Using adaptive runtime filtering to support an event‐based performance analysis

Abstract

Summary Event‐based performance monitoring and analysis are effective means when tuning parallel applications for optimal resource usage. In this article, we address the data capacity challenge that arises when applying the tracing methodology to large‐scale parallel applications and long execution times. Existing approaches use static, pre‐defined event filters to reduce the performance data to a manageable size. In contrast, we propose self‐guided filters that automatically adapt to an application's runtime behaviour and therefore, do not require any previous knowledge or application executions. Our contribution consists of four adaptive runtime filters, which target a specific type of data redundancy each. The filters focus on detecting identical events in loop iterations, constant events with no variation in time, and very short, highly frequent, typically not very meaningful events, having a severe impact on the total data volume. We evaluate our prototype implementation with five real‐world applications and achieve a data reduction of two orders of magnitude while increasing execution time less than 1%. Likewise, we show that the qualitative impact of our filters on performance analysis in state‐of‐the‐art analysis tools can be reduced by adding feedback methods and statistical information to the filtered traces. Copyright © 2017 John Wiley & Sons, Ltd.

Foreword to the special issue of the 18th IEEE international conference on computational science and engineering (CSE2015)

“…The article ‘Using Adaptive Runtime Filtering to Support an Event‐based Performance Analysis’ presents an approach to filter tracing data in the context of event‐based monitoring for performance improvements. The approach is based on self‐guided filters that automatically adapt to an application's runtime behavior and are able to reduce performance data to manageable sizes for large‐scale parallel applications and long execution programs.…”