Using sample-based time series data for automated diagnosis of scalability losses in parallel programs

Abstract

The performance of many parallel applications has failed to scale as fast as successive generations of hardware on which these applications execute. To understand the cause of scalability losses, experts use performance tools to monitor and analyze application behavior. Profiles generated by performance tools can usually indicate the presence of scalability losses while time series data are generally necessary to pinpoint the root causes of such losses. However, manual analysis of time series data can be difficult in executions with a large number of processes, long running times, and deep call chains. This paper describes an automated framework that analyzes sample-based time series data to diagnose scalability losses in parallel executions. The framework’s automated diagnosis of scalability losses indicates their symptoms, severity, and causes. Two case studies illustrate the effectiveness of this framework. When compared to a tool that analyzes performance using instrumentation-based traces, our overhead for collecting sample-based time series is 1/28 in time and 1/1600 in space while our automated analysis takes 1/25 of the time.