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Department of Defense High Performance Computing Modernization Program. HPCMP Benchmarking Update. Cray Henry April 2008. Outline. Context – HPCMP Initial Motivation from 2003 Process Review Results. DoD HPC Modernization Program. DoD HPC Modernization Program.

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Hpcmp benchmarking update

Department of DefenseHigh Performance Computing Modernization Program

HPCMP Benchmarking Update

Cray Henry

April 2008


Outline

Outline

  • Context – HPCMP

  • Initial Motivation from 2003

  • Process Review

  • Results


Dod hpc modernization program

DoD HPC Modernization Program


Dod hpc modernization program1

DoD HPC Modernization Program


Hpcmp serves a large diverse dod user community

HPCMP Serves a Large, Diverse DoD User Community

519 projects and 4,086 users at approximately 130 sites

Requirements categorized in 10 Computational Technology Areas (CTA)

FY08 non-real-time requirements of 1,108 Habu-equivalents

Computational Fluid Dynamics – 1,572 Users

Computational Electromagnetics & Acoustics – 337 Users

Electronics, Networking, and Systems/C4I – 114 Users

Computational Structural Mechanics – 437 Users

Environmental Quality Modeling & Simulation – 147 Users

Forces Modeling & Simulation – 182 Users

Computational Chemistry, Biology & Materials Science – 408 Users

Climate/Weather/Ocean Modeling & Simulation – 241 Users

Signal/Image Processing – 353 Users

Integrated Modeling & Test Environments – 139 Users

156 users are self characterized as “Other”


Benchmarks have real impact

Benchmarks Have REAL Impact

  • In 2003 we started to describe our benchmarking approach

  • Today benchmarks are even more important


2003 benchmark focus

2003 Benchmark Focus

  • Focused on application benchmarks

  • Recognized application benchmarks were not enough


2003 challenge move to synthetic benchmarks

2003 Challenge – Move to Synthetic Benchmarks

  • 5 years later we have made progress, but not enough to fully transition to synthetics

  • Supported over $300M in purchases so far


Comparison of hpcmp system capabilities fy 2003 fy 2008

Comparison of HPCMP System Capabilities – FY 2003 - FY 2008

Habu-equivalents per Processor


What has changed since 2003

What Has Changed Since 2003

(TI-08) Introduction of performance modeling and predictions

Primary emphases still on application benchmarks

Performance modeling now used to predict some application performance

Performance predictions and measured benchmark results compared for HPCMP systems used in TI-08 to assess accuracy

(TI-08) Met one on one with vendors to review performance predictions for each vendor’s individual systems


Overview of ti xx acquisition process

Overview of TI-XX Acquisition Process

Usability/past performance information on offered systems

Determine requirements, usage, and allocations

Choose application benchmarks, test cases, and weights

Vendors provide measured and projected times on offered systems

Measure benchmark times on DoD standard system

Determine performance for each offered system per application test case

Determine performance for each offered system

Collective acquisition decision

Measure benchmark times on existing DoD systems

Determine performance for each existing system per application test case

Use optimizer to determine price/performance for each offered system and combination of systems

Center facility requirements

Life-cycle costs for offered systems

Vendor pricing


Ti 09 application benchmarks

TI-09 Application Benchmarks

AMR – Gas dynamics code

(C++/FORTRAN, MPI, 40,000 SLOC)

AVUS (Cobalt-60) – Turbulent flow CFD code

(Fortran, MPI, 19,000 SLOC)

CTH – Shock physics code

(~43% Fortran/~57% C, MPI, 436,000 SLOC)

GAMESS – Quantum chemistry code

(Fortran, MPI, 330,000 SLOC)

HYCOM – Ocean circulation modeling code

(Fortran, MPI, 31,000 SLOC)

ICEPIC – Particle-in-cell magnetohydrodynamics code

(C, MPI, 60,000 SLOC)

LAMMPS – Molecular dynamics code

(C++, MPI, 45,400 SLOC)

Red = predicted

Black = benchmarked


Predicting code performance for ti 08 and ti 09

Predicting Code Performance for TI-08 and TI-09

*The next 12 charts were provided by the Performance Modeling and Characterization Group at the San Diego Supercomputer Center.


Prediction framework processor and communications models

Prediction Framework – Processor and Communications Models


Hpcmp benchmarking update

Memory Subsystem Is Key in Predicting Performance


Hpcmp benchmarking update

Red Shift – Memory Subsystem Bottleneck


Hpcmp benchmarking update

Predicted Compute Time Per Core –HYCOM


Hpcmp benchmarking update

One curve per stride pattern

Plateaus correspond to data fitting in cache

Drops correspond to data split between cache levels

MultiMAPS ported to C and will be included in HPC Challenge Benchmarks

Sample MultiMAPS Output

Memory Bandwidth (MB/s)

Working Set Size (8 Byte Words)

MultiMAPS System Profile


Hpcmp benchmarking update

4 Core Woodcrest Node

L2 cache being shared

Modeling the Effects of Multicore


Hpcmp benchmarking update

Performance Sensitivity of LAMMPS LRG to 2x Improvements


Hpcmp benchmarking update

Performance Sensitivity of OVERFLOW2 STD to 2x Improvements


Hpcmp benchmarking update

Performance Sensitivity of OVERFLOW2 LRG to 2x Improvements


Hpcmp benchmarking update

Main Memory and L1 Cache Have Most Effect on Runtime


Differences between predicted and measured benchmark times unsigned

Differences Between Predicted and Measured Benchmark Times (Unsigned)

Note: Average uncertainties of measured benchmark times on loaded HPCMP systems are approximately 5%.


Hpcmp benchmarking update

25

Solving the hard problems . . .

9/4/2014


Hpcmp benchmarking update

26

Solving the hard problems . . .

9/4/2014


Hpcmp benchmarking update

27

Solving the hard problems . . .

9/4/2014


Hpcmp benchmarking update

30

Solving the hard problems . . .

9/4/2014


Hpcmp benchmarking update

31

Solving the hard problems . . .

9/4/2014


Hpcmp benchmarking update

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Solving the hard problems . . .

9/4/2014


What s next

What’s Next?

More focus on Signature Analysis

Continue to evolve application benchmarks to represent accurately the HPCMP computational workload

Increase profiling and performance modeling to understand application performance better

Use performance predictions to supplement application benchmark measurements and to guide vendors in designing more efficient systems


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