Out of memory error with 8GB and 64bit
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Hi,
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 个评论
Walter Roberson
2015-10-29
Can you switch the large arrays to single precision, so they use half as much memory?
Alexandra
2015-10-30
So named "single" floating point values take up exactly half the memory of "double" floating point values, but have a lower precision, exactly as the documentation states "Because MATLAB stores numbers of type single using 32 bits, they require less memory than numbers of type double, which use 64 bits. However, because they are stored with fewer bits, numbers of type single are represented to less precision than numbers of type double."
Converting to single would be an easy way to use less memory, if the precision requirement of your algorithm and data permit this.
Alexandra
2015-10-30
采纳的回答
Lessmann
2015-10-30
Hi,
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 个评论
Alexandra
2015-10-30
Lessmann
2015-10-30
I didn't meant to break down the script but the data. Sometimes it is possible divide the data into chunks, process them independendly of each other and combine the results afterwards.
For example, process 50 times a 1000x50000 matrix.
But if such an approach is feasible depents on the problem at hand.
Alexandra
2015-10-30
Walter Roberson
2015-10-30
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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