[R] 8 fast or 4 very fast cores?
clint at ecy.wa.gov
Mon Sep 15 18:21:29 CEST 2014
I'm in a similar situation and am looking seriously at a pair of E5-2643v3
(6 cores each-hyperthreaded).
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On Mon, 15 Sep 2014, Prof Brian Ripley wrote:
> On 15/09/2014 11:21, Ben Bolker wrote:
>> Leif Ruckman <Leif <at> Ruckman.se> writes:
>> > I am going to buy a new computer ( Dell workstation T5810 - Windows 8)
>> > to work with simulatons in R.
>> > Now I am asked what kind of processor I like and I was given two
>> > choices.
>> > 1. Intel Xeon E5-1620 v3 - 4 cores 3.7 GHz Turbo
>> > 2. Intel Xeon E5-2640 v3 - 8 cores 2.6 GHz Turbo
>> > I don't know what is better in simulations studies in R, a few very fast
>> > cores or many cores at normal speed.
>> It's **very** hard to answer such general questions reliably, but I'll
>> take a guess and say that if you're doing simulation studies you're likely
>> to be doing tasks that are easily distributable (e.g. many random
>> realizations of the same simulation and/or realizations for many
>> different sets of parameter values) and so the more-cores option
>> will be a good idea.
>> But it's possible that what you mean by "simulation studies" is
>> If you can do some benchmarking of your problems on an existing
>> machine that would probably be a good idea.
> Unfortunately unless it is of very similar architecture that may not help
> Three issues hard to scale from are the 'Turbo', the hyperthreading of modern
> Xeons and the cache sizes. Now, I happen to have machines with multiple
> E5-24x0 and E5-26x0 Xeons: both do hyperthreading well, so you would have 8
> or 16 virtual CPUs and they will give you say 50% increase in throughput if
> all the virtual cores are used. But you cannot scale up from using just one
> process on one core.
> I find it hard to think of tasks where option 1) would have more throughput,
> but if most of the time you are not running things in parallel then the
> higher speed on a single task is a consideration.
>> Ben Bolker
>> R-help at r-project.org mailing list
>> PLEASE do read the posting guide
>> and provide commented, minimal, self-contained, reproducible code.
> Brian D. Ripley, ripley at stats.ox.ac.uk
> Emeritus Professor of Applied Statistics, University of Oxford
> 1 South Parks Road, Oxford OX1 3TG, UK
> R-help at r-project.org mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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