MathWorks parallel computing products help you harness a variety of computing resources for solving your computationally intensive problems. You can accelerate the processing of repetitive computations, process large amounts of data, or offload processor-intensive tasks on a computing resource of your choice—multicore computers, GPUs, or larger resources such as computer clusters and cloud computing services.
Parallel programming constructs in Parallel Computing Toolbox™, such as parallel for-loops, and GPU-enabled MATLAB functions offer an easy way to speed up your MATLAB® code. Using these constructs to accelerate your computations require minimal code changes. Built-in parallel and GPU computing algorithms significantly reduce the programming effort required for you to take advantage of your high-performance systems.
MATLAB GPU Support (Overview)
Using Parallel Computing Toolbox and MATLAB Distributed Computing Server™, you can work with matrices and multidimensional arrays that are distributed across the memory of a cluster of computers. Using these distributed arrays, you can store and perform computations on big data sets that are too large to fit in a single computer’s memory. Over 150 parallel MATLAB functions, including linear algebra operations such as mldivide (\), lu and chol, are available for performing computations on these large distributed matrices. Using these functions you interact with arrays as you would with MATLAB arrays and manipulate distributed data without low-level MPI programming.
Big Data with MATLAB (Overview)
"I wrote and debugged my program by using multiple MATLAB workers on a workstation. I then ran it on the EGEE Grid and reduced computation time from 5 days to just 6 hours."Read about this project