Motivation & Background Sampling and Modeling Design Optimization Conclusion Statistical Inference for Efficient Microarchitectural Analysis Benjamin C. Lee, David M. Brooks www.deas.harvard.edu/∼bclee Division of Engineering and Applied Sciences Harvard University Boston Area Architecture Workshop 26 January 2007 Benjamin C. Lee, David M. Brooks 1 :: BARC :: 26 Jan 07
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Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Statistical Inference for EfficientMicroarchitectural Analysis
Benjamin C. Lee, David M. Brooks
www.deas.harvard.edu/∼bcleeDivision of Engineering and Applied Sciences
Harvard University
Boston Area Architecture Workshop26 January 2007
Benjamin C. Lee, David M. Brooks 1 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Outline
Motivation & BackgroundSimulation ChallengesSimulation ParadigmRegression Theory
Sampling and ModelingExperimental MethodologyModel Evaluation
Benjamin C. Lee, David M. Brooks 3 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Simulation ChallengesSimulation ParadigmRegression Theory
Microarchitectural Design Space
Increasing diversity of interesting, viable designsExamples :: Power 4, Pentium 4, UltraSPARC T1Tractably quantify trends across comprehensive design space
Benjamin C. Lee, David M. Brooks 4 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Simulation ChallengesSimulation ParadigmRegression Theory
Microarchitectural Simulation Challenges
Cycle-Accurate SimulationAccurately identifies trends in design spaceTracks instructions’ progress through microprocessorEstimates performance, power, temperature, . . .
Simulation CostsLong simulation times (minutes, hours per design)Number of potential simulations scale exponentially (mp)
p :: parameter countm :: parameter resolution
Benjamin C. Lee, David M. Brooks 5 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Simulation ChallengesSimulation ParadigmRegression Theory
Microarchitectural Sampling
Temporal SamplingSample from instruction traces in time domainReduce simulation costs via size of inputsSynthetic traces from profiled workloads 1
Sampled traces from phase analysis 2
Spatial SamplingSample from design spaceReduce simulation costs via number of simulations
Selectively simulate modest number of designsSample points randomly from design space for simulationDecouple resolution of design space and simulation
Efficiently leverage simulation data with inferenceReveal trends, trade-offs from sparse samplingEnable predictions for metrics of interest
Benjamin C. Lee, David M. Brooks 7 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Simulation ChallengesSimulation ParadigmRegression Theory
Regression Theory
Statistical InferenceModels approximate solutions to intractable problemsRequires initial data to train, formulate modelLeverages correlations from initial data for prediction
Regression ModelsLow formulation costs (1K samples from 1B designs)Accurate inference (5 − 7% median error)Efficient computation (100’s of predictions per second)
Benjamin C. Lee, David M. Brooks 8 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Experimental MethodologyModel Evaluation
Outline
Motivation & BackgroundSimulation ChallengesSimulation ParadigmRegression Theory
Sampling and ModelingExperimental MethodologyModel Evaluation
ObjectiveIdentify efficient heterogeneous design compromisesMitigate penalties from single design compromise
ApproachSimulate 1K samples from design spaceFormulate regression models for performance, powerIdentify per benchmark optima (bips3/w) via regressionIdentify compromises via K-means clustering
4Kumar+[ISCA’04], Kumar+[PACT’06]Benjamin C. Lee, David M. Brooks 18 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Pareto FrontierMultiprocessor Heterogeneity
Multiprocessor Heterogeneity II
Benjamin C. Lee, David M. Brooks 19 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Outline
Motivation & BackgroundSimulation ChallengesSimulation ParadigmRegression Theory
Sampling and ModelingExperimental MethodologyModel Evaluation
Chip Multiprocessor DesignDecoupled models (e.g., core and interconnect)Larger parameter space (e.g., in-order execution)
5Green+[Monographs Stat & App Prob]Benjamin C. Lee, David M. Brooks 22 :: BARC :: 26 Jan 07
Motivation & BackgroundSampling and Modeling
Design OptimizationConclusion
Conclusion
Exploration ParadigmComprehensively understand design spaceSelectively simulate modest number of designsEfficiently leverage simulation data with inference
Regression ModelingStatistical analysis for robust, efficient modelsLow formulation costs with accurate inferenceComputationally efficient
Model Evaluation7.2%, 5.4% median errors for performance, powerApplicable to practical design optimization
Benjamin C. Lee, David M. Brooks 23 :: BARC :: 26 Jan 07
Appendix Further Reading
Further Readingwww.deas.harvard.edu/∼bclee
B.C. Lee and D.M. Brooks and B.R. de Supinski and M. Schulz and K. Singh and S.A. Mckee.Methods of inference and learning for performance modeling of parallel applicationsPPoPP’07: Symposium on Principles and Practice of Parallel Programming, March 2007.
B.C. Lee and D.M. Brooks.Illustrative design space studies with microarchitectural regression modelsHPCA-13: International Symposium on High Performance Computer Architecture, Feb 2007.
B.C. Lee and D.M. Brooks.Accurate, efficient regression modeling for microarchitectural performance, power prediction.ASPLOS-XII: International Conference on Architectural Support for Programming Languagesand Operating Systems, Oct 2006.
B.C. Lee and D.M. Brooks.Statistically rigorous regression modeling for the microprocessor design space.MoBS-2: Workshop on Modeling, Benchmarking, and Simulation, June 2006.
B.C. Lee and D.M. Brooks.Regression modeling strategies for microarchitectural performance and power prediction.Harvard University Technical Report TR-08-06, March 2006.
Benjamin C. Lee, David M. Brooks 24 :: BARC :: 26 Jan 07