CVP: An EnergyEfficient Indirect Branch Prediction with Compiler-Guided Value Pattern

Indirect branch prediction is becoming increasingly impor-tant in modern high-performance processors. However, pre-vious indirect branch predictors either require a significant amount of hardware storage and complexity, or heavily rely on the expensive manual profiling. In this paper, we propose the Compiler-Guided Value Pat-tern (CVP) prediction, an energy-efficient and accurate in-direct branch prediction via compiler-microarchitecture co-operation. The key of CVP prediction is to use the compiler-guided value pattern as the correlated information to hint the dynamic predictor. The value pattern reflects the pat-tern regularity of the value correlation, and thus significant-ly improves the prediction accuracy even in the case of deep pipeline stage or long memory latency. CVP prediction relies on the compiler to automatically identify the primary value correlation based on three high-level program substructures: virtual function calls, switch-case statements and function pointer calls. The compiler-identified information is then fed back to the dynamic predictor and is further used to hint the indirect branch prediction at runtime. We show that CVP prediction can be implemented in modern processors with little extra hardware support. Evaluations show that CVP prediction can significantly improve the prediction accuracy by 46 % over the traditional BTB-based prediction, leading to the performance improve-ment of 20%. Compared with the state-of-the-art aggressive ITTAGE and VBBI predictors, CVP prediction can improve the performance by 5.5 % and 4.2 % respectively