Workload Characterization for Branch Predictability

TL;DR

This paper introduces a workload characterization methodology for branch prediction, focusing on branch working set size and predictability, to better understand and improve predictor accuracy in modern processors.

Contribution

It proposes two new workload-driven metrics for branch prediction accuracy and analyzes their relationship with predictor performance across thousands of workload traces.

Findings

Branch working set size and predictability strongly correlate with misprediction rates.

Characterized 2,451 workload traces into distinct categories based on branch behavior.

Insights into workload categories that favor modern branch predictors.

Abstract

Conditional branch prediction predicts the likely direction of a conditional branch instruction to support ILP extraction. Branch prediction is a pattern recognition problem that learns mappings between a context to the branch outcome. An accurate predictor reduces the number of instructions executed on the wrong path resulting in an improvement of performance and energy consumption. In this paper, we present a workload characterization methodology for branch prediction. We propose two new workload-driven branch prediction accuracy identifiers -- branch working set size and branch predictability. These parameters are highly correlated with misprediction rates of modern branch prediction schemes (e.g. TAGE and perceptron). We define the branch working set of a trace as a group of most frequently occurring branch contexts, i.e. the 3-part tuple of branch address, and associated global and local history. We analyze the branch working set's size and predictability on a per-trace basis to study its relationship with a modern branch predictor's accuracy. We have characterized 2,451 workload traces into seven branch working set size and nine predictability categories after analyzing their branch behavior. We present further insights into the source of prediction accuracy and favored workload categories for modern branch predictors.