## Parallel Computing |

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.