Control Of Autonomic Parallelism Adaptation On Software Transactional Memory

Parallel programs need to manage the trade-off between the time spent in synchronization and computation. A high parallelism may decrease computing time while increase synchronization cost among threads. A way to improve program performance is to adjust parallelism to balance conflicts among threads. However, there is no universal rule to decide the best parallelism for a program from an offline view. Furthermore, an offline tuning is error-prone. Hence, it becomes necessary to adopt a dynamic tuning-configuration strategy to better manage a STM system. Software Transactional Memory (STM) has emerged as a promising technique, which bypasses locks, to address synchronization issues through transactions. Autonomic computing offers designers a framework of methods and techniques to build automated systems with well-mastered behaviours. Its key idea is to implement feedback control loops to design safe, efficient and predictable controllers, which enable monitoring and adjusting controlled systems dynamically while keeping overhead low. We propose to design feedback control loops to automate the choice of parallelism level at runtime to diminish program execution time.