Predicting conditional branch directions from previous runs of a program
ASPLOS V Proceedings of the fifth international conference on Architectural support for programming languages and operating systems
A comparison of dynamic branch predictors that use two levels of branch history
ISCA '93 Proceedings of the 20th annual international symposium on computer architecture
Branch classification: a new mechanism for improving branch predictor performance
MICRO 27 Proceedings of the 27th annual international symposium on Microarchitecture
A comparative analysis of schemes for correlated branch prediction
ISCA '95 Proceedings of the 22nd annual international symposium on Computer architecture
Trading conflict and capacity aliasing in conditional branch predictors
Proceedings of the 24th annual international symposium on Computer architecture
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Analyzing the working set characteristics of branch execution
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
Neural methods for dynamic branch prediction
ACM Transactions on Computer Systems (TOCS)
X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
PACT '00 Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques
The Alpha 21264 Microprocessor Architecture
ICCD '98 Proceedings of the International Conference on Computer Design
Code placement for improving dynamic branch prediction accuracy
Proceedings of the 2005 ACM SIGPLAN conference on Programming language design and implementation
Discovering and Exploiting Program Phases
IEEE Micro
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As a result of resource limitations, state in branch predictors is frequently shared between uncorrelated branches. This interference can significantly limit prediction accuracy. In current predictor designs, the branches sharing prediction information are determined by their branch addresses and thus branch groups are arbitrarily chosen during compilation. This feasibility study explores a more analytic and systematic approach to classify branches into clusters with similar behavioral characteristics. We present several ways to incorporate this cluster information as an additional information source in branch predictors. Our profile-based results demonstrate that cluster information is useful in various branch prediction schemes. When clustered indexing is applied, the same performance can be obtained with 2-8 times less hardware budget. For small predictor budgets, clustered indexing is very cost-effective, e.g., the misprediction rate in an 8 Kib gshare is reduced 12.3% on average for SPEC CPU2000 INT. For large budgets up to 4 Mib, clustered indexing reduces the number of mispredictions by 3-5%, or stated otherwise only half the hardware budget is required to obtain the same performance as the original gshare scheme.