CAOS: combined analysis with online sifting for dynamic compilation systems
CAOS: combined analysis with online sifting for dynamic compilation systems
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CAOS：动态编译系统的在线筛选与分析相结合
DOI：
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发表时间：
2016
期刊：
影响因子：
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通讯作者：
Jian Wang
中科院分区：
文献类型：
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作者：
Jie Fu;Guojie Jin;Longbing Zhang;Jian Wang
动态汇编对虚拟机的性能有很大的影响。在本文中，我们研究动态汇编的特征，然后揭示用于优化动态汇编系统的目标。遵循这些目标，我们提出了一种新型的动态汇编计划算法，称为合并分析与在线筛分（CAO）。它由合并的优先分析模型和在线筛分机制组成。合并优先级分析模型用于确定调度时方法的优先级，旨在将响应能力与平均汇编队列延迟进行核对。通过执行在线筛选，可以进一步减少运行时开销，因为筛选出对性能的好处的方法很少。 CAO可以显着改善应用程序的启动性能。实验结果表明，CAO的平均启动性能提高了14.0％，最高的性能提升高达55.1％。有了高多功能性和易于实现的优点，CAO可以应用于大多数动态汇编系统。
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.