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1 TAGE-SC-L Branch Predictors André Seznec INRIA/IRISA.

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Presentation on theme: "1 TAGE-SC-L Branch Predictors André Seznec INRIA/IRISA."— Presentation transcript:

1 1 TAGE-SC-L Branch Predictors André Seznec INRIA/IRISA

2 2 The TAGE-SC-L branch predictor Sorry, nothing really new.. TAGE, JILP 2006  Considered as state-of-the-art global history predictor Can be augmented with small adjunct predictors Loop predictor: CBP-2 (2006) Statistical Corrector + Loop Predictor, Global history CBP-3 (2011) Local history Micro 2011

3 3 Optimized all parameters Number, size, width of the tables Types of the histories for the statistical components All that for decreasing the misprediction number by 3% !!

4 4 (Main) TAGE Predictor Stat. Cor. Prediction + Confidence Loop Predictor Global, local, skeleton histories

5 5 TAGE: multiple tables, global history predictor The set of history lengths forms a geometric series most of the storage for short history !! {0, 2, 4, 8, 16, 32, 64, 128} Capture correlation on very long histories

6 6 TAGE: Tagged and prediction by the longest history matching entry pc h[0:L1] ctru tag =? ctru tag =? ctru tag =? prediction pc h[0:L2]pch[0:L3] Tagless base predictor

7 7

8 8 Prediction computation General case:  Longest matching component provides the prediction Special case:  Many mispredictions on newly allocated entries: weak Ctr On many applications, Altpred more accurate than Pred  Property dynamically monitored through 4-bit counters

9 9 A tagged table entry Ctr: 3-bit prediction counter U: 2-bit counters  Was the entry recently useful ? Tag: partial tag Tag Ctr U

10 10 Allocate entries on mispredictions Allocate entries in longer history length tables  On tables with U unset Set Ctr to Weak and U to 0 Limited storage budget:  Allocate 2 entries for 256Kbits  Allocate 1 or 2 for 32Kbits UNLIMITED STORAGE BUDGET:  multiple entries allocated in different tables

11 11 Managing the (U)seful counter Increment when avoids a misprediction  (Pred = taken) & (Alt ≠ taken) 256K: Global decrement if « difficult » to allocate 32K: Probabilistic decrement when conflict Unlimited: don’t care

12 12 Adjunct predictors TAGE tracks strong correlation with the global branch history Small adjunct predictors to capture some missed correlation:  Loop predictor  Statistical Corrector

13 13 The loop predictor Predict loop with constant number of iterations:  16/32 entries  less than 5 bytes per entry  Capture loops with long bodies and/or irregular internal branches S: 1.2 %  M: 1 %  U:0.4%  Good tradeoff for the Championship Implementation: Not that great

14 14 The Statistical Corrector predictor Branches with poor correlation with global history:  Sometimes better predicted by a single wide PC indexed counter than by TAGE More generally, track cases such that:  « In this case (PC, history, prediction), TAGE is likely (>50 %) to mispredict »

15 15 Small predictor: very limited budget for the SC predictor Just track the statistically PC biased branches  « TAGE predicts this direction on this branch, but in most cases this was wrong » The corrector filter: A small partially tagged associative table 1.5 % misp. reduction: Much simpler than a loop predictor

16 16 Medium predictor « Statistically » correlated branches: Not strongly correlated with the global history, but exhibit a bias better predicted by averaging than tags neural  tags « Statistically » correlated branches: Not strongly correlated with the global history, but exhibit a bias better predicted by averaging than tags neural  tags Branches correlated with local history, but irregular global history pattern (on other branches) TAGE does not learn the pattern Branches correlated with local history, but irregular global history pattern (on other branches) TAGE does not learn the pattern

17 17 MultiGehl Statistical Correlator Predictor TAGE H PC S tat. Corr. Prediction + ctr value ++ H + LH PC Pred Gehl-like Local hist.

18 18 Why does it work The bias table indexed with PC+TAGE output:  Correct (most of the time)  High counter value  Dominates, not many updates  Wrong  Other counters can be trained  Correlation (if it exists) can be captured

19 19 MultiGehl Statistical Correlator Predictor for the Championship TAGE H PC S tat. Corr. Prediction + ctr value Local hist. + RAS associated history + 2 different local histories + simple choser 6.8 % misp reduction

20 20 « Realistic » 256 Kbits TAGE-SC-L « Only » 12 equal size TAGE tables + (local hist., global hist.) 4-tables SC + loop predictor No history tuning Only 2.8 % extra mispredictions

21 21 SC for Unlimited predictor GEHL based SC predictor:  Use any form of history information  Very long global  Mutiple local  « Skeleton » global history  ignore some branches  Recycle old ideas from the MAC-RHSP predictor (2004)

22 22 SC for unlimited predictor 460 predictor tables + 10 choser tables  Globally about 20 % less misp. than TAGE alone If one removes only :  The bias: 1.6 % for a single table  All global history components: 3.7 %  All local history components: 3.9 %  The choser: 3.2 %

23 23 Conclusion TAGE-SC-L fits (nearly) all storage sizes  32Kbits ≈ 64Kbits CBP1 champion on CBP1 traces  256Kbits ≈ 512Kbits CBP3 champion on CBP4 traces Unlimited predictor:  poTAGE-SC does better


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