Title

Automatic source code analysis of branch mispredictions

Document Type

Conference Proceeding

Date of Original Version

1-1-2014

Abstract

After over two decades of extensive research on branch prediction, branch mispredictions are still an important performance/power bottleneck for today's aggressive processors. In our prior work, to further understand the causes for mispredictions, we presented a source-code based classification of branch mispredictions extending the prior work on predictor-specific classification. Since source-code analysis by hand is very time-consuming and not possible in some cases, in this paper, we develop methods in order to automatically identify the data structures for each branch instruction, which allows detailed source-code analysis at run-time. We show that our run-time method can successfully provide source-code analysis and classify more than 99% of the branch mispredictions.

Publication Title

IISWC 2014 - IEEE International Symposium on Workload Characterization

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