Computer Science

Graduate Research Forum Details

Design and Implementation of a Parallel Performance Data Management Framework

Abstract

Empirical performance evaluation of parallel systems and applications can generate significant amounts of performance data and analysis results from multiple experiments as performance is investigated and problems diagnosed. Hence, the management of performance information is a core component of performance analysis tools. To better support tool integration, portability, and reuse, there is a strong motivation to develop performance data management technology that can provide a common foundation for performance data storage, access, merging, and analysis. This paper presents the design and implementation of the Performance Data Management Framework (PerfDMF). PerfDMF addresses objectives of performance tool integration, interoperation, and reuse by providing common data storage, access, and analysis infrastructure for parallel performance profiles. PerfDMF includes an extensible parallel profile data schema and relational database schema, a profile query and analysis programming interface, and an extendible toolkit for profile import/export and standard analysis. We describe the PerfDMF objectives and architecture, give detailed explanation of the major components, and show examples of PerfDMF application.