Introduction to Parallel Computing

As you probably know Matlab  has a Parallel Computing Toolbox available that allows you to use multicore processors, GPUs and computer clusters to improve your scripts. In my next posts I will talk about the main parallel functions that come with this toolbox (parfor, spmd, batch) and how/when to properly use them.

For now I'll leave you with the description of the Parallel Computing Toolbox taken from mathworks.com
  

 

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. You can use the toolbox with Simulink^® to run multiple simulations of a model in parallel.

The toolbox provides twelve workers (MATLAB computational engines) to execute applications locally on a multicore desktop. Without changing the code, you can run the same application on a computer cluster or a grid computing service (using MATLAB Distributed Computing Server™). You can run parallel applications interactively or in batch.