On-line automated performance diagnosis on thousands of processes

Abstract

Performance analysis tools are critical for the effective use of large parallel computing resources, but existing tools have failed to address three problems that limit their scalability: (1) management and processing of the volume of performance data generated when monitoring a large number of application processes, (2) communication between a large number of tool components, and (3) presentation of performance data and analysis results for applications with a large number of processes. In this paper, we present a novel approach for finding performance problems in applications with a large number of processes that leverages our multicast and data aggregation infrastructure to address these three performance tool scalability barriers. First, we show how to design a scalable, distributed performance diagnosis facility. We demonstrate this design with an on-line,