Understanding and Improving Operating System Effects in Control Flow Prediction

Understanding and Improving Operating System Effects in Control Flow Prediction Abstract Many modern applications exercise the operating system kernel significantly, resulting in several implications including affecting the control flow transfer in the execution environment. This paper focuses on understanding the operating system effects on control flow transfer and prediction, and designing architectural support to alleviate the bottlenecks. We characterize the control flow transfer of several emerging applications on a commercial operating system. We find that the exception-driven, intermittent invocation of OS code and the user/OS branch history interference increase the misprediction in both user and kernel code. We propose two simple OS-aware control flow prediction philosophies to alleviate the destructive impact of user/OS branch interference. The first one consists of capturing separate branch correlation information for user and kernel code. The second philosophy involves using separate branch prediction tables for user and kernel code. We study the improvement contributed by the OS-aware philosophies to various branch prediction strategies ranging from simple Gshare to more elegant Agree, Multi-Hybrid and Bi-Mode predictors. On 32K entry predictors, incorporating OS-aware strategies yields up to 34%, 23%, 27% and 9% prediction accuracy improvement in Gshare, Multi-Hybrid, Agree and Bi-Mode predictors, resulting in up to 8% execution speedup.