HPAS

Abstract

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 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.