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T OR A AMODT Andreas Moshovos Paul Chow Electrical and Computer Engineering University of Toronto Canada The Predictability of.

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Presentation on theme: "T OR A AMODT Andreas Moshovos Paul Chow Electrical and Computer Engineering University of Toronto Canada The Predictability of."— Presentation transcript:

1 T OR A AMODT Andreas Moshovos Paul Chow Electrical and Computer Engineering University of Toronto Canada The Predictability of Computations that Produce Unpredictable Outcomes

2 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Outcome-Based Prediction History of Outcomes leading up to Branch “X”: TNTTNTT...NTN... TNTTNTT Why this works: Locality in the outcome stream Next time we encounter X after “TNTTNT” we can predict “T” History Outcome of Branch X

3 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Problem Unpredictable Branches THE Problem. No Outcome-Locality

4 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Operation-Based Prediction Find locality in the computations that produce the outcome bne slt ld add

5 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes This Work First work that looks at the fundamental program behaviour that would facilitate operation-based prediction. Related work… –Characterization of slices –Prefetching loads / pre-execution of branches

6 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Ideally... Slice (i.e., slice trace) will always be the same. Slice will contain very few operations spanning large portion of original program. Easy (fast) to pre-compute.

7 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Terminology Lead : earliest instruction in slice Target : branch we want to precompute bne slt ld add

8 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes What Should a Slice be? Commited Instructions  32, 64, 128, or 256 window Ignore Control Flow  retain side-effect of JAL on $r31 Memory Dependence  follow resolved load-store dependence: M Restrict # Instructions  R = max 1/4, U = “no restriction” FETCH... COMMIT older

9 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Methodology 12 programs from SPEC2000 Baseline Outcome Prediction Hardware –64K Gshare + 64K bimodal w/ 64K selector –64 entry RAS sim-outorder (SimpleScalar 3.0): –8-way, 128 entry RUU, 64 entry-fetch buffer –64K dual LI, 256K unified L2 –64 entry LSQ –Perfect Memory Disambiguation

10 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Measuring Slice Locality locality(1) = Probability same slice was seen last time. High value of locality(1) indicates that last-operation based slice prediction would work well. locality(N) = Probability same slice seen in last N unique slices.

11 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Measuring Slice Locality Save the FOUR unique, most recent slice traces per static branch (only on misprediction). Each time a mispredicted branch is encountered check whether the slice trace was the most recent, 2nd most recent, etc...

12 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Measuring Slice Locality All results are weighted averages. Result for each static branch weighted proportionally to the number of times the operation-based predictor mispredicted it. Characteristics of branches that cause most mispredictions emphasized.

13 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Unrestricted Slices : 32UM gcc equake ammp bzip Saving ONE slice captures most of locality. Locality Better

14 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Restricted vs. Unrestricted 32RM 32UM gcc equake ammp bzip Most slices have few instructions. Locality Better

15 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Effect of Memory Dependence 64RM 64R gcc equake ammp bzip Tracking Dependence Does Not Affect Locality Much. Locality Better

16 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Window Size gcc equake ammp bzip Locality Better 256RM 128RM 64RM 32RM Locality good even for large windows.

17 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Effect of Selection Context 128RM On Mispredict Always Locality Better gcc equake ammp bzip Focusing on Mispredictions Improves Locality.

18 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Idealized Predictor Lead PC Spawn and execute instantaneously when lead operation is encountered. Store up to 4 slice traces per lead operation

19 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Idealized Predictor Match operations & register dependencies as instructions are fetched. After matching there is usually only one prediction per target, if any (>80% of time)... –Tie-breaker #1: longest lead-target distance. –Tie-breaker #2: most recently detected slice.

20 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Correcting Mispredictions High Coverage of Mispredicted Branches 128RM 64RM 32RM gcc equake ammp bzip

21 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Interaction with Outcome-Based Predictor gcc equake ammp bzip Very Little Destructive Interference 128RM 64RM 32RM

22 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Summary Slice-locality for mispredicted branches –average of 70% for restricted slices on a 64 entry window following load-store dependencies (12 SPEC2000 benchmarks). Accuracy of idealized predictor –74% of mispredicted branches eliminated

23 Aamodt, Moshovos, Chow University of Toronto The Predictability of Computations that Produce Unpredictable Outcomes Conclusion First work that looks at the fundamental program behaviour, slice-locality, that would facilitate predicting slice traces to pre-execute outcomes. SPEC2000 benchmarks show very high slice-locality for mispredicted branches.


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