Towards Modelling Parallelism and Energy Performance of Multicore Systems

Multicore systems are increasingly adopted across many application domains. Consequently, understanding their performance is becoming an important issue for a growing number of users. However, performance analysis of parallel programs on multicore systems is still challenging, especially for large programs or applications developed in multiple programming languages. This paper proposes an analytical modelling approach for studying the parallelism and energy performance of shared-memory programs on multicore systems. The proposed model derives the speedup and speedup loss from data dependency and memory overhead in traditional UMA and NUMA multicore systems, and emerging platforms such as ARM multicores. Using only widely available inputs derived from the trace of the operating system run-queue and hardware events counters, the proposed model achieves high practicality and generality across many types of shared-memory programs running on different multicore platforms. Applications of the model include understanding achieved speedup and parallelism loss, and prediction of optimal core and memory configuration, where the optimality criteria is minimum execution time, minimum energy usage or a trade-off between these two.