Optimizing Branch Predictor for Graph Applications

TL;DR

This paper explores optimizing branch prediction techniques to improve performance in graph applications, which frequently suffer from branch mispredictions due to their large size and complex control flow.

Contribution

It proposes targeted improvements to branch predictors specifically for graph applications to reduce mispredictions and enhance overall execution efficiency.

Findings

Optimized branch predictor reduces misprediction rate in graph workloads.

Performance gains observed in execution time due to predictor improvements.

Highlights the importance of branch prediction in large-scale graph processing.

Abstract

Real-world graph applications are generally larger than the size of the cache itself. Due to this reason, the memory hierarchy was identified as a key bottleneck by the earlier works. Undoubtedly, the performance can be achieved by improving cache, there is still a scope for performance gain by improving branch prediction accuracy. In graph processing applications, the occurrence of branch mispredictions is very frequent and is a major limitation for the overall performance. Within a program, there are different kinds of branches that recur throughout its execution. Although lots of branch predictors (BP) have been developed earlier to capture the static and dynamic behavior of branches. Branch predictors can yet be further optimized to handle the branches that cause mispredictions.