Exploiting Thread-Level Parallelism Based on Balancing Load for Speculative Multithreading

Abstract

Speculative Multithreading (SpMT) technology is an effective mechanism for automatic parallelization of irregular programs. While speculative parallelization can potentially deliver significant speedup for irregular applications, several speculative parallelization overheads resulting from the factors, especially inter-thread load imbalance, limit these speedups in practice. Most existing thread partitioning methods are mainly based on heuristic rules strategies to generate speculative threads. However, these heuristic rules extracted from people’s experiences cannot estimate quantitatively but qualitatively the overhead. Based on the thorough analysis of the speculative parallelization overhead resulting from inter-thread load imbalance, we propose a novel method to balance the inter-thread load. In this method, we firstly determine a method by which to unroll the loops. Then, we introduce cluster method to search the solution space of speculative threads and finally get the optimal solution. The experimental results show that, the proposed method can effectively reduce the inter-thread load imbalance; and the load imbalance overhead can be effectively reduced by 43.7%. And we can gain 8.8% performance improvement on Olden benchmark suits.