Identifying the Optimal Level of Parallelism in Transactional Memory Applications

  • Diego Didona
  • Pascal Felber
  • Derin Harmanci
  • Paolo Romano
  • Jörg Schenker
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7853)


In this paper we investigate the issue of automatically identifying the “natural” degree of parallelism of an application using software transactional memory (STM), i.e., the workload-specific multiprogramming level that maximizes application’s performance. We discuss the importance of adapting the concurrency level to the workload in two different scenarios, a shared-memory and a distributed STM infrastructure. We propose and evaluate two alternative self-tuning methodologies, explicitly tailored for the considered scenarios. In shared-memory STM, we show that lightweight, black-box approaches relying solely on on-line exploration can be extremely effective. For distributed STMs, we introduce a novel hybrid approach that combines model-driven performance forecasting techniques and on-line exploration in order to take the best of the two techniques, namely enhancing robustness despite model’s inaccuracies, and maximizing convergence speed towards optimum solutions.


Decision Module Transactional Memory Software Transactional Memory Transactional Memory System Concurrency Level 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Diego Didona
    • 1
  • Pascal Felber
    • 2
  • Derin Harmanci
    • 2
  • Paolo Romano
    • 1
  • Jörg Schenker
    • 2
  1. 1.Instituto Superior Técnico/INESC-IDPortugal
  2. 2.University of NeuchâtelSwitzerland

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