Rollback-Free Value Prediction with Approximate Loads
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Rollback-Free Value Prediction with Approximate Loads Bradley Thwaites, Gennady Pekhimenko, Amir Yazdanbakhsh, Jongse Park, Girish Mururu Hadi Esmaeilzadeh, Onur Mutlu, Todd C. Mowry RFVP Overview esign Principles Design Challenges y Experimental Results Ongoing Work Motivation Mitigate long latency memory accesses Microarchitecturally- triggered approximation Predict the value of an approximate load when it misses in the cache Do not check for mispredictions Do not rollback from mispredictions 1.0 2.0 3.0 4.0 5.0 6.0 Performance Benefit Perfect Prediction Maximize opportunities for performance and energy benefits Minimize the adverse effects of approximation on quality degradation (0%, 0%, 0%) (1.06x, 1.03x, 2% (1.13x, 1.06x, 6%) (1.27x, 1.10x, 11 Extend rollback-free value prediction to GPUs Drop a fraction of the missed requests Preliminary results: Up to 2x improvement in energy and performance with only 10% quality degradation Mitigate both Memory Wall and Bandwidth Wall 1.00 1.05 1.10 1.15 1.20 1.25 Performance Benefit 2MB LLC, 4-Wide, Performance Results Target Performance-Critical Safe Loads Profile-directed compilation Usually, < 32 loads cause 80% of cache misses Utilize Fast-Learning Predictors Two-delta stride predictor Prediction: table lookup plus an addition Integrate RFVP with existing architecture 0% 20% 40% 60% 80% 100% 17.7% 15.4% 2.8% 0.0% 18.3% 19.5% 2.1% 0.2% 1.6% 1.8% 0.0% 0.3% 1.8% 0.0% Value Prediction - Quality Loss Stride Quality Loss (Performance Gain, Energy Gain, Quality
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Rollback-Free Value Prediction with Approximate Loads. Bradley Thwaites , Gennady Pekhimenko , Amir Yazdanbakhsh , Jongse Park, Girish Mururu Hadi Esmaeilzadeh , Onur Mutlu , Todd C. Mowry. Motivation. RFVP Overview. Perfect Prediction. Microarchitecturally -triggered approximation - PowerPoint PPT Presentation
Transcript of Rollback-Free Value Prediction with Approximate Loads
Rollback-Free Value Prediction with Approximate LoadsBradley Thwaites, Gennady Pekhimenko, Amir Yazdanbakhsh, Jongse Park, Girish Mururu
Hadi Esmaeilzadeh, Onur Mutlu, Todd C. MowryRFVP Overview
Design Principles Design Challenges
Key Experimental Results Ongoing Work
Motivation
Mitigate long latency memory accesses Microarchitecturally-triggered approximationPredict the value of an approximate load when it misses in the cacheDo not check for mispredictionsDo not rollback from mispredictions
swim
fma3d
bwavesmcf
cactu
sADM
soplex
GemsFDTD
geomean1.0
2.0
3.0
4.0
5.0
6.0
Perf
orm
ance
Ben
e-fit
Perfect Prediction
Maximize opportunities for performance and energy benefits
Minimize the adverse effects of approximation on quality degradation
(0%, 0%, 0%) (1.06x, 1.03x, 2%)
(1.13x, 1.06x, 6%) (1.27x, 1.10x, 11%)
Extend rollback-free value prediction to GPUsDrop a fraction of the missed requestsPreliminary results: Up to 2x improvement in energy and performancewith only 10% quality degradation
Mitigate both Memory Wall and Bandwidth Wall
swim
fma3d
bwaves
mcf
cactu
sADM
soplex
GemsFDTD
geomean
1.00
1.05
1.10
1.15
1.20
1.25
Perf
orm
ance
Ben
efit
2MB LLC, 4-Wide, Performance Results
Target Performance-Critical Safe LoadsProfile-directed compilationUsually, < 32 loads cause 80% of cache misses
Utilize Fast-Learning PredictorsTwo-delta stride predictorPrediction: table lookup plus an addition
Integrate RFVP with existing architecture
swim fma3d bwaves mcf cactusADM soplex GemsFDTD0%
20%
40%
60%
80%
100%
17.7% 15.4%
2.8% 0.0%
18.3% 19.5%
2.1%0.2% 1.6% 1.8% 0.0% 0.3% 1.8% 0.0%
Value Prediction - Quality Loss
Stride
TwoDelta
Qua
lity
Loss
(Performance Gain, Energy Gain, Quality Loss)
