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Please join MathWorks for complimentary technical seminars on April 28 & 29, 2009 at the University of Michigan, Ann Arbor.  The seminars will be held at the Duderstadt Center, Windows Training Room 1 (Room 3358).  Faculty, staff, researchers, and students are welcome to attend all or part of the seminars. There are three technical seminars:

• Data Analysis with MATLAB
• Speeding Up MATLAB Applications
• Parallel and Distributed Computing with MATLAB

Register now to attend and receive your free presentation and demonstration files at: http://www.mathworks.com/seminars/umi0409

We hope you can join us!

April 28, 10:00 a.m.–12:00 p.m.: Data Analysis with MATLAB

During this session we will use examples to demonstrate how to acquire, analyze, and visualize data through mathematical, statistical, and engineering functions that support common engineering operations.  This session is designed to be an overview of the MATLAB technical computing environment. Highlights include:

• Importing data from various sources
• Performing statistical analysis
• Automating analysis via automatic m-code generation
• Building GUI’s and generating reports

April 28, 1:00 p.m.–3:00 p.m.: Speeding up MATLAB Applications

In this session we will discuss and demonstrate simple ways to improve and optimize your code that can boost execution speed by orders of magnitude.  We will also address common pitfalls in writing m-code, explore the use of the MATLAB Profiler to find bottlenecks, and briefly introduce our Parallel Computing Toolbox and Distributed Computing Server to solve computationally and data-intensive problems on multicore computers and clusters. Highlights include:

• Understand memory usage and vectorization in MATLAB
• Address bottlenecks in your programs
• Optimize file I/O to streamline your code
• Transition from serial to parallel MATLAB programs

April 29, 10:00 a.m.–12:00 p.m.: Parallel and Distributed Computing with MATLAB

This session will show you how to perform parallel and distributed computing in MATLAB to solve computationally and data-intensive problems on multicore computers and clusters.  We will introduce you to parallel processing constructs such as parallel for-loops, distributed arrays, parallel numerical algorithms, and message-passing functions that let you implement task and data parallel algorithms in MATLAB at a high level without programming for specific hardware and network architectures.  And without changing the code, we will show you how to run the same application on a computer cluster. Highlights include:

• Applications of parallel computing
• Implicit multi-threaded computations
• Interactive applications to scheduled applications
• Scaling your work up to a computer cluster
• Tips/tricks on parallel coding in MATLAB