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We consider automatic performance tuning of stencil computations on Graphics Processing Units. We present a strategy that uses machine learning to determine the best way to use memory followed by a heuristic that divides the remaining optimizations into groups and exhaustively explores one group at a time. We evaluate our strategy using 102 synthetically(More)
We describe Genesis, a language for the generation of synthetic programs for use in machine learning-based performance auto-tuning. The language allows users to annotate a template program to customize its code using statistical distributions and to generate program instances based on those distributions. This effectively allows users to generate training(More)
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