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 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.
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Davidson, A., Owens, J. (2012). Toward Techniques for Auto-tuning GPU Algorithms. In: Jónasson, K. (eds) Applied Parallel and Scientific Computing. PARA 2010. Lecture Notes in Computer Science, vol 7134. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-28145-7_11
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DOI: https://doi.org/10.1007/978-3-642-28145-7_11
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-28144-0
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