Software Transactional Memory (STM) is recognized as an effective programming paradigm for concurrent applications. On the other hand, a core problem to cope with in STM deals with (dynamically) regulating the degree of concurrency, in order to deliver optimal performance. We address this problem by proposing a self-regulation approach of the concurrency level, which relies on a parametric analytical performance model aimed at predicting the scalability of the STM application as a function of the actual workload profile. The regulation scheme allows achieving optimal performance during the whole lifetime of the application via dynamic change of the number of concurrent threads according to the predictions by the model. The latter is customized for a specific application/platform through regression analysis, which is based on a lightweight sampling phase. We also present a real implementation of the model-based concurrency self-regulation architecture integrated within the open source TinySTM framework, and an experimental study based on standard STM benchmark applications.
展开▼
