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Cost Effective Speculation with the Omnipredictor

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Abstract

Modern superscalar processors heavily rely on out-of-order and speculative execution to achieve high performance. The conditional branch predictor, the indirect branch predictor and the memory dependency predictor are among the key structures that enable efficient speculative out-of-order execution. Therefore, processors implement these three predictors as distinct hardware components. In this paper, we propose the omnipredictor that predicts conditional branches, memory dependencies and indirect branches at state-of-the-art accuracies without paying the hardware cost of the memory dependency predictor and the indirect jump predictor. We first show that the TAGE prediction scheme based on global branch history can be used to concurrently predict both branch directions and memory dependencies. Thus, we unify these two predictors within a regular TAGE conditional branch predictor whose prediction is interpreted according to the type of the instruction accessing the predictor. Memory dependency prediction is provided at almost no hardware overhead. We further show that the TAGE conditional predic-tor can be used to accurately predict indirect branches through using TAGE entries as pointers to Branch Target Buffer entries. Indirect target prediction can be blended into the conditional predictor along with memory dependency prediction, forming the omnipredictor.
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Dates and versions

hal-01888884 , version 1 (14-11-2018)

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Arthur Perais, André Seznec. Cost Effective Speculation with the Omnipredictor. PACT '18 - 27th International Conference on Parallel Architectures and Compilation Techniques, Nov 2018, Limassol, Cyprus. ⟨10.1145/3243176.3243208⟩. ⟨hal-01888884⟩
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