Hey Ross,
Thanks for the reply.
Yup, I was aware that 1 ns simulation is certainly not enough to
obtain a satisfactory
sampling and convergence.
But I was worried because the GPU runs produced practically the same results
which, compared with the CPU run, they just appear somehow "scaled":
if you look at the graphs I attached in the previous email the trend of the RMSF
is in fact the same, but let's say enhanced in the case of the CPU run.
By the way, it might depends on, as you say, how the GPU and CPU codes handle
the random number seed, which I changed for the production runs by setting
ig=-1 (as recommended in Amber tutorials). Here is my dyn.in input file though:
dynamics complex
&cntrl
imin = 0, irest = 1, ntx = 5,
nstlim = 500000, dt = 0.002,
ntc = 2, ntf = 2,
cut = 10.0, ntb = 1,
ntpr = 1000, ntwx = 1000,
ntt = 3, gamma_ln = 2.0,
temp0 = 300.0,
ig = -1,
iwrap = 1
/
The version I am using is Amber11 (the one you helped me to install
here in Edinburgh :-) ) with all the applied bugfixes described in Jason Swails
Wiki page:
http://jswails.wikidot.com/installing-amber11-and-ambertools-1-5
that is bugfixes till number 20.
Anyway I'll try to run the simulations with different random seed and
different number of
CPUs too.
Thanks again.
Max
2012/7/6 Ross Walker <ross.rosswalker.co.uk>:
> Hi Massimiliano,
>
>> I was doing some benchmarks on my system using the
>> GPU boards I have and the 16 cores CPU of my machine.
>>
>> After minimising and equilibrating the system, I ran four 1ns
>> simulation using:
>>
>> - 16 cores of my CPU
>> - 1 Tesla C2070
>> - 2 Teslas C2070
>> - 1 GeForce GTX 470
>
> These results are almost certainly NOT converged. When comparing CPU and GPU
> results we have typically run around 50 simulations, each of 100ns and even
> then the RMSFs are not fully converged. The GPU runs will all be the same
> since that code is deterministic. You don't give your input settings so it
> is hard to comment more but for example if you are using NTT=3 the random
> number generator on the CPU is different from that used on the 3 GPU runs
> which would account for the differences in such short runs.
>
> I would suggest repeating the GPU runs with different random number seeds
> and the same for the 16 cores of the CPU (try 8 cores and 1 core as well for
> comparison). I'm betting you'll see differences between individual runs that
> are larger than the differences you see here which will give you an idea of
> how unconverged the results are.
>
>> Then, after centering (ptraj center) and re-imaging (ptraj image) and
>> aligning (ptraj rms)
>> the produced trajectories I have worked out the RMSF and I obtained a
>> substantial
>> difference between the 16cores CPU trajectory and the GPU ones
>> (RMSFs obtained from the 3 different GPU runs are practically the
>> same).
>
> Also what version of the code are you using, which patch level in terms of
> bugfixes etc.
>
>> Any idea on which is the correct one?
>
> I suspect all of them are correct, and equally incorrect.
>
> All the best
> Ross
>
> /\
> \/
> |\oss Walker
>
> ---------------------------------------------------------
> | Assistant Research Professor |
> | San Diego Supercomputer Center |
> | Adjunct Assistant Professor |
> | Dept. of Chemistry and Biochemistry |
> | University of California San Diego |
> | NVIDIA Fellow |
> | http://www.rosswalker.co.uk | http://www.wmd-lab.org/ |
> | Tel: +1 858 822 0854 | EMail:- ross.rosswalker.co.uk |
> ---------------------------------------------------------
>
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--
Dr Massimiliano Porrini
Institute for Condensed Matter and Complex Systems
School of Physics & Astronomy
The University of Edinburgh
James Clerk Maxwell Building
The King's Buildings
Mayfield Road
Edinburgh EH9 3JZ
Tel +44-(0)131-650-5229
E-mails : M.Porrini.ed.ac.uk
mozz76.gmail.com
maxp.iesl.forth.gr
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Received on Sat Jul 07 2012 - 12:00:02 PDT
