WebStore presents MATLAB Day 2015:

9/17/15

1000 Micro and Nanotechnology Lab, 208 North Wright Street , Urbana

9:45: Registration and sign-in

10 am - 12 noon  Analyzing and Visualizing Data with MATLAB

1:15 pm  Registration and sign-in

1:30 - 3:30 pm   Parallel and Distributed Computing with MATLAB

10 am -- Analyzing and Visualizing Data with MATLAB

MATLAB is a high-level language and interactive environment for data analysis, visualization, and numerical computation. Using MATLAB, you can reach solutions faster and easier than with spreadsheets or traditional programming languages, such as C/C++ or Java.
During this introductory seminar, we provide an overview of MATLAB and introduce you to the powerful statistical analysis and visualization capabilities available in the MATLAB product family.  We will demonstrate how to analyze and visualize data, present desktop tools for editing code, and show you how to publish and share your results.

Highlights include:

• Accessing data from files, spreadsheets and other sources
• Performing statistical analysis, curve and surface fitting routines
• Developing algorithms and applications to automate your workflow
• Generating reports in HTML and other file formats to share your work
• Developing and deploying MATLAB code as an executable or software component

1:30 pm -- Parallel and Distributed Computing with MATLAB

The Parallel Computing Toolbox lets you solve computationally and data-intensive problems using multicore processors, GPUs, and computer clusters. High-level constructs—parallel for-loops, special array types, and parallelized numerical algorithms—let you parallelize MATLAB applications without CUDA or MPI programming.
In this session we show how to solve and boost the execution speed of computationally intensive problems using multicore processors, GPUs, and computer clusters.  We introduce and demonstrate high-level programming constructs that allow you to create parallel MATLAB applications without low-level programming.

Highlights include:

• Toolboxes with built-in algorithms for parallel computing
• Creating parallel applications to speed up independent tasks
• Scaling up to computer clusters, grid environments or clouds
• Using distributed arrays to work with very large data sets
• Employing CPUs and GPUs to speed up your computations