关键词: 线程级推测, 线程划分, 程序特征, 划分方案

Abstract: As a thread-level automatic parallelization technique, thread-level speculation (TLS) can partition irregular serial programs into multiple threads and implement these threads in parallel on multi-core architectures to improve the performance of programs. In view of the problem that the conventional heuristic rules-based (HR-based) thread partitioning method partitions the programs of different characteristics with the same scheme and several programs have bad partitioning effects, this paper proposes a machine learning based (ML-based) thread partitioning  approach, which uses a machine learning method to learn the knowledge of thread partitioning from TLS sample set, and predicts the schemes of thread partitioning for unknown programs, and finally applies the schemes to thread partitioning. In Prophet compilation system, Olden benchmarks are used to evaluate the proposed approach, and a comparison is made between the proposed approach and conventional heuristic rules-based partitioning approach. The experimental results show that the proposed approach can deliver an average 18.24% speedup improvement than HR-based thread partitioning approach.

Key words: thread-level speculation, thread partitioning, program characteristics, partition scheme