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Janjic","K. Hammond","M. Goli","J. McCall","K. Idrees","C. Glass","M. A. Wafai" ], "authors": [ {"first" : "V.", "last" : "Janjic"}, {"first" : "K.", "last" : "Hammond"}, {"first" : "M.", "last" : "Goli"}, {"first" : "J.", "last" : "McCall"}, {"first" : "K.", "last" : "Idrees"}, {"first" : "C.", "last" : "Glass"}, {"first" : "M. A.", "last" : "Wafai"} ], "abstract": "This paper presents a new programming methodology for intro- ducing and tuning parallelism for heterogeneous shared-memory systems (comprising a mixture of CPUs and GPUs), using a com- bination of algorithmic skeletons (such as farms and pipelines), Monte-Carlo tree search for deriving mappings of tasks to avail- able hardware resources, and refactoring tool support for applying the patterns and mappings in an easy and effective way. Using our approach, we demonstrate easily obtainable, significant and scal- able speedups on a number of case studies showing speedups of up to 41 over the sequential code on a 24-core machine with one GPU. 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