HPAS: An HPC Performance Anomaly Suite for Reproducing Performance Variations
HPAS: An HPC Performance Anomaly Suite for Reproducing Performance Variations
复制标题
HPAS：用于重现性能变化的 HPC 性能异常套件
DOI：
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发表时间：
2019
期刊：
影响因子：
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通讯作者：
A. Coskun
中科院分区：
文献类型：
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作者：
E. Ates;Yijia Zhang;Burak Aksar;J. Brandt;V. Leung;Manuel Egele;A. Coskun
包括超级计算机的现代高性能计算（HPC）系统通常遭受实质性的性能变化。具有相同输入的相同应用程序可能会产生超过100%的性能变化，这种变化会导致效率降低和资源浪费。最近有关于性能可变性和设计用于诊断导致性能可变性的“异常”的自动化方法的研究。这些研究要么观察从HPC系统收集的数据，要么依赖于性能变化场景的合成再现。然而，没有标准化的方法来创建性能变异诱导合成异常，所以，研究人员依赖于设计特设的方法来再现性能变异。本文解决了这种缺乏一个通用的方法来创建相关的性能异常，通过引入HPAS，HPC性能异常套件，在HPC系统中的主要子系统的异常发生器组成。这些易于使用的合成异常生成器有助于在实际性能变化情况下对各种分析方法以及应用程序、中间件或系统的性能或弹性进行低成本评估和比较。本文还分析了异常生成器的行为，并演示了几个用例：（1）使用HPAS进行性能异常诊断，（2）在性能变化下评估资源管理策略，以及（3）设计对性能变化有弹性的应用程序。
Modern high performance computing (HPC) systems, including supercomputers, routinely suffer from substantial performance variations. The same application with the same input can have more than 100% performance variation, and such variations cause reduced efficiency and wasted resources. There have been recent studies on performance variability and on designing automated methods for diagnosing "anomalies" that cause performance variability. These studies either observe data collected from HPC systems, or they rely on synthetic reproduction of performance variability scenarios. However, there is no standardized way of creating performance variability inducing synthetic anomalies; so, researchers rely on designing ad-hoc methods for reproducing performance variability. This paper addresses this lack of a common method for creating relevant performance anomalies by introducing HPAS, an HPC Performance Anomaly Suite, consisting of anomaly generators for the major subsystems in HPC systems. These easy-to-use synthetic anomaly generators facilitate low-effort evaluation and comparison of various analytics methods as well as performance or resilience of applications, middleware, or systems under realistic performance variability scenarios. The paper also provides an analysis of the behavior of the anomaly generators and demonstrates several use cases: (1) performance anomaly diagnosis using HPAS, (2) evaluation of resource management policies under performance variations, and (3) design of applications that are resilient to performance variability.
影响因子：
1.1
作者：
Araújo-Filho, Irami;Rêgo, Amália Cínthia Meneses;Medeiros, Aldo Cunha
通讯作者：
Medeiros, Aldo Cunha