Towards the adoption of Local Branch Predictors in Modern Out-of-Order Superscalar Processors
Niranjan Kumar Soundararajan, Saurabh Gupta, Ragavendra Natarajan, Jared Stark, Rahul Pal, Franck Sala, Lihu Rappoport, Adi Yoaz, Sreenivas Subramoney · 2019
Branch prediction accuracy plays a dominant role in the performance provided by modern Out-of-Order(OOO) superscalar processors. While global history-based branch predictors are more popular, local history-based predictors offer an additional dimension towards enhancing the overall branch prediction accuracy. Integrating the local predictors in modern cores, though, comes with non-trivial challenges associated with managing the local predictor's state and repairing this state on any branch misprediction is essential for the local predictor to operate effectively. Using a highly accurate, industry standard simulator modeling a Skylake-like OOO core and workloads spanning diverse categories including Server, High Performance Computing (HPC) and personal computing suites, besides SPEC, we methodically highlight the issues that need to be tackled, why local predictor repair is non-trivial and the performance opportunity that is lost when the local predictor repair is not handled efficiently. We discuss the issues with prior techniques and quantify their limitations when using them in current OOO cores. Further, we propose three practical, implementable and efficient repair techniques with minimal storage requirements that provide significant performance gains for local predictors. Unlike prior repair techniques that can only attain 50% of the oracular gains, our realistic repair techniques retain about 80% of the oracular gains resulting in significantly better application performance.