Autor:
Vigueras, Guillermo

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guillermo.vigueras@imdea.org

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Vigueras

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Guillermo

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IMDEA Software Institute, Spain
IMDEA Software Institute Madrid, Spain

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Mostrando 1 - 2 de 2
  • Artículo
    Towards a Semantics-Aware Code Transformation Toolchain for Heterogeneous Systems
    Tamarit, Salvador; Mariño, Julio; Vigueras, Guillermo; Carro, Manuel. Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016), 2016-09-02.
    Obtaining good performance when programming heterogeneous computing platforms poses significant challenges. We present a program transformation environment, implemented in Haskell, where architecture-agnostic scientific C code with semantic annotations is transformed into functionally equivalent code better suited for a given platform. The transformation steps are represented as rules which can be fired when certain syntactic and semantic conditions are fulfilled. These rules are not hard-wired into the rewriting engine: they are written in a C-like language and are automatically processed and incorporated by the rewriting engine. That makes it possible for end-users to add their own rules or to provide sets of rules which are adapted to certain specific domains or purposes.
  • Artículo
    Towards Automatic Learning of Heuristics for Mechanical Transformations of Procedural Code
    Vigueras, Guillermo; Carro, Manuel; Tamarit, Salvador; Mariño, Julio. Actas de las XVI Jornadas de Programación y Lenguajes (PROLE 2016), 2016-09-02.
    The current trends in next-generation exascale systems go towards integrating a wide range of specialized (co-)processors into traditional supercomputers. Due to the efficiency of heterogeneous systems in terms of Watts and FLOPS per surface unit, opening the access of heterogeneous platforms to a range of users as wide as possible is an important problem to be tackled. However, the integration of heterogeneous, specialized devices increases programming complexity, restricting it to a few experts, and makes porting applications onto different computational infrastructures extremely costly. In order to bridge the gap between the programming needs of heterogeneous systems and the expertise of programmers, program transformation has been proposed elsewhere as a means to ease program generation and adaptation. This brings about several issues such as how to plan a transformation strategy which eventually generates code with increased performance. 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 this approach.