Get Started with GPU Coder

Generate CUDA code for NVIDIA GPUs

GPU Coder™ generates optimized CUDA® code from MATLAB® code for deep learning, embedded vision, and autonomous systems. The generated code calls optimized NVIDIA® CUDA libraries, including cuDNN, cuSolver, and cuBLAS. It can be integrated into your project as source code, static libraries, or dynamic libraries, and can be used for prototyping on GPUs such as the NVIDIA Tesla® and NVIDIA Tegra®. You can use the generated CUDA within MATLAB to accelerate computationally intensive portions of your MATLAB code. GPU Coder lets you incorporate legacy CUDA code into your MATLAB algorithms and the generated code.

When used with Embedded Coder®, GPU Coder lets you verify the numerical behavior of the generated code via software-in-the-loop (SIL) testing.

Installation and Configuration

Tutorials

About Code Generation from MATLAB Algorithms

Featured Examples

GPU Code Generation: The Mandelbrot Set

GPU Code Generation: The Mandelbrot Set

Generate CUDA® code from a simple MATLAB® function by using GPU Coder™. A Mandelbrot set implementation by using standard MATLAB commands acts as the entry-point function. This example uses the codegen command to generate a MEX function that runs on the GPU. You can run the MEX function to check for run-time errors.

Open Script
Code Generation for Deep Learning Networks

Code Generation for Deep Learning Networks

Perform code generation for an image classification application that uses deep learning. It uses the codegen command to generate a MEX function that runs prediction by using image classification networks such as MobileNet-v2, ResNet, and GoogLeNet.

Open Script
Integrating GPU Coder™ into Simulink®

Integrating GPU Coder™ into Simulink®

Integrate GPU Coder™ into Simulink®. GPU Coder is not supported for Simulink blocks, but you can still leverage GPUs in Simulink by generating a dynamic linked library (dll) using GPU Coder, and then integrating it into a Simulink block by using coder.ExternalDependency APIs. Sobel edge detection is used as an example to demonstrate this concept.

Open Script
Integrating Deep Learning with GPU Coder into Simulink

Integrating Deep Learning with GPU Coder into Simulink

Integrate the CUDA® code generated for a deep learning network into Simulink®. GPU coder™ does not support code generation for Simulink blocks but you can still use the computational power of GPUs in Simulink by generating a dynamic linked library (dll) with GPU Coder and then integrating it into Simulink as an S-Function block by using the legacy code tool. For more information, see legacy_code. To illustrate this concept, the example uses Lane Detection Optimized with GPU Coder. The original example used a C++ file with OpenCV functions to read the frames, draw lanes, and overlay frame rate information on the video output. This example uses Simulink blocks from the Computer Vision System Toolbox™ to perform the same operations.

Open Script
GPU Coder Documentation
Support