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Method-level phase behavior in Java workloads

机译:Java工作负载中的方法级阶段行为

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摘要

Java workloads are becoming more and more prominent on various computing devices. Understanding the behaviour of a Java workload which includes the interaction between the application and the virtual machine (VM), is thus of primary importance during performance analysis and optimization. Moreover, as contemporary software projects are increasing in complexity, automatic performance analysis techniques are indispensable. This paper proposes an off-line method-level phase analysis approach for Java workloads that consists of three steps. In the first step, the execution time is computed for each method invocation. Using an off-line tool, we subsequently analyze the dynamic call graph (that is annotated with the method invocations' execution times) to identify method-level phases. Finally, we measure performance characteristics for each of the selected phases. This is done using hardware performance monitors. As such, our approach allows for linking microprocessor-level information at the individual methods in the Java application's source code. This is extremely interesting information during performance analysis and optimization as programmers can use this information to optimize their code. We evaluate our approach in the Jikes RVM on an IA-32 platform using the SPECjvm98 and SPECjbb2000 benchmarks. This is done according to a number of important criteria: the overhead during profiling, the variability within and between the phases, its applicability in Java workload characterization (measuring performance characteristics of the various VM components) and application bottleneck identification.
机译:Java工作负载在各种计算设备上正变得越来越重要。因此,在性能分析和优化期间,了解Java工作负载的行为(包括应用程序与虚拟机(VM)之间的交互)至关重要。此外,随着现代软件项目的复杂性不断增加,自动性能分析技术必不可少。本文提出了一种针对Java工作负载的离线方法级阶段分析方法,该方法包括三个步骤。第一步,为每个方法调用计算执行时间。随后,我们使用离线工具分析动态调用图(在方法调用的执行时间处进行注释),以确定方法级阶段。最后,我们测量每个选定阶段的性能特征。这是使用硬件性能监视器完成的。因此,我们的方法允许在Java应用程序源代码中的各个方法处链接微处理器级别的信息。在性能分析和优化期间,这是非常有趣的信息,因为程序员可以使用此信息来优化其代码。我们使用SPECjvm98和SPECjbb2000基准,在IA-32平台上的Jikes RVM中评估我们的方法。这是根据许多重要标准完成的:分析过程中的开销,各阶段之间和各阶段之间的可变性,其在Java工作负载表征中的适用性(测量各种VM组件的性能特征)和应用程序瓶颈识别。

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