[Numpy-discussion] How to limit the numpy.memmap's RAM usage?

Charles R Harris charlesr.harris@gmail....
Sat Oct 23 11:15:37 CDT 2010

On Sat, Oct 23, 2010 at 9:44 AM, braingateway <braingateway@gmail.com>wrote:

> David Cournapeau :
>  2010/10/23 braingateway <braingateway@gmail.com>:
>>> Hi everyone,
>>> I noticed the numpy.memmap using RAM to buffer data from memmap files.
>>> If I get a 100GB array in a memmap file and process it block by block,
>>> the RAM usage is going to increasing with the process running until
>>> there is no available space in RAM (4GB), even though the block size is
>>> only 1MB.
>>> for example:
>>> ####
>>> a = numpy.memmap(‘a.bin’, dtype='float64', mode='r')
>>> blocklen=1e5
>>> b=npy.zeros((len(a)/blocklen,))
>>> for i in range(0,len(a)/blocklen):
>>> b[i]=npy.mean(a[i*blocklen:(i+1)*blocklen])
>>> ####
>>> Is there any way to restrict the memory usage in numpy.memmap?
>> The whole point of using memmap is to let the OS do the buffering for
>> you (which is likely to do a better job than you in many cases). Which
>> OS are you using ? And how do you measure how much memory is taken by
>> numpy for your array ?
>> David
>> _______________________________________________
> Hi David,
> I agree with you about the point of using memmap. That is why the behavior
> is so strange to me.
> I actually measure the size of resident set (pink trace in figure2) of the
> python process on Windows. Here I attached the  result. You can see the  RAM
>  usage is definitely not file system cache.
Umm, a good operating system will use *all* of ram for buffering because ram
is fast and it assumes you are likely to reuse data you have already used
once. If it needs some memory for something else it just writes a page to
disk, if dirty, and reads in the new data from disk and changes the address
of the page. Where you get into trouble is if pages can't be evicted for
some reason. Most modern OS's also have special options available for
reading in streaming data from disk that can lead to significantly faster
access for that sort of thing, but I don't think you can do that with
memmapped files.

I'm not sure how windows labels it's memory. IIRC, Memmaping a file leads to
what is called file backed memory, it is essentially virtual memory. Now, I
won't bet my life that there isn't a problem, but I think a misunderstanding
of the memory information is more likely.

-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://mail.scipy.org/pipermail/numpy-discussion/attachments/20101023/d845650f/attachment.html 

More information about the NumPy-Discussion mailing list