[Rd] Q: R 2.2.1: Memory Management Issues?

[Karen.Green@sanofi-aventis.com]

Dear Simon,

Thank you for taking time to address my questions.

>> summary(EMclust(y),y)
>
>I suspect that memory is your least problem. Did you even try to run  
>EMclust on a small subsample? I suspect that if you did, you would  
>figure out that what you are trying to do is not likely to terminate  
>within days...

The empirically derived limit on my machine (under R 1.9.1) was approximately 7500 data points.
I have been able to successfully run the script that uses package MCLUST on several hundred smaller data sets.

I even had written a work-around for the case of greater than 9600 data points.  My work-around first orders the
points by their value then takes a sample (e.g. every other point or 1 point every n points) in order to bring the number under 9600.  No problems with the computations were observed, but you are correct that a deconvolution on that larger dataset of 9600 takes almost 30 minutes.  However, for our purposes, we do not have many datasets over 9600 so the time is not a major constraint.

Unfortunately, my management does not like using a work-around and really wants to operate on the larger data sets.
I was told to find a way to make it operate on the larger data sets or avoid using R and find another solution.  

>From previous programming projects in a different scientific field long ago, I recall making a trade-off of using temp files instead of holding data in memory in order to make working with larger data sets possible.  I am wondering if something like that would be possible for this situation, but I don't have enough knowledge at this moment to make this decision.

Karen
---
Karen M. Green, Ph.D.
Karen.Green at sanofi-aventis.com
Research Investigator
Drug Design Group
Sanofi Aventis Pharmaceuticals
Tucson, AZ  85737

-----Original Message-----
From: Simon Urbanek [mailto:simon.urbanek at r-project.org]
Sent: Thursday, January 05, 2006 5:13 PM
To: Green, Karen M. PH/US
Cc: R-devel at stat.math.ethz.ch
Subject: Re: [Rd] Q: R 2.2.1: Memory Management Issues?
Importance: High


Karen,

On Jan 5, 2006, at 5:18 PM, <Karen.Green at sanofi-aventis.com>  
<Karen.Green at sanofi-aventis.com> wrote:

> I am trying to run a R script which makes use of the MCLUST package.
> The script can successfully read in the approximately 17000 data  
> points ok, but then throws an error:
> --------------------------------------------------------
> Error:  cannot allocate vector of size 1115070Kb

This is 1.1GB of RAM to allocate alone for one vector(!). As you  
stated yourself the total upper limit is 2GB, so you cannot even fit  
two of those in memory anyway - not much you can do with it even if  
it is allocated.

> summary(EMclust(y),y)

I suspect that memory is your least problem. Did you even try to run  
EMclust on a small subsample? I suspect that if you did, you would  
figure out that what you are trying to do is not likely to terminate  
within days...

> (1) I had initially thought that Windows 2000 should be able to  
> allocate up to about 2 GB memory.  So, why is there a problem to  
> allocate a little over 1GB on a defragmented disk with over 15 GB  
> free?  (Is this a pagefile size issue?)

Because that is not the only 1GB vector that is allocated. Your "15GB/ 
defragmented" are irrelevant - if at all, look how much virtual  
memory is set up in you system's preferences.

> (2) Do you think the origin of the problem is
>     (a) the R environment, or
>     (b) the function in the MCLUST package using an in-memory  
> instead of an on-disk approach?

Well, a toy example of 17000x2 needs 2.3GB and it's unlikely to  
terminate anytime soon, so I'd rather call it shooting with the wrong  
gun. Maybe you should consider different approach to your problem -  
possibly ask at the BioConductor list, because people there have more  
experience with large data and this is not really a technical  
question about R, but rather how to apply statistical methods.

> (3)
>     (a) If the problem originates in the R environment, would  
> switching to the Linux version of R solve the problem?

Any reasonable unix will do - technically (64-bit versions  
preferably, but in your case even 32-bit would do). Again, I don't  
think memory is your only problem here, though.

Cheers,
Simon