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Descripción
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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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Internacional
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Si |
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Nombre congreso
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First International Workshop on Program Transformation for Programmability in Heterogeneous Architectures (PROHA 2016) |
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Tipo de participación
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OTHERS |
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Lugar del congreso
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Barcelona |
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Revisores
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Si |
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ISBN o ISSN
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arXiv:1603.03022 |
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DOI
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Fecha inicio congreso
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12/03/2016 |
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Fecha fin congreso
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12/03/2016 |
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Desde la página
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1 |
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Hasta la página
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9 |
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Título de las actas
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Proceedings of the First International Workshop on Program Transformation for Programmability in Heterogeneous Architectures (PROHA 2016) |