A Machine LearningBased Approach for Thread Mapping on Transactional Memory Applications

Abstract—Thread mapping has been extensively used as a technique to efficiently exploit memory hierarchy on modern chip-multiprocessors. It places threads on cores in order to amortize memory latency and/or to reduce memory contention. However, efficient thread mapping relies upon matching ap-plication behavior with system characteristics. Particularly, Software Transactional Memory (STM) applications introduce another dimension due to its runtime system support. Existing STM systems implement several conflict detection and reso-lution mechanisms, which leads STM applications to behave differently for each combination of these mechanisms. In this paper we propose a machine learning-based approach to automatically infer a suitable thread mapping strategy for transactional memory applications. First, we profile sev-eral STM applications from the STAMP benchmark suite considering application, STM system and platform features to build a set of input instances. Then, such data feeds a machine learning algorithm, which produces a decision tree able to predict the most suitable thread mapping strategy for new unobserved instances. Results show that our approach improves performance up to 18.46 % compared to the worst case and up to 6.37 % over the Linux default thread mapping strategy. Keywords-machine learning; software transactional memory; thread mapping. I