Modern JIT compilers often employ multi-level recompilation strategies as a means of ensuring the most used code is also the most highly optimized, balancing optimization costs and expected future performance. Accurate selection of code to compile and level of optimization to apply is thus important to performance. In this paper we investigate the effect of an improved recompilation strategy for a Java virtual machine. Our design makes use of a lightweight, low-level profiling mechanism to detect high-level, variable length phases in program execution. Phases are then used to guide adaptive recompilation choices, improving performance. We develop both an offline implementation based on trace data and a self-contained online version. Our offline study shows an average speedup of 8.7% and up to 21%, and our online system achieves an average speedup of 4.4%, up to 18%. We subject our results to extensive analysis and show that our design achieves good overall performance with high consistency despite the existence of many complex and interacting factors in such an environment.

Many new Java runtime optimizations report relatively small, single-digit performance improvements. On modern virtual and actual hardware, however, the performance impact of an optimization can be influenced by a variety of factors in the underlying systems. Using a case study of a new garbage collection optimization in two different Java virtual machines, we show the relative effects of issues that must be taken into consideration when claiming an improvement. We examine the specific and overall performance changes due to our optimization and show how unintended side-effects can contribute to, and distort the final assessment. Our experience shows that VM and hardware concerns can generate variances of up to 9.5% in whole program execution time. Consideration of these confounding effects is critical to a good, objective understanding of Java performance and optimization.