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Parallel Computing with MATLAB ®

Parallel Computing with MATLAB ®. Silvina Grad-Freilich Manager, Parallel Computing Marketing sgrad@mathworks.com. Some Customer Pain Points. Clusters are still hard to use and manage Power, cooling and floor space are major issues Third party software costs

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Parallel Computing with MATLAB ®

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  1. Parallel Computing with MATLAB® Silvina Grad-Freilich Manager, Parallel Computing Marketing sgrad@mathworks.com

  2. Some 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

  3. The MathWorks at a Glance • Headquarters:Natick, Massachusetts US • Revenues ~$450M in 2007 • Privately held • Over 1,800 employees worldwide • More than 1,000,000 usersin 175+ countries Earth’s topography on an equidistant cylindrical projection, created with MATLAB®and Mapping Toolbox™.

  4. MathWorks Product Family Overview View full product list MATLAB Product Family

  5. Higher data volumes & compute intensity Easier programming • Optimal • Hardware • and • License • Use C Fortran Technical Computing User Three User Communities PERSONAL SUPERCOMPUTING WITH MATLAB HPC User Cluster Administrator

  6. Using Distributed Arrays P>> D = distributed(A) P>> E = D’ Easier Parallel Programming Example: Transposing a Distributed Matrix Using MATLAB and MPI Using Fortran and MPI

  7. Computer Cluster MATLAB Distributed Computing Server CPU CPU CPU CPU Worker Worker Parallel Computing Toolbox™ TOOLBOXES Scheduler Worker BLOCKSETS Worker Parallel Computing with MATLAB®

  8. Parallel Computing with MATLAB® Task Parallel Data Parallel Toolbox Support: Optimization Toolbox™ Genetic Algorithm and Direct Search Toolbox™ SystemTest™ parfor jobandtasks No code changes Trivial changes darray MATLAB and MPI Extensive changes

  9. Support in Optimization Toolbox

  10. Time Time Distributing Tasks (Task Parallel) Processes

  11. Vehicle model created with PSAT. Argonne National Laboratory Develops Powertrain Systems Analysis Toolkit with MathWorks™ Tools • Challenge To evaluate designs and technologies for hybrid and fuel cell vehicles Solution Use 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

  12. 11 11 26 26 41 41 12 12 27 27 42 42 13 13 28 28 43 43 14 14 29 29 44 44 15 15 30 30 45 45 16 16 31 31 46 46 17 17 32 32 47 47 17 17 33 33 48 48 19 19 34 34 49 49 20 20 35 35 50 50 21 21 36 36 51 51 22 22 37 37 52 52 Large Data Sets (Data Parallel)

  13. Batch Execution >> createMatlabPoolJob

  14. Parallel Computing Toolbox Run FourLocal Workers with a Parallel Computing Toolbox License • Easily experiment with explicit parallelism on multicore machines • Rapidly develop parallel applications on local computer

  15. CPU CPU CPU CPU Worker Worker Scheduler Worker Worker Scale Up to Cluster Configuration with No Code Changes Computer Cluster MATLAB Distributed Computing Server Parallel Computing Toolbox

  16. CPU CPU CPU CPU Worker Worker Scheduler Worker Worker Dynamic Licensing Computer Cluster

  17. CPU CPU CPU CPU Worker Worker Scheduler Worker Worker Dynamic Licensing Computer Cluster

  18. CPU CPU CPU CPU Worker Worker Scheduler Worker Worker Dynamic Licensing Computer Cluster

  19. CPU CPU CPU CPU Worker Worker Scheduler Worker Worker Dynamic Licensing Computer Cluster

  20. Support for Third-Party Schedulers Open API for generic schedulers

  21. 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  

  22. Thank you! Silvina Grad-Freilich sgrad@mathworks.com

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