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.
- Code Generation by Using the GPU Coder App
Generate CUDA code from MATLAB code by using the GPU Coder app.
- Code Generation Using the Command Line Interface
Generate CUDA code from MATLAB code by using the
- 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.
- Debug CUDA MEX Functions
Suggestions for debugging CUDA MEX function.
- Accelerate Simulation Speed by Using GPU Coder
Achieve faster simulation for models that contain MATLAB Function blocks.
- Code Generation from Simulink Models with GPU Coder
Generate CUDA code from Simulink models by using GPU Coder.
- GPU Code Generation for Blocks from the 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
Design, implement, and verify generated CUDA MEX for acceleration and standalone CUDA code for deployment.