Value predictors for reuse through speculation on traces

Abstract

Reusing dynamic sequences of instructions - i.e., traces - improves performance for many benchmarks. However, many traces are not reused because of unavailable inputs in the reuse test. Reuse through speculation on traces (RST) aims to increase the number of reused traces by predicting those inputs when necessary, with minimal additional hardware when compared to nonspeculative trace reuse. In this paper, we compare last n-value and stride-aware prediction for trace inputs. Last n-value prediction uses the last recorded values as predictions, while stride-aware prediction identifies and uses strides to compute new predictions. Stride-aware RST has a higher hardware cost than last n-value RST and has also the shortcoming of not allowing branches inside predicted traces. This paper aims to determine which scheme is the most beneficial for RST. We show that stride values are important for reuse in RST and that last n-value prediction works as well as the more sophisticated stride-aware approach with simpler hardware.