Performance Data Extrapolation in Parallel Codes

Abstract

Measuring the performance of parallel codes is a compromise between lots of factors. The most important one is which data has to be analyzed. Current supercomputers are able to run applications in large number of processors as well as the analysis data that can be extracted is also large and varied. That implies a hard compromise between the potential problems one want to analyze and the information one is able to capture during the application execution. In this paper we present an extrapolation methodology to maximize the information extracted in a single application execution. It is based on a structural characterization of the applications, performed using clustering techniques, the ability to multiplex the read of performance hardware counters, plus a projection process. As a result, we obtain the approximated values of a large set of metrics for each phase of the application, with minimum error.