[R] Linux Standalone Server Suggestions for R

bogdan romocea br44114 at gmail.com
Thu Sep 1 20:54:10 CEST 2005


Most powerful in what way? Quite a lot depends on the jobs you're going to run.
	- To run CPU-bound jobs, more CPUs is better. (Even though R doesn't
do threading, you can manually split some CPU-bound jobs in several
parts and run them simultaneously.) Apart from multiple CPUs and
hyperthreading, check the new dual-core CPUs.
	- To run very large jobs, more memory is better. You can easily spend
most of your money on memory. Get the fastest one.
	- You should get 64-bit CPUs, otherwise you won't be able to run very
large jobs (search the list for details).

I would suggest that you buy a configuration that can handle more CPUs
and memory than you think you need now (say, at least 4 max CPUs and
16 GB max memory), then keep on adding more memory and CPUs as your
needs change.
hth,
b.


> -----Original Message-----
> From: Jia-Shing So [mailto:jiso at ucsd.edu] 
> Sent: Wednesday, August 31, 2005 10:03 PM
> To: r-help at stat.math.ethz.ch
> Cc: Phuoc Hong
> Subject: [R] Linux Standalone Server Suggestions for R
> 
> 
> Hi All,
> 
> My group is  looking for any suggestions on what to purchase to  
> achieve the most powerful number crunching system that $50k 
> can buy.   
> The main application that will be used is R so input on what 
> hardware  
> benefits R most will be appreciated.  The requirements are 
> that it be  
> a single standalone server (i.e. not a cluster solution), and 
> it that  
> must be able to run unix/linux.  If anyone has any experience/ 
> suggestions regarding the following questions that would also be  
> greatly appreciated.
> 
> AMD vs Intel chips, especially 64-bit versions of the two?
> Using Itanium/Opterons and if so how much of a performance boost did  
> you achieve vs other 64-bit chip sets?
> Also, does anyone know if there is an upper thresh hold on much  
> memory R can use?
> 
> Thanks in advance for any help and suggestions,
> 
> Jia-Shing So
> Programmer Analyst
> Biostatistics and Bioinformatics Lab
> University of California, San Diego
> 
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