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GPU Code Generation

Generate CUDA® code from MATLAB®

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.

Functions

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cwtContinuous 1-D wavelet transform
dwtSingle-level 1-D discrete wavelet transform
idwtSingle-level inverse discrete 1-D wavelet transform
dwt2Single-level discrete 2-D wavelet transform
idwt2Single-level inverse discrete 2-D wavelet transform
modwtMaximal overlap discrete wavelet transform
imodwtInverse maximal overlap discrete wavelet transform
modwtmraMultiresolution analysis based on MODWT
mdwtdecMultisignal 1-D wavelet decomposition
wavedec1-D wavelet decomposition
wavedec22-D wavelet decomposition
convConvolution and polynomial multiplication
conv22-D convolution
fftFast Fourier transform
fft22-D fast Fourier transform

Topics

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.

Featured Examples