[R] memory managment under Windows XP

[Prof Brian Ripley]

On Thu, 23 Feb 2006, roger bos wrote:

> And of course using rm(...) to clean up objects you no longer need.  No
> amount of physical memory can save you from grossly inefficient code and
> large memory leaks.  For example, lets say I have a large testMat object
> that I use time period.  I loop though each month using for loops.  Even
> though the object has the same name each month, and thus gets overwritten,
> the memory management seems to go better by manually removing the object at
> the end of each loop.  Also, I sometimes call gc() at the end of each loop,
> but I don't know if that actually helps or not.  I figure it can't hurt.

It does help to rm() and then gc() at a point when you know that the 
number of objects in use is minimal.  R will gc() repeatedly when it 
starts to run out of address space, but this does not help if the address 
space is already fragmented.

The main problem on 32-bit OSes is (virtual) memory fragmentation, and 3Gb 
is not really much address space for objects in 100s of Mb.

I've now only got a 64-bit desktop and servers (plus a 32-bit Windows 
laptop).  It is a shame for Windows users that a 64-bit Open Source 
toolchain* is nowhere in sight, but I suspect a sufficiently determined 
user of Win64 could build a 64-bit port of R with commercial compilers.

*You need a compiler, assembler, linker and runtime, and the latter may 
well be the most problematic.

> On 2/23/06, Liaw, Andy <andy_liaw at merck.com> wrote:
>>
>> There are plenty in the the list archive, one of which is to switch to a
>> run
>> 64-bit R on a 64-bit platform with lots of physical RAM.  Such hardware is
>> quite affordable these days (certainly cheaper than most commercial
>> software
>> that you'd have to buy if you didn't have R).
>>
>> Andy
>>
>> From: r user
>>>
>>> I am using R 2.2.1 in a Windowes XP environment.
>>>
>>> I work with very large datasets, and occassionally run
>>> out of memory.
>>>
>>> I have modified my boot.ini file to use the "/3gb
>>> switch".
>>>
>>> I also run the following line after I launch R ( I am
>>> unsure if it is helpful).
>>>
>>> "memory.limit(size = 4095)"
>>>
>>> Please point me to useful references on how to better
>>> manage memory, or suggestother actions.
>>>
>>> ______________________________________________
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>>>
>>
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-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595