CAOS: combined analysis with online sifting for dynamic compilation systems

Proceedings of the ACM International Conference on Computing Frontiers Pub Date : 2016-05-16 DOI:10.1145/2903150.2903151

Jie Fu, Guojie Jin, Longbing Zhang, Jian Wang

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引用次数: 1

Abstract

Dynamic compilation has a great impact on the performance of virtual machines. In this paper, we study the features of dynamic compilation and then unveil objectives for optimizing dynamic compilation systems. Following these objectives, we propose a novel dynamic compilation scheduling algorithm called combined analysis with online sifting (CAOS). It consists of a combined priority analysis model and an online sifting mechanism. The combined priority analysis model is used to determine the priority of methods while scheduling, aiming at reconciling responsiveness with the average delay of compilation queue. By performing online sifting, runtime overhead can be further reduced since methods with little benefit to performance are sifted out. CAOS can significantly improve the startup performance of applications. Experimental results show that CAOS achieves 14.0% improvement of startup performance on average, and the highest performance boost is up to 55.1%. With the virtue of high versatility and easy implementation, CAOS can be applied to most dynamic compilation systems.

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CAOS:动态编译系统的综合分析与在线筛选

动态编译对虚拟机的性能影响很大。本文研究了动态编译的特点，揭示了动态编译系统优化的目标。根据这些目标，我们提出了一种新的动态编译调度算法，称为联机筛选结合分析(CAOS)。它由组合优先级分析模型和在线筛选机制组成。在调度过程中，采用组合优先级分析模型确定方法的优先级，以协调响应性与编译队列的平均延迟。通过执行在线筛选，可以进一步减少运行时开销，因为对性能没有什么好处的方法被筛选掉了。CAOS可以显著提高应用程序的启动性能。实验结果表明，CAOS的启动性能平均提升了14.0%，最高提升了55.1%。CAOS具有通用性强、易于实现的优点，可以应用于大多数动态编译系统。

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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Proceedings of the ACM International Conference on Computing Frontiers

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