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.
Generate CUDA C code from MATLAB code by using the GPU Coder app.
Generate CUDA C code from MATLAB code by using the
Behavioral verification of generated code, traceability, and code generation reports.
Generate code for pretrained convolutional neural networks by using the cuDNN library.
Generate code for pretrained convolutional neural networks by using the TensorRT library.
Generate C++ code for prediction from a deep learning network targeting an ARM® Mali GPU processor.
Introduction to GPU accelerated computing.