MATLAB® provides two main ways to take advantage of multicore and multiprocessor computers. By using the full computational power of your machine, you can run your MATLAB applications faster and more efficiently.
Linear algebra and numerical functions such as
sort are multithreaded in MATLAB. Multithreaded computations have been on by default in MATLAB since Release 2008a. These functions automatically execute on multiple computational threads in a single MATLAB session, allowing them to execute faster on multicore-enabled machines. Additionally, many functions in Image Processing Toolbox™ are multithreaded.
You can run multiple MATLAB workers (MATLAB computational engines) on a single machine to execute applications in parallel, with Parallel Computing Toolbox™. This approach allows you more control over the parallelism than with built-in multithreading, and is often used for coarser grained problems such as running parameter sweeps in parallel.
With programming constructs such as parallel for-loops (
batch, you can write parallel MATLAB programs with minimal programming effort. Several other products including Optimization Toolbox™, Statistics and Machine Learning Toolbox™, and Simulink Coder™ offer built-in parallel algorithms that work with Parallel Computing Toolbox. These algorithms let you distribute computations across available parallel computing resources without writing any parallel code.
Unlike with multithreaded computations, you can scale parallel applications that use MATLAB workers beyond a single machine to a computer cluster or grid using MATLAB Distributed Computing Server™.
In this course you’ll learn the tools and techniques for distributing code and writing parallel algorithms in MATLAB®.