Kernel Creation from Simulink Models

Simulink blocks that create CUDA^® GPU kernels

GPU Coder™ generates and executes optimized CUDA kernels from Simulink^® models containing MATLAB^® Function blocks. The generated code calls optimized NVIDIA^® CUDA libraries, including cuFFT, cuSolver, cuBLAS, cuDNN, and TensorRT. You can use the generated CUDA code within Simulink to perform simulation acceleration, rapid prototyping on all NVIDIA GPU devices, and hardware-in-the-loop testing.

Model Settings

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Topics

Simulation Acceleration by Using GPU Coder
Improve simulation speed by using NVIDIA GPUs.
Code Generation from Simulink Models with GPU Coder
Generate CUDA code from Simulink models by using GPU Coder.
Targeting NVIDIA Embedded Boards
Build and deploy to NVIDIA GPU boards.
Numerical Equivalence Testing
Compare results from model and generated code simulations.
Parameter Tuning and Signal Monitoring by Using External Mode
Tune parameters and monitor signals through a TCP/IP communication channel between development computer and target hardware.

Featured Examples

GPU Code Generation for Lane Detection in Simulink

Generate CUDA® code for a Simulink® model that can detect and output lane marker boundaries on an image. This example takes RGB image as an input and uses the imresize, rgb2gray, ordfilt2 (Image Processing Toolbox), hough (Image Processing Toolbox), houghpeaks (Image Processing Toolbox), and houghlines (Image Processing Toolbox) functions that are part of Image Processing Toolbox™ to detect lane markings. This example closely follows Lane Detection on the GPU by Using the houghlines Function.

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GPU Code Generation for a Fog Rectification Simulink Model

A typical implementation of fog rectification algorithm. The example uses conv2, im2gray, and imhist (Image Processing Toolbox) functions. This example closely follows Fog Rectification example. This example illustrates the following concepts: Verification of GPU Environment.