Perform a parameter sweep in parallel and plot progress during parallel computations. You can use a DataQueue to monitor results during computations on a parallel pool. You can also use a DataQueue with parallel language features such as parfor, parfeval and spmd.

Uses Parallel Computing Toolbox™ to perform a two-dimensional Fast Fourier Transform (FFT) on a GPU. The two-dimensional Fourier transform is used in optics to calculate far-field diffraction patterns. These diffraction patterns are observed when a monochromatic light source passes through a small aperture, such as in Young's double-slit experiment.

Distributed arrays are well-suited for large mathematical computations, such as large problems of linear algebra. This example shows how you can solve a system of linear equations of the form Ax=b in parallel with a direct method using distributed arrays. In the same way as for arrays stored in the client memory, you can use mldivide to solve can systems of linear equations defined using distributed arrays, so you do not need to change your code.

Develop your parallel MATLAB® code on your local machine and scale up to a cluster. Clusters provide more computational resources to speed up and distribute your computations. You can run your code interactively in parallel on your local machine, then on a cluster, without changing your code. When you are done prototyping your code on your local machine, you can offload your computations to the cluster using batch jobs. So, you can close MATLAB and retrieve the results later.

Access a large data set in the cloud and process it in a cloud cluster using MATLAB capabilities for big data.

Train a convolutional neural network using MATLAB automatic support for parallel training. Deep learning training often takes hours or days. With parallel computing, you can speed up training using multiple graphical processing units (GPUs) locally or in a cluster in the cloud. If you have access to a machine with multiple GPUs, then you can complete this example on a local copy of the data. If you want to use more resources, then you can scale up deep learning training to the cloud. To learn more about your options for parallel training, see Scale Up Deep Learning in Parallel and in the Cloud. This example guides you through the steps to train a deep learning network in a cluster in the cloud using MATLAB automatic parallel support.