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Autonomic Parallelism Adaptation for Software Transactional Memory

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Parallel programs need to manage the time trade-off between synchronization and computation. A high parallelism may decrease computing time but meanwhile increase synchronization cost. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. A way to reduce conflicts is by adjusting parallelisms. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is costly and error-prone. Hence, it becomes necessary to adopt a dynamical tuning-configuration strategy to better manage a STM system. Autonomic computing offers designers a framework of methods and techniques to build systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low.
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hal-01312786 , version 1 (09-05-2016)


  • HAL Id : hal-01312786 , version 1


Naweiluo Zhou, Gwenaël Delaval, Bogdan Robu, Éric Rutten, Jean-François Méhaut. Autonomic Parallelism Adaptation for Software Transactional Memory. ComPAS 2016 - Conférence francophone d'informatique en parallélisme, architecture et système, Jul 2016, Lorient, France. ⟨hal-01312786⟩
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