Using Artificial Neural Network for Predicting Thread Partitioning in Speculative Multithreading

Abstract

Speculative multithreading (SpMT) is a thread-level automatic parallelization technique to accelerate sequential programs on multi-core, and it partitions programs into multiple threads to be speculatively executed in the presence of ambiguous data and control dependences while the correctness of the programs is guaranteed by hardware support. Thread granularity, number of parallel threads as well as partition postions are crucial to the performance improvement in SpMT, for they determine the amount of resources (CPU, memory, cache, or waiting cycles, etc), and affect the efficiency of every PE (Processing Element). In conventional way, these three parameters are determined by heuristic rules. Although it is simple to partition threads with them, they are a type of one-size-fits-all strategy and can not guarantee to get the optimal solution of thread partitioning. This paper proposes an Artificial Neural Network (ANN) based approach to learn and determine the thread partition strategy. Using the ANN-based thread partition approach, an unseen irregular program can obtain a stable, much higher speedup than the Heuristic Rules (HR) based approach. On Prophet, which is a generic SpMT processor to evaluate the performance of multithreaded programs, the novel thread partitioning policy is evaluated and reaches an average speedup of 1.80 on 4-core processor. Experiments show that our proposed approach can obtain a significant increase in speedup and Olden benchmarks deliver a better performance improvement of 2.36% than the traditional heuristic rules based approach. The results indicate that our approach finds the best partitioning scheme for each program and is more stable across programs.