CAOS: combined analysis with online sifting for dynamic compilation systems

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.