How could MATLAB store large scale of data.

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C Zeng 2013-3-17

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Hi, All,

I have to use a dataset as large as ones(3^N1,3^N2,10^3), where N1 and N2 can be 7~14. However, matlab will report "Out of memory" even for ones(3^14,1000). Is there a way to let matlab store such a large array.

I can split my data to several parts and store the sub-data. However that is very inconvenient.

I wonder if MATLAB can extend the limit, and handle large scale data. Thanks.

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Answer 1

Walter Roberson 2013-3-17

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编辑：Walter Roberson 2013-3-17

在 MATLAB Online 中打开

If you are able to use 8 bits per value, you could use

ones(3^N1, 3^N2, 10^3, 'uint8') %or 'int8' as appropriate.

That would occupy 4.45 gigabytes, which would be too much for any 32 bit version of MATLAB (32 bits is 4 gigabytes of address space.) It would, however, be feasible with a 64 bit MATLAB with as little as 8 gigabytes of RAM.

If you need to use double precision, then that occupies 8 bytes per location, and so requires 35.64 gigabytes for the array. You would need a 64 bit MATLAB for sure, and you would need probably 40 or so gigabytes of RAM. If that much RAM is not feasible for you, then using a double precision array of that size is not feasible for you.

This is not a matter of how MATLAB uses its memory: this is just a matter of having enough memory to store the array at all.

Note: your options are different if the array is reasonably sparse.

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Cedric 2013-3-19

编辑：Cedric 2013-3-19

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But be aware that uint8 can store only 8 bits unsigned integers, which means integers in the range 0-255. It might not be adapted to the nature of your data, which might lead to e.g. truncation..

 >> a = uint8(200)
 a =
    200
 >> 2*a                   % Will it be 400?
 ans =
    255

I can say by experience that often, when people would need much more RAM than what is present on an average computer to perform their computation, the problem is not that computers don't have enough RAM, but that the approach is flawed.

C Zeng 2013-3-19

Yes, Cedric, will be careful on that. Yes, uint8 is very limited range, and I probably use uint16. However that will exceed the 10G limit.

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Answer 2

Cedric 2013-3-17

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编辑：Cedric 2013-3-17

If your data is sparse, you can build 1000 sparse matrices or use some FEX function that will allow you to create ND sparse matrices.

The ones(3^14, 1000) that you tried to evaluate would take more than 38GB RAM to be stored as double. It certainly indicates that your approach is not appropriate. One thing that you could do though, is to store your data using a "smaller" type/class, i.e. any decent enough type (for the nature of your data) that is stored on 1, 2, or 4 bytes. If your data were made of unsigned integers in the range 0-255, you could use uint8 that would require 1 byte per element instead of 8.