The generated code includes CUDA kernels for parallelizable parts of your deep learning, embedded vision, and radar and signal processing algorithms. For high performance, the generated code can call NVIDIA® TensorRT®. You can integrate the generated CUDA into your project as source code or static/dynamic libraries and compile it for modern NVIDIA GPUs, including those embedded on NVIDIA Jetson™ and NVIDIA DRIVE™ platforms. You can access peripherals on the Jetson and DRIVE platforms and incorporate manually written CUDA into the generated code.

GPU Coder enables you to profile the generated CUDA to identify bottlenecks and opportunities for performance optimization (with Embedded Coder). Bidirectional links let you trace between MATLAB code and generated CUDA. You can verify the numerical behavior of the generated code via software-in-the-loop (SIL) and processor-in-the-loop (PIL) testing.