[Numpy-discussion] How to limit the numpy.memmap's RAM usage?
Charles R Harris
Sat Oct 23 12:39:42 CDT 2010
On Sat, Oct 23, 2010 at 10:27 AM, braingateway <email@example.com>wrote:
> Charles R Harris :
> > On Sat, Oct 23, 2010 at 10:15 AM, Charles R Harris
> > <firstname.lastname@example.org <mailto:email@example.com>> wrote:
> > On Sat, Oct 23, 2010 at 9:44 AM, braingateway
> > <firstname.lastname@example.org <mailto:email@example.com>> wrote:
> > David Cournapeau :
> > 2010/10/23 braingateway <firstname.lastname@example.org
> > <mailto:email@example.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.
> > It is also possible that something else in your program is hanging
> > onto memory but without knowing a lot more it is hard to tell. Are you
> > seeing symptoms besides the memory graphs? It looks like you aren't
> > running on windows, actually, so what OS are you running on?
> > Chuck
> > ------------------------------------------------------------------------
> Hi Chuck,
> Thanks a lot for quick response. I do run following supper simple script
> on windows:
> a = numpy.memmap(‘a.bin’, dtype='float64', mode='r')
> for i in range(0,len(a)/blocklen):
> Everything became supper slow after python ate all the RAM.
> By the way, I also tried Qt QFile::map() there is no problem at all...
Hmm. Nothing looks suspicious. For reference, can you be specific about the
OS/version, python version, and numpy version?
What happens if you simply do
for i in range(0,len(a)/blocklen):
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