We introduce a variety of techniques toward autotuning data-parallel algorithms on the GPU. Our techniques tune these algorithms independent of hardware architecture, and attempt to select near-optimum parameters. We work towards a general framework for creating auto-tuned data-parallel algorithms, using these techniques for common algorithms with varying characteristics. Our contributions include tuning a set of algorithms with a variety of computational patterns, with the goal in mind of building a general framework from these results. Our tuning strategy focuses first on identifying the computational patterns an algorithm shows, and then reducing our tuning model based on these observed patterns. © 2012 Springer-Verlag.
CITATION STYLE
Davidson, A., & Owens, J. (2012). Toward techniques for auto-tuning GPU algorithms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7134 LNCS, pp. 110–119). https://doi.org/10.1007/978-3-642-28145-7_11
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