Parallel Computing Toolbox

Speeding Up Task-Parallel Applications

You can speed up some applications by organizing them into independent tasks (units of work) and executing multiple tasks concurrently. This class of task-parallel applications includes simulations for design optimization, BER testing, Monte Carlo simulations, and repetitive analysis on a large number of data files.

The toolbox offers parfor, a parallel for-loop construct that can automatically distribute independent tasks to multiple MATLAB workers (MATLAB computational engines running independently of your desktop MATLAB session). This construct automatically detects the presence of workers and reverts to serial behavior if none are present. You can also set up task execution using other methods, such as manipulating task objects in the toolbox.

Using parallel for-loops for a task-parallel application. You can use parallel for-loops in MATLAB scripts and functions and execute them both interactively and offline.

Using parallel for-loops for a task-parallel application. You can use parallel for-loops in MATLAB scripts and functions and execute them both interactively and offline.

Next: MATLAB GPU Computing

Try Parallel Computing Toolbox

Get trial software

Machine Learning with MATLAB

View webinar