BeBoP: A Cost Effective Predictor Infrastructure for Superscalar Value Prediction

Arthur Perais 1 André Seznec 1
1 ALF - Amdahl's Law is Forever
Inria Rennes – Bretagne Atlantique , IRISA-D3 - ARCHITECTURE
Abstract : Up to recently, it was considered that a performance-effective implementation of Value Prediction (VP) would add tremendous complexity and power consumption in the pipeline, especially in the Out-of-Order engine and the predictor infrastructure. Despite recent progress in the field of Value Prediction, this remains partially true. Indeed, if the recent EOLE architecture proposition suggests that the OoO engine need not be altered to accommodate VP, complexity in the predictor infrastructure itself is still problematic. First, multiple predictions must be generated each cycle, but multi-ported structures should be avoided. Second, the predictor should be small enough to be considered for implementation, yet coverage must remain high enough to increase performance. To address these remaining concerns, we first propose a block-based value prediction scheme mimicking current instruction fetch mechanisms, BeBoP. It associates the predicted values with a fetch block rather than distinct instructions. Second, to remedy the storage issue, we present the Differential VTAGE predictor. This new tightly coupled hybrid predictor covers instructions predictable by both VTAGE and Stride-based value predictors, and its hardware cost and complexity can be made similar to those of a modern branch predictor. Third, we show that block-based value prediction allows to implement the checkpointing mechanism needed to provide D-VTAGE with last computed/predicted values at moderate cost. Overall, we establish that EOLE with a 32.8KB block-based D-VTAGE predictor and a 4-issue OoO engine can significantly outperform a baseline 6-issue superscalar processor, by up to 62.2% and 11.2% on average (gmean), on our benchmark set.
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Communication dans un congrès
International Symposium on High Performance Computer Architecture, Feb 2015, San Francisco, United States. 21, pp.13 - 25 ), 〈http://darksilicon.org/hpca/〉. 〈10.1109/HPCA.2015.7056018〉
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Arthur Perais, André Seznec. BeBoP: A Cost Effective Predictor Infrastructure for Superscalar Value Prediction. International Symposium on High Performance Computer Architecture, Feb 2015, San Francisco, United States. 21, pp.13 - 25 ), 〈http://darksilicon.org/hpca/〉. 〈10.1109/HPCA.2015.7056018〉. 〈hal-01193175〉

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