Software Transactional Memory (STM) stands as powerful concurrent programming paradigm, enabling atomicity, and isolation while accessing shared data. On the downside, STM may suffer from performance degradation due to excessive conflicts among concurrent transactions, which cause waste of CPU-cycles and energy because of transaction aborts. An approach to cope with this issue consists of putting in place smart scheduling strategies which temporarily suspend the execution of some transaction in order to reduce the transaction conflict rate. In this article, we present an adaptive model-based transaction scheduling technique relying on a Markov Chain-based performance model of STM systems. Our scheduling technique is adaptive in a twofold sense: (i) It controls the execution of transactions depending on throughput predictions by the model as a function of the current system state. (ii) It re-tunes on-line the Markov Chain-based model to adapt it—and the outcoming transaction scheduling decisions—to dynamic variations of the workload. We have been able to achieve the latter target thanks to the fact that our performance model is extremely lightweight. In fact, to be recomputed, it requires a reduced set of input parameters, whose values can be estimated via a few on-line samples related to the current workload dynamics. We also present a scheduler that implements our adaptive technique, which we integrated within the open source TinySTM package. Further, we report the results of an experimental study based on the STAMP benchmark suite, which has been aimed at assessing both the accuracy of our performance model in predicting the actual system throughput and the advantages of the adaptive scheduling policy over literature techniques.

中文翻译：

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软件事务内存中基于自适应模型的调度

软件事务内存 (STM) 是强大的并发编程范式，可在访问共享数据时实现原子性和隔离性。不利的一面是，STM 可能会因并发事务之间的过度冲突而导致性能下降，从而导致由于事务中止而导致 CPU 周期和能量的浪费。解决此问题的一种方法是采用智能调度策略，临时暂停某些事务的执行以降低事务冲突率。在本文中，我们提出了一种基于自适应模型的事务调度技术，该技术依赖于基于马尔可夫链的 STM 系统性能模型。我们的调度技术在双重意义上是自适应的：(i) 它根据模型作为当前系统状态的函数的吞吐量预测来控制事务的执行。(ii) 它在线重新调整基于马尔可夫链的模型，使其——以及输出的事务调度决策——适应工作负载的动态变化。由于我们的性能模型非常轻量级，我们能够实现后一个目标。事实上，要重新计算，它需要一组简化的输入参数，其值可以通过一些与当前工作负载动态相关的在线样本来估计。我们还提供了一个调度程序，它实现了我们的自适应技术，我们将其集成在开源 TinySTM 包中。此外，我们报告了基于 STAMP 基准套件的实验研究结果，