TitleToward Techniques for Auto Tuning GPU Algorithms (Article)
inPara 2010: State of the Art in Scientific and Parallel Computing
Author(s) Andrew Davidson, John D. Owens
Year June 2010
LocationReykjavik, Iceland
Abstract 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 certain 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.