Measuring the performance of interactive applications with listener latency profiling

M. Jovic, Matthias Hauswirth
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引用次数: 28

Abstract

When Java developers need to improve the performance of their applications, they usually use one of the many existing profilers for Java. These profilers generally capture a profile that represents the execution time spent in each method. The developer can thus focus her optimization efforts on the methods that consume the most time. In this paper we argue that this type of profile is insufficient for tuning interactive applications. Interactive applications respond to user events, such as mouse clicks and key presses. The perceived performance of interactive applications is directly related to the response time of the program. In this paper we present listener latency profiling, a profiling approach with two distinctive characteristics. First, we call it latency profiling because it helps developers to find long latency operations. Second, we call it listener profiling because it abstracts away from method-level profiles to compute the latency of the various listeners. This allows a developer to reason about performance with respect to listeners, also called observers, the high level abstraction at the core of any interactive Java application. We present our listener latency profiling approach, describe LiLa, our implementation, validate it on a set of micro-benchmarks, and evaluate it on a complex real-world interactive application.
使用侦听器延迟分析测量交互式应用程序的性能
当Java开发人员需要改进其应用程序的性能时,他们通常使用现有的Java分析器之一。这些分析器通常捕获一个表示在每个方法中花费的执行时间的概要文件。因此,开发人员可以将优化工作集中在消耗最多时间的方法上。在本文中,我们认为这种类型的配置文件不足以调优交互式应用程序。交互式应用程序响应用户事件,例如鼠标单击和按键。交互式应用程序的感知性能与程序的响应时间直接相关。在本文中,我们提出了侦听器延迟分析,这是一种具有两个显著特征的分析方法。首先,我们称之为延迟分析,因为它可以帮助开发人员找到长延迟操作。其次,我们称之为侦听器分析,因为它从方法级配置文件中抽象出来,计算各种侦听器的延迟。这允许开发人员根据侦听器(也称为观察器)来推断性能,侦听器是任何交互式Java应用程序的核心高级抽象。我们介绍了我们的侦听器延迟分析方法,描述了LiLa,我们的实现,在一组微基准测试中验证了它,并在一个复杂的现实世界交互式应用程序中对它进行了评估。
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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