15575

Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code

Guillermo Vigueras, Manuel Carro, Salvador Tamarit, Julio Marino
IMDEA Software Institute, Campus de Montegancedo 28223, Pozuelo de Alarcon, Madrid, Spain
arXiv:1603.03022 [cs.PL], (10 Mar 2016)
@article{vigueras2016towards,

   title={Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code},

   author={Vigueras, Guillermo and Carro, Manuel and Tamarit, Salvador and Marino, Julio},

   year={2016},

   month={mar},

   archivePrefix={"arXiv"},

   primaryClass={cs.PL}

}

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The current trend in next-generation exascale systems goes towards integrating a wide range of specialized (co-)processors into traditional supercomputers. However, the integration of different specialized devices increases the degree of heterogeneity and the complexity in programming such type of systems. Due to the efficiency of heterogeneous systems in terms of Watt and FLOPS per surface unit, opening the access of heterogeneous platforms to a wider range of users is an important problem to be tackled. In order to bridge the gap between heterogeneous systems and programmers, in this paper we propose a machine learning-based approach to learn heuristics for defining transformation strategies of a program transformation system. Our approach proposes a novel combination of reinforcement learning and classification methods to efficiently tackle the problems inherent to this type of systems. Preliminary results demonstrate the suitability of the approach for easing the programmability of heterogeneous systems.
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