Folding Methods for Event Timelines in Performance Analysis

2015 IEEE International Parallel and Distributed Processing Symposium Workshop Pub Date : 2015-05-25 DOI:10.1109/IPDPSW.2015.47

Matthias Weber, Ronald Geisler, H. Brunst, W. Nagel

引用次数: 4

Abstract

The complexity of today's high performance computing systems, and their parallel software, requires performance analysis tools to fully understand application performance behavior. The visualization of event streams has proven to be a powerful approach for the detection of various types of performance problems. However, visualization of large numbers of process streams quickly hits the limits of available screen resolution. To alleviate this problem we propose folding strategies for event timelines that consider common questions during performance analysis. We demonstrate the effectiveness of our solution with code inefficiencies in two real-world applications, PIConGPU and COSMO-SPECS. Our methods facilitate visual scalability and provide powerful overviews of performance data at the same time. Furthermore, our folding strategies improve GPU stream visualization and allow easy evaluation of the GPU device utilization.

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性能分析中事件时间线的折叠方法

当今高性能计算系统及其并行软件的复杂性需要性能分析工具来完全理解应用程序的性能行为。事件流的可视化已被证明是检测各种类型性能问题的强大方法。然而，大量流程流的可视化很快就会达到可用屏幕分辨率的极限。为了缓解这个问题，我们提出了考虑性能分析过程中常见问题的事件时间线折叠策略。我们在两个实际应用程序PIConGPU和cosmos - spec中演示了我们的解决方案在代码效率低下方面的有效性。我们的方法促进了可视化的可伸缩性，同时提供了强大的性能数据概述。此外，我们的折叠策略改进了GPU流可视化，并允许轻松评估GPU设备利用率。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2015 IEEE International Parallel and Distributed Processing Symposium Workshop

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