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Titlebook: Smart Education and e-Learning 2018; Vladimir L. Uskov,Robert J. Howlett,Ljubo Vlacic Conference proceedings 2019 Springer International P

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cted during the verification of the optimality property of the program. We describe a proof-of-concept implementation of our approach for auto-tuning programs written in OpenCL – the standard for programming modern multi-core CPU (Central Processing Units) and many-core GPU (Graphics Processing Unit
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Vladimir L. Uskov,Jeffrey P. Bakken,Ashok Shah,Timothy Krock,Alexander Uskov,Jitendra Syamala,Rama Rcted during the verification of the optimality property of the program. We describe a proof-of-concept implementation of our approach for auto-tuning programs written in OpenCL – the standard for programming modern multi-core CPU (Central Processing Units) and many-core GPU (Graphics Processing Unit
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Karsten Henke,Heinz-Dietrich Wuttke,René Hutschenreuter,Aleander Kistources (as if they were a single source) must be feasible. We present a detailed description of a code expansion-based framework that proposes novel solutions for these problems. We argue that the approach, while implemented for Ciao, can be adapted for other Prolog-based systems and languages.
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Aneesha Bakharia,Marco Lindleyources (as if they were a single source) must be feasible. We present a detailed description of a code expansion-based framework that proposes novel solutions for these problems. We argue that the approach, while implemented for Ciao, can be adapted for other Prolog-based systems and languages.
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Jeffrey P. Bakken,Vladimir L. Uskov,Narmada Rayala,Jitendra Syamala,Ashok Shah,Lavanya Aluri,Karnika also facilitates the combination with static analysis. The generation process is based on running standard predicates under non-standard (random) search rules. Generation can be fully automatic but can also be guided or defined specifically by the user. We propose methods for supporting (C)LP-speci
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Marina V. Lapenok,Olga M. Patrusheva,Galina V. Pokhodzey,Anastasiya I. Suetina,Anna M. Lozinskaya,Ir wide range of resources, platforms, and execution models. The analysis estimates both lower and upper bounds on the resource usage of a parallel program (without executing it) as functions on input data sizes. In addition, it also infers other meaningful information to better exploit and assess the
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