[Numpy-discussion] np.memmap and memory usage

[Pauli Virtanen]

Wed, 01 Jul 2009 10:17:51 +0200, Emmanuelle Gouillart kirjoitti:
> 	I'm using numpy.memmap to open big 3-D arrays of Xray tomography
> data. After I have created a new array using memmap, I modify the
> contrast of every Z-slice (along the first dimension) inside a for loop,
> for a better visualization of the data. Although I call memmap.flush
> after each modification of a Z-slice, the memory used by Ipython keeps
> increasing at every new iteration. At the end of the loop, the memory
> used by Ipython is of the order of magnitude of the size of the data
> file (1.8Go !). I would have expected that the maximum amount of memory
> used would corresponde to only one Z-slice of the 3D array. See the code
> snapshots below for more details.
> 
> 	Is this an expected behaviour? How can I reduce the amount of
> memory used by Ipython and still process my data?

How do you measure the memory used? Note that on Linux, "top" includes 
the size of OS caches for the memmap in the RSS size of a process.
You can try to monitor "free" instead:

$ free
             total       used       free     shared    buffers     cached
Mem:      12300488   11485664     814824          0     642928    7960736
-/+ buffers/cache:    2882000    9418488
Swap:      7847712       2428    7845284

If the memory is used by OS caches, the "used" number on the "-/+ buffers/
cache" line should stay constant while the program runs.

In this case, what is most likely actually taking up memory is the OS 
buffering the data in memory, before writing it to disk. Linux has at 
least some system-wide parameters available that tune the aggressiveness 
of data cachine. I suppose there may also be some file-specific settings, 
but I have no idea what they are.

-- 
Pauli Virtanen