Adaptive Software Speculation for Enhancing the Cost-Efficiency of Behavior-Oriented Parallelization

Abstract

Recently, software speculation has shown promising results in parallelizing complex sequential programs by exploiting dynamic high-level parallelism. The speculation however is cost-inefficient. Failed speculations may cause unnecessary shared resource contention, power consumption, and interference to co-running applications. In this work, we propose adaptive speculation and design two algorithms to predict the profitability of a speculation and dynamically disable and enable the speculation of a region. Experimental results demonstrate significant improvement of computation efficiency without performance degradation. The adaptive speculation can also enhance the usability of behavior-oriented parallelization by allowing more flexibility in labeling possibly parallel regions.