Accelerazione dell'algoritmo GPU
Per velocizzare il codice, puoi provare a utilizzare la GPU del tuo computer. Se tutte le funzioni che desideri utilizzare sono supportate dalla GPU, puoi semplicemente utilizzare la funzione gpuArray per trasferire i dati di input alla GPU e chiamare la funzione gather per recuperare i dati di output dalla GPU. Per il Deep Learning, MATLAB® fornisce supporto parallelo automatico per più GPU. Per abilitare il supporto GPU è necessario Parallel Computing Toolbox™.
Per un elenco delle funzioni che accettano array GPU, vedere Elenco delle funzioni (array GPU).
Argomenti
- Run MATLAB Functions on a GPU (Parallel Computing Toolbox)
Supply a
gpuArrayargument 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. (Da R2021a)
- GPU Acceleration of Scalograms for Deep Learning (Wavelet Toolbox)
Use your GPU to accelerate feature extraction for signal classification.
Informazioni complementari
Esempi in primo piano
Accelerate Correlation with GPUs
Use a graphics processing unit to accelerate cross-correlation in applications that involve large data sets.
Accelerate Signal Feature Extraction and Classification Using a GPU
Use a graphical processing unit (GPU) to extract signal multidomain features for bearing fault detection.
Classify ECG Signals Using Long Short-Term Memory Networks with GPU Acceleration
Classify heartbeat electrocardiogram data using deep learning and signal processing with GPU acceleration.
Waveform Segmentation Using Deep Learning
Segment human electrocardiogram signals using time-frequency analysis and deep learning.
Human Health Monitoring Using Continuous Wave Radar and Deep Learning
Use a deep learning network to reconstruct electrocardiograms from continuous-wave radar signals.
