Out of memory error with 8GB and 64bit

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Alexandra 2015 年 10 月 29 日
コメント済み: Walter Roberson 2015 年 10 月 30 日
I am working with lots of memory and I am getting an out of memory error.
>> memory
Maximum possible array: 16146 MB (1.693e+10 bytes) *
Memory available for all arrays: 16146 MB (1.693e+10 bytes) *
Memory used by MATLAB: 1080 MB (1.132e+09 bytes)
Physical Memory (RAM): 8111 MB (8.505e+09 bytes)
* Limited by System Memory (physical + swap file) available.
The model is quite large. It's a script with dozens of matrixes 50k*50k. I know I use intensive functions as copulas, kron function, nested for and if's.
But this model is being developed for a long time and I really need it to run. What can I do?
Am I being inefficient or am I simply asking the impossible? This is a 64bit version.
Thanks a lot,
  4 件のコメント
Alexandra 2015 年 10 月 30 日
Ok. I see the difference is in the number of decimals ("32-bit single-precision variables represent data to about seven decimal places, which is less accurate than doubles"). Since I work with €, I have no interest in computing bellow cents and I will try single.



Lessmann 2015 年 10 月 30 日
considering the mentioned size of a matrix, the matrix would occupy ~20GB or ~10GB in the case of single precision. With the 16GB available memory you are asking the impossible.
>> M = zeros(50000,50000);
>> whos
Name Size Bytes Class Attributes
M 50000x50000 20000000000 double
To the point what you cando, take a good look at your script and see if the problem can be broken down into smaller pieces (not processing all the data at once).
If the matrix is sparsely populated, you could have a look into sparse matrices.
  4 件のコメント
Walter Roberson
Walter Roberson 2015 年 10 月 30 日
sum and average do not require having all of the data available at the same time.
Consider that sum([1 2 3 4]) is 1+2+3+4 which is the same as (1+2)+(3+4) which is sum([1 2])+sum([3 4]). Therefore you can break your data up into smaller blocks, do the sum of each block, and then add the subtotals.
Likewise, average is sum divided by the number of elements, so you can break the data up into blocks, do the sum of each block, add the subtotals, and then divide the total by the number of elements. Also, in the special case that all the blocks are the same size, you can mean() the mean()'s of the blocks (a technique that will not work if any block is a different size than the others.)


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