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.
Learn the basics of GPU Coder
MATLAB language syntax and functions for code generation
Algorithm structures and patterns that create CUDA GPU kernels
Troubleshoot code generation issues, improve code execution time, and reduce memory usage of generated code
Generate CUDA code for deep learning neural networks
Deploy generated code to NVIDIA Tegra hardware targets
Support for third-party hardware, such as NVIDIA Drive and Jetson platforms.