Sviluppo di algoritmi
R2025bIl pacchetto di supporto MATLAB Coder™ per le piattaforme NVIDIA Jetson™ e NVIDIA DRIVE® si avvale del prodotto base MATLAB Coder per generare codice C/C++ ottimizzato e sul prodotto GPU Coder™ per generare codice CUDA® ottimizzato. Il codice generato può quindi essere distribuito sulle piattaforme NVIDIA mirando alle CPU ARM® o alle GPU CUDA compatibili di queste piattaforme.
Argomenti
- GPU Programming Paradigm (GPU Coder)
Introduction to GPU accelerated computing.
- Generate Code by Using the GPU Coder App (GPU Coder)
Generate CUDA code from MATLAB code by using the GPU Coder app.
- Generate Code Using the Command Line Interface (GPU Coder)
Generate CUDA code from MATLAB code by using the
codegencommand. - MATLAB Language Features Support for GPU Coder (GPU Coder)
Use the MATLAB language capabilities that GPU Coder supports.
- Functions and Objects Supported for GPU Code Generation (GPU Coder)
Use MATLAB functions that you can generate GPU code from.
- Kernel Creation (GPU Coder)
Algorithm structures and patterns that create CUDA GPU kernels
- Code Generation for Deep Learning Networks by Using cuDNN (GPU Coder)
Generate code for pretrained convolutional neural networks by using the cuDNN library.
- Code Generation for Deep Learning Networks by Using TensorRT (GPU Coder)
Generate code for pretrained convolutional neural networks by using the TensorRT library.
Esempi in primo piano
Deploy and Run Sobel Edge Detection on NVIDIA Jetson
This step of the example shows how to deploy a MATLAB® function onto an NVIDIA® Jetson™ board. You generate CUDA® code that takes snapshots using a webcam on the Jetson board, detects edges in the snapshots, and displays the output on the Jetson board. The Jetson board runs the code without using MATLAB.
