1. © 2008 The MathWorks, Inc. ® ® Parallel Computing with MATLAB ® Silvina Grad-Freilich Manager, Parallel Computing Marketing sgrad@mathworks.com sgrad@mathworks.com

2. © 2007 IDC 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. 3 ® ® TM Headquarters: Natick, Massachusetts US Revenues ~$450M in 2007 Privately held Over 1,800 employees worldwide More than 1,000,000 users in 175+ countries The MathWorks at a Glance Earth’s topography on an equidistant cylindrical projection, created with MATLAB ® and Mapping Toolbox ™.

4. 4 ® ® TM MathWorks Product Family Overview MATLAB Product Family View full product list Simulink Product FamilyApplication-Specific Products

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

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

7. 7 ® ® Parallel Computing with MATLAB ® Parallel Computing Toolbox™ T OOLBOXES B LOCKSETS Computer Cluster CPU MATLAB Distributed Computing Server Scheduler Worker

8. 8 ® ® 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 ®

9. 9 ® ® Support in Optimization Toolbox

10. 10 ® ® Distributing Tasks (Task Parallel) Time Processes

11. 11 ® ®

12. 12 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 “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.

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

14. 14 ® ®

15. 15 ® ® Batch Execution >> createMatlabPoolJob

16. 16 ® ® 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

17. 17 ® ® Scale Up to Cluster Configuration with No Code Changes Parallel Computing Toolbox Computer Cluster MATLAB Distributed Computing Server Scheduler CPU Worker

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

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

20. 20 ® ® Computer Cluster Scheduler CPU Worker Dynamic Licensing

21. 21 ® ® Computer Cluster Scheduler CPU Worker Dynamic Licensing

22. 22 ® ® Open API for generic schedulers Support for Third-Party Schedulers

23. 23 ® ® 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

24. © 2008 The MathWorks, Inc. ® ® Thank you! Silvina Grad-Freilich sgrad@mathworks.com