Measuring Effective Work to Reward Success in Dynamic Transaction Scheduling

One of the greatest challenges of modern computing is the development of software optimized for parallel execution in multi-core processors. Transactional Memory (TM) is a new trend in concurrency control that has emerged to address these challenges. TM promises the performance of finer grain locks combined with lower programming complexity. However, transactional memories are speculative and rely on contention managers to resolve conflicts between transactions. This paper explores a complementary approach to boost the performance of TM through the use of schedulers. A TM scheduler is a software component that decides when a particular transaction should be executed. TM scheduling mechanisms are typically restricted to either serialization or yielding. Moreover, their effectiveness is very sensitive to the accuracy of the metric used to predict transaction behavior, particularly in high-contention scenarios. This paper proposes a new Dynamic Transaction Scheduler (DTS) to select a transaction to execute next, based on a new policy that rewards success and uses an improved metric that measures the amount of effective work performed by a transaction. An experimental evaluation indicates that scheduling transactions based on DTS can provide good average-case performance.