对Java应用程序的分析和跟踪支持

A. Nisbet, N. Nobre, G. Riley, M. Luján
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引用次数: 6

摘要

我们用以下方法演示了jvm性能评估的可行性:(1)混合JVM/OS工具,如async-profiler;(2)基于Linux perf的以OS为中心的分析和跟踪工具;(3)扩展伯克利包过滤跟踪(eBPF)框架,其中我们展示了标准off - waketime工具背后的基本原理,用于分析阻塞延迟的原因;以及我们自己的基于eBPF的工具bcc-java,该工具将微架构性能计数器值的变化与低开销的单个JVM和应用程序线程的执行联系起来。在运行Ubuntu 16.04的Intel Xeon E5-2690上使用OpenJDK9进行DaCapo-bach-9.12基准测试,说明了性能工具的相对执行时间开销。尽管基于采样的工具在使用4kHz频率时可以有高达25%的减速,但我们的工具bcc-java的几何平均值小于5%。只有在avrora基准测试中,由于异常多的futex系统调用,bc -java的开销很大(37%)。最后,我们讨论了解决特定性能用例场景的推荐方法。
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Profiling and Tracing Support for Java Applications
We demonstrate the feasibility of undertaking performance evaluations for JVMs using: (1) a hybrid JVM/OS tool, such as async-profiler, (2) OS centric profiling and tracing tools based on Linux perf, and (3) the Extended Berkeley Packet Filter Tracing (eBPF) framework where we demonstrate the rationale behind the standard offwaketime tool, for analysing the causes of blocking latencies, and our own eBPF-based tool bcc-java, that relates changes in microarchitecture performance counter values to the execution of individual JVM and application threads at low overhead. The relative execution time overheads of the performance tools are illustrated for the DaCapo-bach-9.12 benchmarks with OpenJDK9 on an Intel Xeon E5-2690, running Ubuntu 16.04. Whereas sampling based tools can have up to 25% slowdown using 4kHz frequency, our tool bcc-java has a geometric mean of less than 5%. Only for the avrora benchmark, bcc-java has a significant overhead (37%) due to an unusually high number of futex system calls. Finally, we provide a discussion on the recommended approaches to solve specific performance use-case scenarios.
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