AW: Question about GPUs hardware.

From: Norman Geist (
Date: Tue Apr 24 2012 - 01:11:01 CDT

>I'm no expert, but I'll give you my two cents.
>1) For NAMD, I've found the GTX cards run pretty well (GTX570 performs at
the same speed as an M2070), but you need to consider the expected size of
your >simulated systems. I think there is data on how much memory an X-atom
system will take up for Implicit solvent, or explicit NTP with PME. You can
get >GTX580s with 3GB of RAM, but if your system is larger (I think 3GB will
allow up to ~500k atoms NTP/PME) you'll have to go with the expensive
Tesla's >(6GB). Also, I would not purchase GTX 600 series, they are
supremely meagre at doing double-precision, so no good for MD (unless maybe
you are doing >single precision GROMACS? never tried).


Even with 1,4 million atoms, I only occupy about 10% of my 3GB (Tesla C2050)
with NAMD as it doesn't store the whole system there. That's different from
acemd and pmemd, where the gpu memory size can be a limitation factor, as
the whole system is computed on the gpu, what makes the simulations also
faster. But that's just by the way. The bottom line is that 3GB per GPU with
NAMD is by far ok.

>2) If you do go with the GTX, since NAMD requires a transfer of data from
the GPU every step (not everything is computed on the GPU), you'll want to
make >sure you have full 16x PCI-E for each card, otherwise you're
performance will plummet when using multiple cards. So good motherboard is
probably >something to consider.


I agree

>3) Remember that the Tesla cards don't have fans like the GTX ones, so you
really need to make sure you have a good system for heat exchange. On this
note, >if you do go with the GTX 580s, I believe there are 3GB cards that
have built in water cooled systems. They cost maybe $50-100 more than the
fan cooled 3GB >ones, but could be worth considering if you are concerned
about heat (on the downside, due to the data transfer every step,
overclocking them probably >won't lead to much of a performance spike
compared to AMBER or GROMACS). Overall though, NAMD doesn't generate as
much GPU heat as GROMACS or >AMBER (due to the slowdown of waiting for data
transfer every step), what I've found with a GTX 570 with the fan running at
65% is that NAMD his ~53 >degrees (23 degree ambient temp in the room),
whereas AMBER or GROMACS can pass 70 degrees.


That's not completely true. Only the M-series don't have fans, the C-series
do have fans. But Aron is right, cooling is important, but if your whole
office is cooled, a non-passive cooled machine will be fine (and possibly

>I guess if it was me with $40k, I'd build a larger number of multi-GTX 580
boxes. The guys who make OpenMM (the thing that enables GPU acceleration in
>GROMACS) have a nice little binary for doing memory tests on GPU memory.
Since the GTX cards sometimes have memory issues, I'd suggest testing all of
>them thoroughly when you first get them, no point in having weeks of
troubleshooting afterwards all because of some faulty memory.


True, or go with the Tesla that support ECC, unfortunately that's no
guarantee to indicate a broken gpu. Overall the Tesla's are better to
administrate and provide better warranty as they are build for computing.
But they are expensive and don't come with a great performance advantage as
the special features provided are not used by NAMD.

>I've found that a GTX 570/Tesla M2070 is about the equivalent of 48-96
CPUs, depending on the system (the GPU improvement is better for larger
systems, >and particularly for implicit solvent, though maybe less in NAMD
on this last point). For smaller systems (<10k atoms) the GPU is really not
the best option, and >obviously as mentioned earlier, if the system exceeds
the available GPU memory, it's useless. I know Axel has responded on
several emails recommending >multiple cheaper AMD boxes (I think the 10-core
ones???), but I think if you're expected systems fall into a good size range
for the GPU it's cheaper.


Of course a bigger problem always scales better. You have to remember that
you have some 100 compute cores on the gpu, that must be utilized. A too
small system cannot do that. Also, remember Amdahls Law and the serial part
of an application. A system cannot be accelerated to infinity due


Also, if you plan to buy multiple machines and want to run them in parallel,
go with Infiniband as 1Gb/s Ethernet is insufficient.


>>On Mon, Apr 23, 2012 at 6:32 PM, Cesar Millan <
<>> wrote:

>>Hi everyone,
>>I hope not to be repetitive with this question.
>>Our group wants to buy some workstations with GPUs to do MD simulations of
>>proteins (generate and analyze the results). We have a discussion about
>>which GPUs (with the appropriate hardware to make the run fine) we must
>>(our budget is around 40k dllrs). We plan to use that workstations on a
>>office (around 12 m2) cooled with air conditioning .
>>One option that we have is to buy tesla cards but they are expensive, and
>>the other one is to buy GTX cards (less expensive). Unfortunately, we do
>>not have access to this GPUs to test how well our systems run on both

>Aron Broom M.Sc
>PhD Student
>Department of Chemistry
>University of Waterloo


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