GPU 算法加速
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
主题
- Run MATLAB Functions on a GPU (Parallel Computing Toolbox)
Supply a
gpuArray
argument to automatically run functions on a GPU. - GPU Computing Requirements (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 deep learning and time-frequency analysis.
- GPU Acceleration of Scalograms for Deep Learning (Wavelet Toolbox)
Use your GPU to accelerate feature extraction for signal classification.