Machine Learning-Based Self-Adjusting Concurrency in Software Transactional Memory Systems

Diego Rughetti, Pierangelo Di Sanzo, Bruno Ciciani, Francesco Quaglia
2012 2012 IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems  
Software Transactional Memories (STM) are a promising paradigm for parallel programming on multicore platforms. One of the problems of STM systems is the performance degradation that can be experienced when applications run with a non-optimal concurrency level, namely number of concurrent threads. When this level is too high a loss of performance may occur due to excessive data contention and consequent transaction aborts. Conversely, if concurrency is too low, the performance may be penalized
more » ... ue to limitation of both parallelism and exploitation of available resources. In this paper we will present an introduction to a complete work [1], which will be published in the proceedings of the IEEE 20th International Symposium on Modeling, Analysis and Simulation of Computer And Telecommunication Systems (MASCOTS 2012), in which we propose a machine-learning based approach which enables STM systems to predict their performance as a function of the number of concurrent threads in order to dynamically select the optimal concurrency level during the whole lifetime of the application. In our approach, the STM is coupled with a neural network and an on-line control algorithm that activates or deactivates application threads in order to maximize performance via the selection of the most adequate concurrency level, as a function of the current data access profile. A real implementation of our proposal within the TinySTM open source package and an experimental study relying on the STAMP benchmark suite has been realized. The experimental data confirm how our self-adjusting concurrency scheme constantly provides optimal performance, thus avoiding performance loss phases caused by non-suited selection of the amount of concurrent threads and associated with the above depicted phenomena.
doi:10.1109/mascots.2012.40 dblp:conf/mascots/RughettiSCQ12 fatcat:4rgm3f37tzg5zdudspbkrsctxm