Improving virtual machine performance using a cross-run profile repository

Abstract

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.