After you develop your application using Wavelet Toolbox™, you can generate optimized CUDA code for NVIDIA® GPUs from MATLAB code. The code can be integrated into your project as source code, static libraries, or dynamic libraries, and can be used for prototyping on GPUs. You can use the generated CUDA within MATLAB to accelerate computationally intensive portions of your MATLAB code in machine learning, deep learning, or other applications. You must have MATLAB Coder™ and GPU Coder™ to generate CUDA code.
|Single-level 1-D discrete wavelet transform|
|Single-level inverse discrete 1-D wavelet transform|
|Single-level discrete 2-D wavelet transform|
|Single-level inverse discrete 2-D wavelet transform|
|Maximal overlap discrete wavelet transform|
|Inverse maximal overlap discrete wavelet transform|
|Multiresolution analysis based on MODWT|
|Multisignal 1-D wavelet decomposition|
|1-D wavelet decomposition|
|2-D wavelet decomposition|
Code Generation by Using the GPU Coder App (GPU Coder)
Generate CUDA C code from MATLAB code by using the GPU Coder app.
Getting Started with the GPU Coder Support Package for NVIDIA GPUs (GPU Coder Support Package for NVIDIA GPUs)
This example shows how to use the GPU Coder™ Support Package for NVIDIA GPUs and connect to NVIDIA® DRIVE™ and Jetson hardware platforms, perform basic operations, generate CUDA® executable from a MATLAB® function, and run the executable on the hardware.