November 13, 2006 Performance Engineering Research Institute 1 Scientific Discovery through Advanced Computation Performance Engineering.

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November 13, 2006 Performance Engineering Research Institute www.peri-scidac.org

Scientific Discovery through Advanced Computation

Performance Engineering Research Institute

David H BaileyLawrence Berkeley National Laboratory

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Argonne National Laboratory

Lawrence Berkeley National Laboratory

Lawrence Livermore National Laboratory

Oak Ridge National Laboratory

Rice University

University of California at San Diego

University of Maryland

University of North Carolina

University of Southern California

University of Tennessee

Participating Institutions

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SciDAC

Scientific Discovery through Advanced Computation

DOE Office of Science’s path to Petascale computational science

Maximizing performance is getting harder: Systems are more complicated

O(100K) multi-core CPUsSIMD extensions

Codes are more complicatedMulti-disciplinaryMulti-scale

IBM BlueGene at LLNL

Cray Xt3 at ORNL

BeamBeam3D accelerator modeling

POP model of El Nino

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Example of the Challenge

Figure courtesy Roger Logan, LLNL

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SciDAC-1 PERC

Performance Evaluation Research Center (PERC)

Initial goal was to develop performance related toolsBenchmarksAnalysisModelingOptimization

Second phase refocused on SciDAC applications incl.Community Climate System ModelPlasma Microturbulence ProjectOmega3P accelerator model

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Some Lessons Learned

Performance portability is criticalCodes outlive machinesScientists can’t publish that they migrated code

Computational scientists were not interested in toolsThey wanted experts to work with themSuch experts are not scalable

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SciDAC-2 PERI

Performance Engineering Research Institute

Performance modeling of applicationsHow fast do we expect to go?

Automatic tuningLong term research goalRemove burden from scientific programmers

Application engagementNear-term impact on SciDAC applications

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Performance Modeling

Modeling is critical for automation of tuningNeed to know where to focus effort

Where are the bottlenecks?Need to know when we’re done

How fast can we hope to go?

Obvious improvements:Greater accuracyReduced cost

Modeling efforts contribute to procurements and other activities beyond PERI automatic tuning

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Performance Tuning

Humans have been doing this for 50 years

Compilers have been doing it statically for 40 years

Recent self-tuning libraries:PHIPAC, ATLAS, FFTW, SPIRAL, SPOOLES

Performance Engineering Research Institute goal:Automatic performance tuning of applications

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Automatic Performance Tuning of Scientific Code

Long-term goals for PERI

Automate the process of tuning software to maximize its performance

Reduce the performance portability challenge facing computational scientists.

Address the problem that performance experts are in short supply

Build upon forty years of human experience and recent success with linear algebra libraries

PERI automatic tuning framework

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Automatic Tuning Steps

Triage: where to focus effortSemantic analysis: traditional compiler analysisTransformation: code restructuringCode generation: domain specific codeOff-line search: empirical experimentsAssembly: choose the best componentsTraining runs: performance data for feedbackOn-line search: optimize long-running jobs

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Early Results

Empirical optimization of dense matrix-matrix multiplication (Hall, USC)

Empirical optimization of Madness kernel (Moore, UTK)

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PERI Portal

Automatic tuning is common goal of half-a-dozen research projects

No hope of actually integrating them into one systeme.g., Open64, SUIF, and ROSE compilers

Instead PERI will bring up a Web portal which will be our interface to application developers

There will often be “a man behind the curtain”Goal is research demonstration of capability

www.peri-scidac.org/portal

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Application Engagement

Application Engagement Work directly with DOE computational

scientists Ensure successful performance

porting of scientific software Focus PERI research on real problems

Application Liaisons Build long-term personal relationships

with PERI researchers and scientific code teams

Tiger Teams Focus on DOE’s highest priorities

SciDAC-2 INCITE

Maximizing scientific throughput

Optimizing arithmetic kernels

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Summary

SciDAC-2 Performance Engineering Research Institute

Performance modeling of scientific applications so we understand what performance is possible

Automatic performance tuning to alleviate computational scientists from this recurring problem

Near-term impact via direct engagement with SciDAC application teams

November 13, 2006 Performance Engineering Research Institute 1 Scientific Discovery through Advanced Computation Performance Engineering.

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