Tuning the Level of Concurrency in Software Transactional Memory: An Overview of Recent Analytical, Machine Learning and Mixed Approaches

Synchronization transparency offered by Software Transactional Memory (STM) must not come at the expense of run-time efficiency, thus demanding from the STM-designer the inclusion of mechanisms properly oriented to performance and other quality indexes. Particularly, one core issue to cope with in STM is related to exploiting parallelism while also avoiding thrashing phenomena due to excessive transaction rollbacks, caused by excessively high levels of contention on logical resources, namely concurrently accessed data portions. A means to address run-time efficiency consists in dynamically determining the best-suited level of concurrency (number of threads) to be employed for running the application (or specific application phases) on top of the STM layer. For too low levels of concurrency, parallelism can be hampered. Conversely, over-dimensioning the concurrency level may give rise to the aforementioned thrashing phenomena caused by excessive data contention—an aspect which has reflections also on the side of reduced energy-efficiency. In this chapter we overview a set of recent techniques aimed at building “application-specific” performance models that can be exploited to dynamically tune the level of concurrency to the best-suited value. Although they share some base concepts while modeling the system performance vs the degree of concurrency, these techniques rely on disparate methods, such as machine learning or analytic methods (or combinations of the two), and achieve different tradeoffs in terms of the relation between the precision of the performance model and the latency for model instantiation. Implications of the different tradeoffs in real-life scenarios are also discussed

Advanced concurrency control for transactional memory using transaction commit rate

Abstract. Concurrency control for Transactional Memory (TM) is investigated as a means for improving resource usage by adjusting dynamically the number of threads concurrently executing transactions. The proposed control system takes as feedback the measured Transaction Commit Rate to adjust the concurrency. Through an extensive evaluation, a new Concurrency Control Algorithm (CCA), called P-only Concurrency Control (PoCC), is shown to perform better than our other four proposed CCAs for a synthetic benchmark, and the STAMP and Lee-TM benchmarks.

Steal-on-abort: Improving transactional memory performance through dynamic transaction reordering

Abstract. In transactional memory, aborted transactions reduce performance, and waste computing resources. Ideally, concurrent execution of transactions should be optimally ordered to minimise aborts, but such an ordering is often either complex, or unfeasible, to obtain. This paper introduces a new technique called steal-on-abort, which aims to improve transaction ordering at runtime. Suppose transactions A and B conflict, and B is aborted. In general it is difficult to predict this first conflict, but once observed, it is logical not to execute the two transactions concurrently again. In steal-on-abort, the aborted transaction B is stolen by its opponent transaction A, and queued behind A to prevent concurrent execution of A and B. Without steal-on-abort, transaction B would typically have been restarted immediately, and possibly had a repeat conflict with transaction A. Steal-on-abort requires no application-specific information, modification, or offline pre-processing. In this paper, it is evaluated using a sorted linked list, red-black tree, STAMP-vacation, and Lee-TM. The evaluation reveals steal-on-abort is highly effective at eliminating repeat conflicts, which reduces the amount of computing resources wasted, and significantly improves performance.