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, e.g., Journal
IISWC 2014 - IEEE International Symposium on Workload Characterization
Citation/Publisher Attribution
Ozturk, Celal, Ibrahim B. Karsli, and Resit Sendag. "Automatic source code analysis of branch mispredictions." IISWC 2014 - IEEE International Symposium on Workload Characterization (2014): 82-83. doi: 10.1109/IISWC.2014.6983045.