GPU Algorithm Acceleration

Accelerate your code using basic GPU computing

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 Parallel Computing Toolbox™ to enable GPU support.

For a list of functions that accept GPU arrays, see Function List (GPU Arrays).

Rubriques

Exécutez les fonctions MATLAB sur un GPU (Parallel Computing Toolbox)
Fournissez un argument gpuArray pour exécuter automatiquement des fonctions sur un GPU.
Exigences de calcul GPU (Parallel Computing Toolbox)
Prise en charge des architectures GPU NVIDIA^®.
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)
Speed up deep neural network training using multiple GPUs locally or in the cloud.
Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis. (depuis R2021a)
GPU Acceleration of Scalograms for Deep Learning (Wavelet Toolbox)
Use your GPU to accelerate feature extraction for signal classification.