memory management

Sudheer Joseph sjo.india at gmail.com
Tue Feb 19 02:13:32 CET 2013


> Python version and OS please.  And is the Python 32bit or 64bit?  How 
> 
> much RAM does the computer have, and how big are the swapfiles ?
> 
Python 2.7.3
ubuntu 12.04 64 bit
4GB RAM
> 
> "Fairly big" is fairly vague.  To some people, a list with 100k members 
> 
> is huge, but not to a modern computer.
I have a data loaded to memory from netcdf file which is 2091*140*180 grid points (2091 time, 140 latitude 180 longitude) apart from this I define a 2 3d arrays r3d and lags3d to store the output for writing out to netcdf file after completion. 
> 
> 
> How have you checked whether it's running out of memory?  Have you run 
> 
> 'top' on it?  Or is that just a guess?

I have not done this but the speed (assessed from the listing of grid i and j) get stopped after j=6 ie after running 6 longitude grids)
>
Will check the top as you suggested

Here is the result of top it used about 3gB memory

  PID USER      PR  NI  VIRT  RES  SHR S %CPU %MEM    TIME+  COMMAND            
 3069 sjo       20   0 3636m 3.0g 2504 D    3 78.7   3:07.44 python  
> 
> I haven't used numpy, scipy, nor matplotlib, and it's been a long time 
> 
> since I did correlations.  But are you sure you're not just implementing 
> 
> an O(n**3) algorithm or something, and it's just extremely slow?
> 
Correlation do not involve such computation normally, I am not sure if internally python does some thing like that.
with best regards,
Sudheer
> 
> 
> 
> > from mpl_toolkits.basemap import Basemap as bm, shiftgrid, cm
> 
> > import numpy as np
> 
> > import matplotlib.pyplot as plt
> 
> > from netCDF4 import Dataset
> 
> > from math import pow, sqrt
> 
> > import sys
> 
> > from scipy.stats import t
> 
> 
> 
>   <snip>
> 
> 
> 
> -- 
> 
> DaveA



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