Collecting and exploiting high-accuracy call graph profiles in virtual machines

Abstract

Due to the high dynamic frequency of virtual method calls in typical object-oriented programs, feedback-directed devirtualization and Mining is one of the most important optimizations performed by high-performance virtual machines. A critical input to effective feedback-directed Mining is an accurate dynamic call graph. In a virtual machine, the dynamic call graph is computed online during program execution. Therefore, to maximize overall system performance, the profiling mechanism must strike a balance between profile accuracy, the speed at which the profile becomes available to the optimizer, and profiling overhead. This paper introduces a new low-overhead sampling-based technique that rapidly converges on a high-accuracy dynamic call graph. We have implemented the technique in two high-performance virtual machines: Jikes RVM and J9. We empirically assess our profiling technique by reporting on the accuracy of the dynamic call graphs it computes and by demonstrating that increasing the accuracy of the dynamic call graph results in more effective feedback-directed inlining.