Alternative implementations of two-level adaptive branch prediction
ISCA '92 Proceedings of the 19th annual international symposium on Computer architecture
Exceeding the dataflow limit via value prediction
Proceedings of the 29th annual ACM/IEEE international symposium on Microarchitecture
Improving the accuracy and performance of memory communication through renaming
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Streamlining inter-operation memory communication via data dependence prediction
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Highly accurate data value prediction using hybrid predictors
MICRO 30 Proceedings of the 30th annual ACM/IEEE international symposium on Microarchitecture
Improving branch predictors by correlating on data values
Proceedings of the 32nd annual ACM/IEEE international symposium on Microarchitecture
Understanding the backward slices of performance degrading instructions
Proceedings of the 27th annual international symposium on Computer architecture
Using Dataflow Based Context for Accurate Value Prediction
Proceedings of the 2001 International Conference on Parallel Architectures and Compilation Techniques
Control-Flow Speculation through Value Prediction for Superscalar Processors
PACT '99 Proceedings of the 1999 International Conference on Parallel Architectures and Compilation Techniques
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Contexts formed only from the outcomes of the last several instances of a static branch instruction or that of the last several dynamic branches do not always encapsulate all of the information required for correct prediction of the branch. Complex interactions between data flow and control flow change the context in ways that result in predictability loss for a significant number of dynamic branches. For improving the prediction accuracy, we use contexts derived from the predictable portions of the data flow graph. That is, the predictability of hard-to-predict branches can be improved by taking advantage of the predictability of the easy-to-predict instructions that precede it in the data flow graph. We propose and investigate a run-time scheme for producing such an improved context from the predicted values of preceding instructions. We also propose a novel branch predictor that uses dynamic dataflow-inherited speculative context (DDISC) for prediction. Simulation results verify that the use of dataflow-based contexts yields significant reduction in branch mispredictions, ranging up to 40%. This translates to an overall branch prediction accuracy of 89% to 99.5%.