Get Started with GPU Coder

GPU Coder™ generates optimized CUDA^® code from MATLAB^® code and Simulink^® models. The generated code includes CUDA kernels for parallelizable parts of your deep learning, embedded vision, and signal processing algorithms. For high performance, the generated code calls optimized NVIDIA^® CUDA libraries, including TensorRT, cuDNN, cuFFT, cuSolver, and cuBLAS. The code can be integrated into your project as source code, static libraries, or dynamic libraries, and it can be compiled for desktops, servers, and GPUs embedded on NVIDIA Jetson, NVIDIA DRIVE, and other platforms. You can use the generated CUDA within MATLAB to accelerate deep learning networks and other computationally intensive portions of your algorithm. GPU Coder lets you incorporate handwritten CUDA code into your algorithms and into 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) and processor-in-the-loop (PIL) testing.

Tutorials

MATLAB

Code Generation by Using the GPU Coder App

Generate CUDA C code from MATLAB code by using the GPU Coder app.

Code Generation Using the Command Line Interface

Generate CUDA C code from MATLAB code by using the codegen command.

Verify Correctness of the Generated Code

Behavioral verification of generated code, traceability, and code generation reports.

Code Generation for Deep Learning Networks by Using cuDNN

Generate code for pretrained convolutional neural networks by using the cuDNN library.

Code Generation for Deep Learning Networks by Using TensorRT

Generate code for pretrained convolutional neural networks by using the TensorRT library.

Simulink

Simulation Acceleration by Using GPU Coder

Improve simulation speed by using NVIDIA GPUs.

Code Generation from Simulink Models by Using GPU Coder

Generate CUDA code from Simulink models by using GPU Coder.

Deep Learning in Simulink Using Deep Neural Networks Library

Simulate and generate code for deep learning models in Simulink using library blocks.

About Code Generation from MATLAB Algorithms

• GPU Programming Paradigm
  

  Introduction to GPU accelerated computing.

• GPU Code Generation Workflow
  

  Workflow for CUDA MEX and standalone CUDA C++ code generation.

Featured Examples

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

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