Dynamic Thread Mapping Based on Machine Learning for Transactional Memory Applications

Abstract : Thread mapping is an appealing approach to efficiently exploit the potential of modern chip-multiprocessors. However, efficient thread mapping relies upon matching the behavior of an application with system characteristics. In particular, Software Transactional Memory (STM) introduces another dimension due to its runtime system support. In this work, we propose a dynamic thread mapping approach to automatically infer a suitable thread mapping strategy for transactional memory applications composed of multiple execution phases with potentially different transactional behavior in each phase. At runtime, it profiles the application at specific periods and consults a decision tree generated by a Machine Learning algorithm to decide if the current thread mapping strategy should be switched to a more adequate one. We implemented this approach in a state-of-the-art STM system, making it transparent to the user. Our results show that the proposed dynamic approach presents performance improvements up to 31% compared to the best static solution.
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Communication dans un congrès
International European Conference on Parallel and Distributed Computing (Euro-Par), 2012, Rhodes Island, Greece, Greece. Springer-Verlag, 7484, pp.465-476, 2012, Lecture Notes in Computer Science (LNCS). 〈10.1007/978-3-642-32820-6_47〉
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https://hal.inria.fr/hal-00788013
Contributeur : Arnaud Legrand <>
Soumis le : mercredi 13 février 2013 - 15:03:08
Dernière modification le : jeudi 11 janvier 2018 - 06:21:39

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Márcio Castro, Luís Fabrício Góes, Luiz Gustavo Fernandes, Jean-François Mehaut. Dynamic Thread Mapping Based on Machine Learning for Transactional Memory Applications. International European Conference on Parallel and Distributed Computing (Euro-Par), 2012, Rhodes Island, Greece, Greece. Springer-Verlag, 7484, pp.465-476, 2012, Lecture Notes in Computer Science (LNCS). 〈10.1007/978-3-642-32820-6_47〉. 〈hal-00788013〉

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