MATLAB: Gpu support single sparse matrix

gpu

I can't create a single-precision sparse matrix on the GPU. Does version 2017a allow that yet?

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You can't create a single-precision sparse matrix on the CPU either, so the answer is no, this is not supported. Do you have an application in mind?

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MATLAB: Training NN with single precision data on GPU

Hi,

The single precision GPU training can only be done in the ‘nnGPU’ calculation mode. By default train uses ‘nnGPUOp’ which doesn’t support single precision GPU Training.

As a workaround, you may do single precision GPU training by any of the two ways mentioned below:

You can use the nndata2gpu function:

% Here x,t are original double precision data 
net = configure(net,x,t); 
sx = nndata2gpu(x,'single'); 
st = nndata2gpu(t,'single'); 
[net,tr] = train(net,sx,st,'useGPU','yes');

You can specify single precision GPU training:

 % Here x,t are single precision data 
[net,tr] = train(net,x,t,nnGPU('precision','single'));

Hope it helps.

MATLAB: Does MATLAB even have single precision variables

I'm not quite sure what you are asking really.

I am starting to use 'single' more and more often nowadays because we work with very large datasets which, while stored in file as integers, have to be converted to floating point in Matlab to do maths, but since a double takes twice the memory of a single this is an excessive use of memory quite often.

The extra precision that a double gives is rarely relevant and especially given that at the end of whatever calculation I am doing the final result will be converted back to integer anyway with the requisite loss of accuracy.

Propagation of the data type from the start through to its results is not exactly uncommon though. In C++ you generally have to cast to a different data type (or accept warnings in your code) if you want the result in a different type to the original variable.

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Related Solutions

MATLAB: Training NN with single precision data on GPU

MATLAB: Does MATLAB even have single precision variables

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