Pushing the branch predictability limits with the multi-poTAGE+SC - PowerPoint PPT Presentation

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Pushing the branch predictability limits with the multi-poTAGE+SC predictor Pierre Michaud + Andr Seznec june 2014 Competition track: Unlimited size 2 We did not modify the predictor algorithm after the submission We just corrected a bug

Pushing the branch predictability limits with the multi-poTAGE+SC predictor Pierre Michaud + André Seznec june 2014
Competition track: Unlimited size 2
We did not modify the predictor algorithm after the submission We just corrected a bug (out of bound array write) that had almost no impact on prediction accuracy 3
What we did 4
What we did André TAGE statistical corrector (SC) 5
What we did André Pierre multi-poTAGE TAGE (MP) statistical corrector (SC) 6
What we did André Pierre multi-poTAGE TAGE (MP) statistical corrector (SC) Approximately same prediction accuracy on average, but significant differences on individual traces 7
What we did multi-poTAGE TAGE (MP) statistical corrector (SC) 8
What we did TAGE multi-poTAGE (MP) statistical corrector (SC) 9
What we did multi-poTAGE (MP) statistical corrector (SC) 10
What we did multi-poTAGE (MP) statistical corrector (SC) 11
What we did multi-poTAGE (MP) statistical corrector (SC) change a few parameters because of the memory size constraint 12
Multi-poTAGE + Statistical Corrector MP SC 13
Multi-poTAGE + Statistical Corrector MP -5% MPKI SC 14
Conclusion • Performance gain of MP+SC over TAGE-SC comes mainly from the non-global components of multi-poTAGE • With the Statistical Corrector, the post-predictor in poTAGE is almost superfluous - on isolated poTAGE, removing the post-predictor  +10% MPKI - with SC, removing post-predictor  +1% MPKI • The Statistical Corrector solves the cold-counter problem more effectively than the post-predictor 15
Questions ? 16

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