From: Axel Kohlmeyer (akohlmey_at_gmail.com)
Date: Thu Apr 14 2011 - 14:57:16 CDT
On Thu, Apr 14, 2011 at 3:48 PM, Gianluca Interlandi
> I wonder which setup would be faster: a node with 4x 12-core opteron 2.3 GHz
> or a system with 4x GeForce GTX580 and a dual Quad core intel?
its going to be a close call. i would not want to go with 2.3GHz CPUs
with 48 tasks per node, you may already be bounded by memory bandwidth.
> Something like this (unselect the one TESLA and purchase separately 4
> versus (select 12-core opteron 2.3 GHz):
> On Thu, 14 Apr 2011, Axel Kohlmeyer wrote:
>> On Thu, Apr 14, 2011 at 2:58 PM, Salvador H-V <chava09hv_at_gmail.com> wrote:
>>> Hi All,
>>> If I understood well, to perform serious calculation for high performance
>>> computing, people is using GPUs cards with double precision
>>> such as tesla and quadro GPUs.
>> that is not quite right. some methods and algorithms _have_ to have
>> double precision math support, some others can make do with a combination
>> where you do part of the computation in single, the rest in double and
>> other calculations work just fine in single precision. the gromacs MD
>> code has had an all-single precision option for ages and that is how you
>> run it the fastest.
>>> I was wondering if it is possible to use the gaming series of GPUs such
>>> GeForce GTX250 and the new one GeForce GTX580 for such kind of
>>> I mean, if i use this GTX series for small systems with NAMD how
>>> are the results?
>> just as reliable as if you compute forces with SSE vectorization support.
>> in the current implementation, you should not see a difference in the
>> computation between Tesla and Geforce.
>>> Are there any benchmark biomolecular simulation results between double
>>> single precision GPUs?
>> any g200 or fermi class GPU supports (some) double precision.
>> the 20x0 tesla cards just have _more_ double precision units.
>> that only makes a difference if _all_ calculations are done in double,
>> but they usually are not, since even on Tesla, you have twice as
>> many single precision units available than double precision.
>> the differences of the tesla cards is:
>> - more memory
>> - ECC memory support (at a small performance penalty)
>> - more double precision support
>> - the cards are very tightly quality controlled
>> and warranted by nvidia. if you have a broken
>> one, you get it replaced instantly.
>> all of this is very beneficial if you set up a large cluster
>> with many gpus and a large variety of applications.
>> if you want to equip a single desktop with a couple of
>> high-end geforce GPUs and only want to use NAMD on it,
>> then you might not need the tesla features so much.
>> ECC support is a bit of a religious issue. you can have
>> errors on the geforce without noticing, but many people
>> use MD without a problem on them. if i was running
>> MD on a geforce, i would regularly run cuda_memtest
>> in order to confirm the card is still doing ok.
>>> Any help o suggestion (or point out to information), will be greatly
>>> Thanks a lot in advance,
>>> Salvador HV
>> Dr. Axel Kohlmeyer
>> akohlmey_at_gmail.com http://goo.gl/1wk0
>> Institute for Computational Molecular Science
>> Temple University, Philadelphia PA, USA.
> Gianluca Interlandi, PhD gianluca_at_u.washington.edu
> +1 (206) 685 4435
> Postdoc at the Department of Bioengineering
> at the University of Washington, Seattle WA U.S.A.
-- Dr. Axel Kohlmeyer akohlmey_at_gmail.com http://goo.gl/1wk0 Institute for Computational Molecular Science Temple University, Philadelphia PA, USA.
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