Calcul GPU dans MATLAB
Accélérez votre code en exécutant les fonctions MATLAB® sur un GPU
Si toutes les fonctions que vous souhaitez utiliser sont prises en charge sur le GPU, vous pouvez simplement utiliser gpuArray
pour transférer les données d'entrée vers le GPU. Pour démarrer avec le calcul GPU, consultez Exécutez les fonctions MATLAB sur un GPU .
Pour le Deep Learning, MATLAB fournit une prise en charge parallèle automatique de plusieurs GPU. Voir Deep Learning with MATLAB on Multiple GPUs (Deep Learning Toolbox) .
Vous pouvez utiliser la fonction gpuDevice
pour inspecter et sélectionner votre GPU et utiliser les fonctions gpuDeviceTable
pour inspecter plusieurs GPU.
Fonctions
Rubriques
Exécutez le code MATLAB sur le GPU
- Exécutez les fonctions MATLAB sur un GPU
Fournissez un argumentgpuArray
pour exécuter automatiquement des fonctions sur un GPU. - Identifier et sélectionner un dispositif GPU
Cet exemple montre comment utilisergpuDevice
pour identifier et sélectionner l'appareil que vous souhaitez utiliser. - Exigences de calcul GPU
Prise en charge des architectures GPU NVIDIA®. - Establish Arrays on a GPU
UsegpuArray
objects to store data on the GPU and perform calculation on the GPU. - Run MATLAB Functions on Multiple GPUs
This example shows how to run MATLAB® code on multiple GPUs in parallel, first on your local machine, then scaling up to a cluster. - Work with Remote GPUs
This example shows how to run MATLAB® code on multiple remote GPUs in a cluster. (depuis R2024a) - Run MATLAB using GPUs in the Cloud
Take advantage of powerful GPUs in the cloud to accelerate your code. - Train Network Using Automatic Multi-GPU Support (Deep Learning Toolbox)
This example shows how to use multiple GPUs on your local machine for deep learning training using automatic parallel support. - Work with Sparse Arrays on a GPU
Learn about the conditions for working with sparse arrays on a GPU. - Work with Complex Numbers on a GPU
Learn about the conditions for applying functions that might return complex values on a GPU.
Améliorer les performances sur le GPU
- Measure and Improve GPU Performance
Time code running on your GPU and explore options for improving performance. - Improve Performance Using a GPU and Vectorized Calculations
This example shows you how to improve performance by running a function on the GPU instead of the CPU, and by vectorizing the calculations. - Improve Performance of Element-Wise MATLAB Functions on the GPU Using arrayfun
This example shows how to improve the performance of your code by running MATLAB® functions on the GPU usingarrayfun
. - Improve Performance of Small Matrix Problems on the GPU Using pagefun
This example shows how to usepagefun
to improve the performance of independent operations applied to multiple matrices arranged in a multidimensional array. - Benchmarking A\b on the GPU
This example looks at how we can benchmark the solving of a linear system on the GPU.
Apprendre encore plus
- Profile Your Code to Improve Performance
Use the Profiler to measure the time it takes to run your code and identify which lines of code consume the most time or which lines do not run. - Vectorization
Revise loop-based, scalar-oriented code to use MATLAB matrix and vector operations. - Random Number Streams on a GPU
Control the random number streams on a GPU to generate the same sequences of random numbers as on the CPU. - Generating Random Numbers on a GPU
This example shows how to switch between the different random number generators that are supported on the GPU. - Stencil Operations on a GPU
This example uses Conway's "Game of Life" to demonstrate how stencil operations can be performed using a GPU. - Compute the Mandelbrot Set Using GPU-Enabled Functions
This example shows how to use GPU-enabled MATLAB® functions to compute a well-known mathematical construction: the Mandelbrot set.