The Case for Domain-Specialized Branch Predictors for Graph-Processing

Branch prediction is believed by many to be a solved problem, with state-of-the-art predictors achieving near-perfect prediction for many programs. In this article, we conduct a detailed simulation of graph-processing workloads in the GAPBS benchmark suite and show that branch mispredictions occur f...

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Bibliographic Details
Published inIEEE computer architecture letters Vol. 19; no. 2; pp. 101 - 104
Main Authors Samara, Ahmed, Tuck, James
Format Journal Article
LanguageEnglish
Published New York IEEE 01.07.2020
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Benchmark testing
Benchmarks
branch prediction
Data structures
Graph-processing
Hardware
Performance degradation
Predictive models
Prefetching
Roads
Social networking (online)
Workload
Workloads
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Summary:Branch prediction is believed by many to be a solved problem, with state-of-the-art predictors achieving near-perfect prediction for many programs. In this article, we conduct a detailed simulation of graph-processing workloads in the GAPBS benchmark suite and show that branch mispredictions occur frequently and are still a large limitation on performance in key graph-processing applications. We provide a detailed analysis of which branches are mispredicting and show that a few key branches are the main source of performance degradation across the graph-processing benchmarks we looked at. We also propose a few ideas for future work to improve branch prediction accuracy on graph workloads.
ISSN:1556-6056
1556-6064
DOI:10.1109/LCA.2020.3005895