Quantifying and Reducing Execution Variance in STM via Model Driven Commit Optimization

Abstract

Simplified parallel programming coupled with an ability to express speculative computation is realized with Software Transactional Memory (STM). Although STMs are gaining popularity because of significant improvements in parallel performance, they exhibit enormous variation in transaction execution with non-repeatable performance behavior which is unacceptable in many application domains, especially in which frame rates and responsiveness should be predictable. In other domains reproducible transactional behavior helps towards system provisioning. Thus, reducing execution variance in STM is an important performance goal that has been mostly overlooked. In this work, we minimize the variance in execution time of threads in STM by reducing non-determinism exhibited due to speculation. We define the state of STM, and we use it to first quantity non-determinism and then generate an automaton that models the execution behavior of threads in STM. We finally use the state automaton to guide the STM to avoid non-predictable transactional behavior thus reducing non-determinism in roll-backs which in turn results in reduction in variance. We observed average reduction of variance in execution time of threads up to 74% in 16 cores and 53% in 8 cores by reducing non-determinism up to 24% in 16 cores and 44% in 8 cores, respectively, on STAMP benchmark suite while experiencing average slowdown of 4.8% in 8 cores and 19.2% in 16 cores. We also reduced the variance in frame rate by maximum of 65% on a version of real world game Quake3 without degradation in timing.