Automatic source code analysis of branch mispredictions

Document Type

Conference Proceeding

Date of Original Version



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