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accelerate your code by running it on a gpu

to speed up your code, first try profiling and vectorizing it. for information, see . after profiling and vectorizing, you can also try using your computer’s gpu to speed up your calculations. if all the functions that you want to use are supported on the gpu, you can simply use gpuarray to transfer input data to the gpu, and call gather to retrieve the output data from the gpu. to get started with gpu computing, see run matlab functions on a gpu.

for deep learning, matlab® provides automatic parallel support for multiple gpus. see (deep learning toolbox).

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  • accelerate your code using basic gpu computing

  • further accelerate your code using advanced gpu cuda and mex programming
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