Adaptive Correction of Sampling Bias in Dynamic Call Graphs

Abstract

This article introduces a practical low-overhead adaptive technique of correcting sampling bias in profiling dynamic call graphs. Timer-based sampling keeps the overhead low but sampling bias lowers the accuracy when either observable call events or sampling actions are not equally spaced in time. To mitigate sampling bias, our adaptive correction technique weights each sample by monitoring time-varying spacing of call events and sampling actions. We implemented and evaluated our adaptive correction technique in Jikes RVM, a high-performance virtual machine. In our empirical evaluation, our technique significantly improved the sampling accuracy without measurable overhead and resulted in effective feedback directed inlining.