Looking for limits in branch prediction with the GTL predictor André Seznec IRISA/INRIA/HIPEAC

Motivations Geometric history length predictors introduced in 2004-2006 OGEHL, CBP-1, dec. 2004 TAGE, JILP ’06, feb. 2006 Storage effective Exploits very long global histories Were defined with possible implementation in mind What are the limits of accuracy that can be captured with these schemes ? How do they compare with unconstrained prediction schemes ?

Geometric history length predictors: global history +multiple lengths ? L(0) T3 L(1) L(2) T4 L(3) L(4)

GEometric History Length predictor The set of history lengths forms a geometric series Capture correlation on very long histories {0, 2, 4, 8, 16, 32, 64, 128} most of the storage for short history !! What is important: L(i)-L(i-1) is drastically increasing

Combining multiple predictions Neural inspired predictors Use a (multiply)-add tree Partial matching Use tagged tables and the longest matching history O-GEHL, CBP-1 TAGE, JILP’ 06

Final computation through a sum 256Kbits: 12 components 3.670 misp/KI CBP-1 (2004): O-GEHL Final computation through a sum ∑ L(4) L(3) L(2) L(1) TO T1 T2 T3 T4 L(0) Prediction=Sign 256Kbits: 12 components 3.670 misp/KI

JILP ‘06: TAGE longest matching history =? =? =? 1 1 1 1 1 1 1 1 1 256Kbits: 3.358 misp/KI

What is global history conditional branch history: path confusion on short histories  path history: Direct hashing leads to path confusion  Represent all branches in branch history Use path AND direction history

Using a kernel history and a user history Traces mix user and kernel activities: Kernel activity after exception Global history pollution Solution: use two separate global histories User history is updated only in user mode Kernel history is updated in both modes

Accuracy limits for TAGE Varying the predictor size, the number of components, the tag width, the history length. Allowing multiple allocations The best accuracy on distributed traces: 3.054 misp/KI History length around 1,000 15-20 components No need for tags wider than 16 bits

Accuracy limits for GEHL Varying the predictor size, the number of components, the history length, counter width (slightly) improving the update policy and fitting in the two hours simulation rule  on the distributed traces: 2.842 misp/KI 97 components 8 bits counter 2,000 bits global history

GEHL vs TAGE Realistic implementation parameters (storage budget, number of components) TAGE is more accurate than (O-)GEHL Unlimited budget, huge number of components GEHL is more accurate than TAGE

Will it be sufficient to win The Championship ? GEHL history length: 2,000 97 components 2.842 misp/KI

A step further: hybrid GEHL-TAGE On a few benchmarks, TAGE is more accurate than GEHL, Let us try an hybrid GEHL-TAGE predictor

Branch/path history + PC Hybrid GEHL-TAGE Branch/path history + PC GEHL TAGE Meta = egskew mux Inherit from: Agree/bimode, YAGS, 2bcgskew,

GEHL+TAGE GEHL provides the main prediction: also used as the base predictor for TAGE (YAGS inspired) TAGE records when GEHL fails: {prediction, address, history} (agree/bimode, YAGS inspired) Meta selects between GEHL and TAGE (2bcgskew inspired)

Let us have fun !! GEHL history length: 400 TAGE history length: 100,000 2.774 misp/KI

Might still be unsufficient  GEHL history length: 400 TAGE history length: 100,000 2.774 misp/KI

Adding a loop predictor The loop predictor captures the number of iterations of a loop When successively encounters 8 times the same number of iterations, the loop predictor provides the prediction. Advantage: Very reliable

Branch/path history + PC GTL predictor Branch/path history + PC GEHL TAGE Meta = egskew mux Loop predictor confidence + static prediction on first occurrence

Hope this will be sufficient to win the Championship !! GTL GEHL, 97 comp., 400 hist. + TAGE, 19 comp., 100,000 hist + loop predictor 2.717 misp/KI

Geometric History Length predictors and limits on branch prediction Unlimited budget, huge number of components GEHL is more accurate than TAGE Very old correlation can be captured: On two benchmarks, using 10,000 history is really helping Does not seem to be a lot of potential extra benefit from local history We did not find any interesting extra scheme apart loop prediction Loop prediction, very marginal apart gzip

The End 