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Dynamic Thread Mapping Based on Machine Learning for Transactional Memory Applications

  • Márcio Castro
  • Luís Fabrício Wanderley Góes
  • Luiz Gustavo Fernandes
  • Jean-François Méhaut
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7484)

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.

Keywords

transactional memory dynamic thread mapping machine learning 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Márcio Castro
    • 1
  • Luís Fabrício Wanderley Góes
    • 2
  • Luiz Gustavo Fernandes
    • 3
  • Jean-François Méhaut
    • 1
  1. 1.INRIA - CEA - LIG LaboratoryGrenoble UniversityMontbonnot Saint MartinFrance
  2. 2.Department of Computer SciencePontifical Catholic University of Minas GeraisBelo HorizonteBrazil
  3. 3.PPGCC - Pontifical Catholic University of Rio Grande do SulPorto AlegreBrazil

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