针对OpenMP多线程系统性能异常的机器学习框架
Machine Learning Framwork for Performance Anomaly in OpenMP Multi-Threaded Systems
摘要：
由于共享资源争用以及与软件和硬件相关的问题，一些OpenMP多线程应用程序越来越多地遭受性能异常。这种性能异常可能导致故障和效率低下，并且是系统弹性的主要挑战之一。为了最大限度地减少性能异常的影响，必须快速准确地检测和诊断导致故障的性能异常。然而，这是很难识别的动态和嘈杂的数据收集的OpenMP多线程监控基础设施的异常。提出了一种新的机器学习框架，用于诊断OpenMP多线程系统中的性能异常。为了评估我们的框架，NAS并行NPB基准测试，EPCC OpenMP微基准测试套件，和Jacobi基准测试我们提出的框架的性能。实验结果表明，该框架成功地识别了90.3%的OpenMP多线程应用程序注入异常。
英文摘要：
Some OpenMP multi-threaded applications increasingly suffer from performance anomaly owning to shared resource contention as well as software- and hardware-related problems. Such performance anomaly can result in failure and inefficiencies, and are among the main challenges in system resiliency. To minimize the impact of performance anomaly, one must quickly and accurately detect and diagnose the performance anomalies that cause the failures. However, it is difficult to identify anomalies in the dynamic and noisy data collected by OpenMP multi-threaded monitoring infrastructures. This paper presents a novel machine learning framework for performance anomaly in OpenMP multi-threaded systems. To evaluate our framework, the NAS Parallel NPB benchmark, EPCC OpenMP micro-benchmark suite, and Jacobi benchmark are used to test the performance of our framework proposed. The experimental results demonstrate that our framework successfully identifies 90.3\% of injected anomalies of OpenMP multi-threaded applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
