SoftGuide: A Hardware-Software Co-design Predictor for Data-Dependent Branches

Abstract

Branch predictors based on historical information exhibit exemplary performance. However, a subset of data-dependent branches still pose a significant challenge, often resulting in severe mispredictions. Such branches are commonly encountered during the processing of various data structures, and increasing the capacity of predictors has shown limited effective-ness. Thus, a dedicated branch predictor is necessary to enhance performance for varying data structures while maintaining lower hardware complexity. This paper proposes SoftGuide, a novel hardware-software cooperative branch predictor designed to tackle two main chal-lenges inherent in data-dependent branch prediction: (1) the detection and identification of branch dependencies(software-friendly) and (2) data prefetching and dependency chain pre-execution(hardware-friendly). SoftGuide leverages software to convey the memory access patterns and the dependency chains associated with the branch, thereby circumventing the overhead of hardware-based detection. Utilizing the information provided by the software, the enhanced hardware prefetches data and triggers pre-execution in advance. SoftGuide can perfectly unify prefetching and prediction tasks. For SPEC2006 and GAP benchmarks with the method of SimPoint, SoftGuide realizes a decrease in branch mispredictions per 1K instructions (MPKI) by 46.4% and an increase in Instructions Per Cycle (IPC) by 1.25x average on the processor equipped with the state-of-the-art branch predictor. Moreover, the storage overhead is just 3.88KB.