOIs
    Publication statusPublished - 2008
    Event2008 IEEE International Conference on Cluster Computing, CCGRID 2008 - Tsukuba
    Duration: 1 Jul 2008 → …

    Conference

    Conference2008 IEEE International Conference on Cluster Computing, CCGRID 2008
    CityTsukuba
    Period1/07/08 → …

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