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Adaptive implementation selection in the SkePU skeleton programming library

Usman Dastgeer, Lu Li, Christoph Kessler
IDA, Linkoping University, 58183 Linkoping, Sweden
Biennial Conference on Advanced Parallel Processing Technology (APPT-2013), 2013
@article{dastgeer2013adaptive,

   title={Adaptive implementation selection in the SkePU skeleton programming library},

   author={Dastgeer, Usman and Li, Lu and Kessler, Christoph},

   year={2013}

}

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In earlier work, we have developed the SkePU skeleton programming library for modern multicore systems equipped with one or more programmable GPUs. The library internally provides four types of implementations (implementation variants) for each skeleton: serial C++, OpenMP, CUDA and OpenCL targeting either CPU or GPU execution respectively. Deciding which implementation would run faster for a given skeleton call depends upon the computation, problem size(s), system architecture and data locality. In this paper, we present our work on automatic selection between these implementation variants by an offline machine learning method which generates a compact decision tree with low training overhead. The proposed selection mechanism is flexible yet high-level allowing a skeleton programmer to control different training choices at a higher abstraction level.We have evaluated our optimization strategy with 9 applications/kernels ported to our skeleton library and achieve on average more than 94% (90%) accuracy with just 0.53% (0.58%) training space exploration on two systems. Moreover, we discuss one application scenario where local optimization considering a single skeleton call can prove sub-optimal, and propose a heuristic for bulk implementation selection considering more than one skeleton call to address such application scenarios.
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