GPU 算法加速

使用基本 GPU 计算加快代码执行速度

为了加快代码执行速度，您可以尝试使用您计算机的 GPU。如果 GPU 上支持您要使用的所有函数，您可以直接使用 gpuArray 函数将输入数据传输到 GPU，并调用 gather 函数从 GPU 检索输出数据。对于深度学习，MATLAB^® 为多个 GPU 提供了自动并行支持。您需要 Parallel Computing Toolbox™ 来启用 GPU 支持。

有关接受 GPU 数组的函数列表，请参阅函数列表（GPU 数组）。

函数

`gather`	Transfer distributed array or gpuArray to local workspace
`gpuArray`	Array stored on GPU

主题

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 gpuArray (Parallel Computing Toolbox) argument.
GPU Support by Release (Parallel Computing Toolbox)
Support for NVIDIA^® GPU architectures.
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 a deep learning network and time-frequency analysis.
GPU Acceleration of Scalograms for Deep Learning (Wavelet Toolbox)
Use your GPU to accelerate feature extraction for signal classification.

GPU 算法加速

函数

主题

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