Adaptive speculation in behavior-oriented parallelization

Behavior-oriented parallelization is a technique for parallelizing complex sequential programs that have dynamic parallelism. Although the technique shows promising results, the software speculation mechanism it uses is not cost-efficient. Failed speculations may waste computing resource and severely degrade system efficiency. In this work, we propose adaptive speculation to predict the profitability of a speculation and dynamically enable or disable the speculation of a region. Experimental results demonstrate the effectiveness of the scheme in improving the efficiency of software speculation. In addition, the adaptive speculation can also enhance the usability of behavior-oriented parallelization by allowing users to label potential parallel regions more flexibly.