To speed up your code, you can try using your computer’s GPU. If all the functions that you
want to use are supported on the GPU, you can simply use the
gpuArray function to transfer input data to the GPU,
and call the
gather function to retrieve the output data
from the GPU. For deep learning, MATLAB® provides automatic parallel support for multiple GPUs. You need
Computing Toolbox™ to enable GPU
|Fast Fourier transform|
|Inverse fast Fourier transform|
|2-D fast Fourier transform|
|2-D inverse fast Fourier transform|
|Shift zero-frequency component to center of spectrum|
|Inverse zero-frequency shift|
|Discrete cosine transform|
|Inverse discrete cosine transform|
Run MATLAB Functions on a GPU (Parallel Computing Toolbox)
Hundreds of functions in MATLAB and other toolboxes run automatically on a GPU if you supply a
GPU Support by Release (Parallel Computing Toolbox)
Support for NVIDIA® GPU architectures by MATLAB release.
Run MATLAB Functions on Multiple GPUs (Parallel Computing Toolbox)
This example shows how to run MATLAB code on multiple GPUs in parallel, first on your local machine, then scaling up to a cluster.
Deep Learning with MATLAB on Multiple GPUs (Deep Learning Toolbox)
Specify multiple GPUs to use locally or in the cloud for training.
Pedestrian and Bicyclist Classification Using Deep Learning (Phased Array System Toolbox)
This example shows how to classify pedestrians and bicyclists based on their micro-Doppler characteristics using a deep learning network and time-frequency analysis.