Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
Compiler synthesized dynamic branch prediction
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Path-based next trace prediction
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
An analysis of correlation and predictability: what makes two-level branch predictors work
Proceedings of the 25th annual international symposium on Computer architecture
Dynamic history-length fitting: a third level of adaptivity for branch prediction
Proceedings of the 25th annual international symposium on Computer architecture
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Using Dataflow Based Context for Accurate Value Prediction
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Using SimPoint for accurate and efficient simulation
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
A study of branch prediction strategies
ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
Dynamic Data Dependence Tracking and its Application to Branch Prediction
HPCA '03 Proceedings of the 9th International Symposium on High-Performance Computer Architecture
Proceedings of the 30th annual international symposium on Computer architecture
Dynamic Branch Prediction with Perceptrons
HPCA '01 Proceedings of the 7th International Symposium on High-Performance Computer Architecture
Analysis of the O-GEometric History Length Branch Predictor
Proceedings of the 32nd annual international symposium on Computer Architecture
Dynamic feature selection for hardware prediction
Journal of Systems Architecture: the EUROMICRO Journal
The significance of affectors and affectees correlations for branch prediction
HiPEAC'08 Proceedings of the 3rd international conference on High performance embedded architectures and compilers
Exploiting intra-function correlation with the global history stack
SAMOS'05 Proceedings of the 5th international conference on Embedded Computer Systems: architectures, Modeling, and Simulation
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This work investigates the potential of direction-correlations to improve branch prediction. There are two types of direction-correlation: affectors and affectees. This work considers for the first time their implications at a basic level. These correlations are determined based on dataflow graph information and are used to select the subset of global branch history bits used for prediction. If this subset is small then affectors and affectees can be useful to cut down learning time, and reduce aliasing in prediction tables. This paper extends previous work explaining why and how correlation-based predictors work by analyzing the properties of direction-correlations. It also shows that branch history selected based on direction-correlations improves the accuracy of the limit and realistic conditional branch predictors, that won at the recent branch prediction contest, by up to 30% and 17% respectively. The findings in this paper call for the investigation of predictors that can efficiently learn correlations that may be non-consecutive (i.e. with holes between them) from long branch history.