© 2008 the mathworks, inc. ® ® parallel computing with matlab ® silvina grad-freilich manager,...

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Parallel Computing with MATLAB®

Silvina Grad-Freilich

Manager, Parallel Computing Marketing sgrad@mathworks.com

© 2007 IDC

Some Customer Pain PointsSome Customer Pain Points

Clusters are still hard to use and manage Power, cooling and floor space are major issues Third party software costs Weak interconnect performance at all levels Applications & programming — Hard to scale beyond a node RAS is a growing issue Storage and data management Multi-processor type support and accelerator support

Requirements are diverging High-end — need more, but is a shrinking segment Mid and lower end – the mainstream will look more for complete

solutions New entrants – ease-of-use will drive them, plus need

applications Parallel software is missing for most users

And will get weaker in the near future—Software will be the #1 roadblock

Multi-core will cause many issues to “hit-the-wall”

Hard to scale beyond a node Parallel software is missing for most users

…. Software will be the #1 roadblock

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Headquarters:Natick, Massachusetts US

Revenues ~$450M in 2007

Privately held

Over 1,800 employees worldwide

More than 1,000,000 usersin 175+ countries

The MathWorks at a Glance

Earth’s topography on an equidistant cylindrical projection,

created with MATLAB® and Mapping Toolbox™.

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MathWorks Product Family Overview

MATLAB Product Family

  View full product list

Simulink Product Family Application-Specific Products

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Three User Communities

Easier programming

CFortran

Higher data volumes & compute intensity

Technical Computing User

PERSONAL SUPERCOMPUTING

WITH MATLAB

Cluster Administrator

Optimal Hardware

and License

UseHPC User

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Using Fortran and MPI Using MATLAB and MPIUsing Distributed Arrays

P>> D = distributed(A)P>> E = D’

Easier Parallel Programming Example: Transposing a Distributed Matrix

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Parallel Computing with MATLAB®

ParallelComputing Toolbox™

TOOLBOXES

BLOCKSETS

Computer ClusterComputer Cluster

CPU

CPU

CPU

CPU

MATLAB Distributed Computing ServerMATLAB Distributed Computing Server

Scheduler

Worker

Worker

Worker

Worker

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Toolbox Support:Optimization Toolbox™

Genetic Algorithm and Direct Search Toolbox™

SystemTest™

parfor

job and tasks

No code changes

Trivial changes

Extensive changes

Task Parallel Data Parallel

darray

MATLAB and MPI

Parallel Computing with MATLAB®

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Support in Optimization Toolbox

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Distributing Tasks (Task Parallel)

Time Time

Pro

cess

es

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Argonne National Laboratory Develops Powertrain Systems Analysis Toolkit with MathWorks™ Tools

ChallengeTo evaluate designs and technologies for hybrid and fuel cell vehicles

SolutionUse MathWorks tools to model advanced vehicle powertrains and accelerate the simulation of hundreds of vehicle configurations

Results Distributed simulation environment developed in

one hour Simulation time reduced from two weeks to

one day Simulation results validated using vehicle test data

“We developed an advanced

framework and scalable powertrain

components in Simulink®, designed

controllers with Stateflow®,

automated the assembly of models

with MATLAB® scripts, and then

distributed the complex simulation

runs on a computing cluster – all

within a single environment."

Sylvain Pagerit

Argonne National Laboratory

“We developed an advanced

framework and scalable powertrain

components in Simulink®, designed

controllers with Stateflow®,

automated the assembly of models

with MATLAB® scripts, and then

distributed the complex simulation

runs on a computing cluster – all

within a single environment."

Sylvain Pagerit

Argonne National Laboratory

Vehicle model created with PSAT.

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Large Data Sets (Data Parallel)

1111 2626 4141

1212 2727 4242

1313 2828 4343

1414 2929 4444

1515 3030 4545

1616 3131 4646

1717 3232 4747

1717 3333 4848

1919 3434 4949

2020 3535 5050

2121 3636 5151

2222 3737 5252

1111 2626 4141

1212 2727 4242

1313 2828 4343

1414 2929 4444

1515 3030 4545

1616 3131 4646

1717 3232 4747

1717 3333 4848

1919 3434 4949

2020 3535 5050

2121 3636 5151

2222 3737 5252

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Batch Execution

>> createMatlabPoolJob

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Run Four Local Workers with a Parallel Computing Toolbox License

Easily experiment with explicit parallelism on multicore machines

Rapidly develop parallel applications on local computer

Parallel Computing

Toolbox

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Scale Up to Cluster Configuration with No Code Changes

Parallel Computing

Toolbox

Computer ClusterComputer Cluster

MATLAB Distributed Computing ServerMATLAB Distributed Computing Server

Scheduler

CPU

CPU

CPU

CPU

Worker

Worker

Worker

Worker

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Computer ClusterComputer Cluster

Scheduler

Dynamic Licensing

CPU

CPU

CPU

CPU

Worker

Worker

Worker

Worker

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Computer ClusterComputer Cluster

Scheduler

CPU

CPU

CPU

CPU

Worker

Worker

Worker

Worker

Dynamic Licensing

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Computer ClusterComputer Cluster

Scheduler

CPU

CPU

CPU

CPU

Worker

Worker

Worker

Worker

Dynamic Licensing

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Computer ClusterComputer Cluster

Scheduler

CPU

CPU

CPU

CPU

Worker

Worker

Worker

Worker

Dynamic Licensing

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Open API for generic schedulers

Support for Third-Party Schedulers

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Summary

Back to the pains… Hard to scale beyond a node Parallel software is missing for most users

The power of supercomputing is now accessible to thousands of engineers and scientists MATLAB users - delivering the power of HPC HPC users - delivering the benefits of MATLAB

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Thank you!

Silvina Grad-Freilich

sgrad@mathworks.com