Thread-local concurrency: a technique to handle data race detection at programming model abstraction
Thread-local concurrency: a technique to handle data race detection at programming model abstraction
复制标题
线程本地并发：一种在编程模型抽象中处理数据竞争检测的技术
DOI：
10.1145/3208040.3208056
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
Müller
中科院分区：
文献类型：
--
作者：
Protze;Schulz;Müller
随着越来越多地采用各种高级并行编程模型来利用节点上的并行性，准确的数据竞争检测变得比以往任何时候都更加重要。然而，现有的工具很难通过这些高级模型发现数据竞争，因为它们主要针对低级并发执行模型（例如，在POSIX线程级别表示的并发性）。在本文中，我们提出了一种新的技术，以准确地检测这些数据竞争，可以发生在更高级别的并发执行。我们的技术的核心思想是引入线程本地并发（TLC）的一般概念，作为一种新的方式来翻译的并发表示的高层次的编程范式到低的执行级别理解现有的工具。具体来说，我们扩展了向量时钟的定义，使现有的国家的最先进的比赛检测器，以识别这些比赛发生在更高级别的并发性与这些工具的微小修改。我们对ThreadSanitizer中实现的原型进行的评估表明，TLC可以允许现有工具准确地检测这些种族，只需很少的额外分析开销。
With greater adoption of various high-level parallel programming models to harness on-node parallelism, accurate data race detection has become more crucial than ever. However, existing tools have great difficulty spotting data races through these high-level models, as they primarily target low-level concurrent execution models (e.g., concurrency expressed at the level of POSIX threads). In this paper, we propose a novel technique to accurately detect those data races that can occur at higher levels of concurrent execution. The core idea of our technique is to introduce the general concept of Thread-Local Concurrency (TLC) as a new way to translate the concurrency expressed by a high-level programming paradigm into the low execution level understood by the existing tools. Specifically, we extend the definition of vector clocks to allow the existing state-of-the-art race detectors to recognize those races that occur at the higher level of concurrency with minor modifications to these tools. Our evaluation with our prototype implemented within ThreadSanitizer shows that TLC can allow the existing tool to detect these races accurately with only small additional analysis overheads.
VORD：OpenMP 程序中的多功能动态竞争检测工具
影响因子：
1.5
作者：
Young;Sejun Song;Yong
通讯作者：
Yong
针对异步完成并行性的高效数据竞争检测
影响因子：
0.8
作者：
Raghavan Raman;Jisheng Zhao;Vivek Sarkar;Martin T. Vechev;Eran Yahav
通讯作者：
Eran Yahav