How to make Matlab use Nvidia???

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Safia 2017-11-1

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https://ww2.mathworks.cn/matlabcentral/answers/364555-how-to-make-matlab-use-nvidia

评论： Walter Roberson 2017-11-3

My computer has a CPU and a Nvidia Quadro. I tried script from "transfer learning with AlexNet" and it took me 1 h to finish it. And as the script was running it said that it was running on a single CPU. How can I make sure that Matlab uses the Nvidia that is installed on my computer????

I would really appreciate the help

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Answer 1

Walter Roberson 2017-11-1

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https://ww2.mathworks.cn/matlabcentral/answers/364555-how-to-make-matlab-use-nvidia#answer_288915

https://www.mathworks.com/help/nnet/deep-learning-basics.html

"You can train a convolutional neural network on either a CPU, a GPU, or multiple GPUs and/or in parallel. Training on a GPU or in parallel requires the Parallel Computing Toolbox™. Using a GPU requires a CUDA® enabled NVIDIA® GPU with compute capability 3.0 or higher. Specify the training parameters including the execution environment using the trainingOptions function."

Some models of Quadro have compute capacity as low as 1.0; you did not happen to mention which one you use. You need to have the Parallel Computing toolbox, And you have to request that the GPU be used.

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Walter Roberson 2017-11-2

The GTX 1060 card takes 120 Watts by itself and it is recommended that the system have a 400 W power supply. My suspicion would be that your current system might not be up to that.

The specs you list for your computer suggests it was built around 2010. An upgrade would be technically recommended -- but the costs would not be trivial.

https://www.nvidia.com/en-us/geforce/products/10series/geforce-gtx-1060/

Walter Roberson 2017-11-3

Note that if you were doing a lot of this kind of work and if you cannot use single precision, and if you have enough money, then instead of the GTX 1060 you might want to get one of NVidia's Deep Learning Workstations, or equivalent system. The

There is an interesting analysis at http://timdettmers.com/2017/04/09/which-gpu-for-deep-learning/

Use of double precision is common in deep learning, but some systems can be trained well with single precision. Double precision speeds do not scale the way you would think they should: there is effectively double precision done in software at about 1/32 of single precision floating point speed, to which specialized double precision processors can be added in the design that might take you up to 1/8 of single precision floating point speed or even 1/3 of single precision floating point speed for the real $$$$ top end devices.

https://www.microway.com/knowledge-center-articles/comparison-of-nvidia-geforce-gpus-and-nvidia-tesla-gpus/

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