Identifying the optimal level of parallelism in transactional memory applications

Abstract

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 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.