Improving virtual machine performance using a cross-run profile repository
Improving virtual machine performance using a cross-run profile repository
复制标题
DOI：
10.1145/1094811.1094835
复制
发表时间：
2005-10
期刊：
影响因子：
--
通讯作者：
Matthew Arnold;Adam Welc;V. T. Rajan
中科院分区：
文献类型：
--
作者：
Matthew Arnold;Adam Welc;V. T. Rajan
Java编程语言等语言的虚拟机可以广泛使用在线分析和动态优化，以提高程序性能。但是，尽管分析在实现高性能方面发挥了重要作用，但当前的虚拟机在执行结束时丢弃了程序的个人资料数据，从而浪费了机会利用过去的知识来提高未来的绩效。在本文中，我们提出了一个完全自动化的体系结构，用于利用虚拟机中的跨运行配置文件数据。我们的工作解决了许多以前限制这种方法实用性的挑战。我们应用此体系结构来解决选择性优化的问题，并描述我们在IBM的J9 J9 Java虚拟机中的实现。我们的结果表明，在广泛的Java计划中的性能得到了重大改进，根据执行方案，平均绩效范围为8.8％-16.6％。
Virtual machines for languages such as the Java programming language make extensive use of online profiling and dynamic optimization to improve program performance. But despite the important role that profiling plays in achieving high performance, current virtual machines discard a program's profile data at the end of execution, wasting the opportunity to use past knowledge to improve future performance. In this paper, we present a fully automated architecture for exploiting cross-run profile data in virtual machines. Our work addresses a number of challenges that previously limited the practicality of such an approach.We apply this architecture to address the problem of selective optimization, and describe our implementation in IBM's J9 Java virtual machine. Our results demonstrate substantial performance improvements on a broad suite of Java programs, with the average performance ranging from 8.8% -- 16.6% depending on the execution scenario.