ISCA '86 Proceedings of the 13th annual international symposium on Computer architecture
Two-level adaptive training branch prediction
MICRO 24 Proceedings of the 24th annual international symposium on Microarchitecture
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
PLDI '93 Proceedings of the ACM SIGPLAN 1993 conference on Programming language design and implementation
Improving semi-static branch prediction by code replication
PLDI '94 Proceedings of the ACM SIGPLAN 1994 conference on Programming language design and implementation
Branch classification: a new mechanism for improving branch predictor performance
MICRO 27 Proceedings of the 27th annual international symposium on Microarchitecture
Improving the accuracy of static branch prediction using branch correlation
ASPLOS VI Proceedings of the sixth international conference on Architectural support for programming languages and operating systems
Reducing branch costs via branch alignment
ASPLOS VI Proceedings of the sixth international conference on Architectural support for programming languages and operating systems
Avoiding conditional branches by code replication
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Accurate static branch prediction by value range propagation
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Corpus-based static branch prediction
PLDI '95 Proceedings of the ACM SIGPLAN 1995 conference on Programming language design and implementation
Dynamic path-based branch correlation
Proceedings of the 28th annual international symposium on Microarchitecture
The agree predictor: a mechanism for reducing negative branch history interference
Proceedings of the 24th 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
Improving trace cache effectiveness with branch promotion and trace packing
Proceedings of the 25th annual international symposium on Computer architecture
The YAGS branch prediction scheme
MICRO 31 Proceedings of the 31st annual ACM/IEEE international symposium on Microarchitecture
A study of branch prediction strategies
ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
Static Methods in Hybrid Branch Prediction
PACT '98 Proceedings of the 1998 International Conference on Parallel Architectures and Compilation Techniques
The Effect of Code Reordering on Branch Prediction
PACT '00 Proceedings of the 2000 International Conference on Parallel Architectures and Compilation Techniques
Improving Branch Prediction Accuracy by Reducing Pattern History Table Interference
PACT '96 Proceedings of the 1996 Conference on Parallel Architectures and Compilation Techniques
Static methods in branch prediction
Static methods in branch prediction
Trace Scheduling: A Technique for Global Microcode Compaction
IEEE Transactions on Computers
Improving instruction delivery with a block-aware ISA
Euro-Par'05 Proceedings of the 11th international Euro-Par conference on Parallel Processing
| Hi-index | 0.00 |
Branch prediction accuracy is a very important factor for superscalar processor performance. It is the ability to predict the outcome of a branchwh ichallo ws the processor to effectively use a large instruction window, and extract a larger amount of ILP. The first approachto branchpre diction were static predictors, which always predicted the same direction for a given branch. The use of profile data and compiler transformations proved very effective at improving the accuracy of these predictors. In this paper we propose a novel dynamic predictor organization which makes extensive use of profile data. The main advantage of our proposed predictor (the agbias predictor) is that it does not depend heavily on the quality of the profile data to provide high prediction accuracy. Our results show that our agbias predictor reduces the branch misprediction rate by 14% on a 16KB predictor over the next best compilerenhanced predictor.