ProCTA: program characteristic-based thread partition approach

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. To tackle the problem that the conventional heuristic rule-based (HR-based) thread partition approach partitions programs of different characteristics with the same scheme and several programs have bad partition results, this paper proposes a program characteristic-based thread partition approach (ProCTA), which uses a machine learning method to learn the knowledge of thread partition from TLS sample set and predicts thread partition schemes for unknown programs in accordance with programs’ characteristics and finally applies the schemes to thread partition. In Prophet compilation system, Olden benchmarks are used to evaluate ProCTA, and a comparison is made between ProCTA and conventional heuristic rules-based partition approach. The experimental results show that the proposed approach can deliver an average 18.24% speedup improvement than HR-based thread partition approach.