NVIDIA GPU Support from GPU Coder

Generate and deploy optimized CUDA code on NVIDIA GPUs


GPU Coder™ generates optimized CUDA® code from MATLAB® code for deep learning, embedded vision, and autonomous systems. The generated code can be compiled and executed on NVIDIA® GPUs. Generated CUDA code calls optimized NVIDIA CUDA libraries including cuDNN, cuSolver, and cuBLAS.

You can use the generated CUDA within MATLAB to accelerate computationally intensive portions of your MATLAB code on NVIDIA GPUs such as NVIDIA Titan® and NVIDIA Tesla® GPUs. GPU Coder lets you incorporate legacy CUDA code into your MATLAB algorithms and the generated code.

You can deploy a variety of trained deep learning networks, such as YOLO, ResNet-50, SegNet, and MobileNet, from Deep Learning Toolbox™ to NVIDIA GPUs. You can generate optimized code for preprocessing and postprocessing along with your trained deep learning networks to deploy complete algorithms.

When used with Embedded Coder®, GPU Coder lets you verify the numerical behavior of the generated code via software-in-the-loop (SIL) testing on NVIDIA GPUs.

GPU Coder also supports embedded NVIDIA Tegra® platforms such as the NVIDIA Drive PX2 Jetson® TK1, Jetson TX1, Jetson TX2, Jetson Xavier, and Jetson Nano developer kits.


Related Hardware Support Views: Aerospace and Defense, Automotive, Consumer Electronics, C ∕ C++ Code Generation, Embedded Systems, GPU Coder, Image Processing and Computer Vision, MathWorks Supported, MATLAB Product Family, NVIDIA, Signal Processing