A Top-Down Performance Analysis Methodology for Workflows: Tracking Performance Issues from Overview to Individual Operations

2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS) Pub Date : 2019-11-01 DOI:10.1109/WORKS49585.2019.00008

Ronny Tschüter, C. Herold, William Williams, Maximilian Knespel, Matthias Weber

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引用次数: 3

Abstract

Scientific workflows are well established in parallel computing. A workflow represents a conceptual description of work items and their dependencies. Researchers can use workflows to abstract away implementation details or resources to focus on the high-level behavior of their work items. Due to these abstractions and the complexity of scientific workflows, finding performance bottlenecks along with their root causes can quickly become involving. This work presents a top-down methodology for performance analysis of workflows to support users in this challenging task. Our work provides summarized performance metrics covering different workflow perspectives, from general overview to individual jobs and their job steps. These summaries allow to identify inefficiencies and determine the responsible job steps. In addition, we record detailed performance data about job steps, enabling a fine-grained analysis of the associated execution to exactly pinpoint performance issues. The introduced methodology provides a powerful tool for comprehensive performance analysis of complex workflows.

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工作流的自顶向下性能分析方法:从总体到单个操作跟踪性能问题

科学工作流程在并行计算中得到了很好的建立。工作流表示工作项及其依赖关系的概念性描述。研究人员可以使用工作流抽象出实现细节或资源，以关注其工作项的高级行为。由于这些抽象和科学工作流的复杂性，找到性能瓶颈及其根本原因可能很快就会变得复杂起来。这项工作提出了一种自顶向下的工作流性能分析方法，以支持用户完成这项具有挑战性的任务。我们的工作提供了涵盖不同工作流透视图的总结性能指标，从总体概述到单个作业及其作业步骤。这些总结有助于识别效率低下的地方，并确定负责任的工作步骤。此外，我们还记录了有关作业步骤的详细性能数据，从而能够对相关执行进行细粒度分析，从而精确定位性能问题。所介绍的方法为复杂工作流的综合性能分析提供了一个强大的工具。

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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2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS)

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