10A is recommended for membrane simulations, though, I think.
On Thu, Aug 6, 2020 at 5:02 AM David Cerutti <dscerutti.gmail.com> wrote:
> Agreed, the topology for a membrane system may be denser in terms of atoms
> that a protein in water system. If that is the case, the non-bonded
> kernels are going to run slower, but there is no way around this. If you
> are seeing the benchmarks run at about the right pace then it's your
> system. You also have a 10A cutoff in there, versus a 9A used in the
> benchmarks.
>
> Dave
>
>
> On Thu, Aug 6, 2020 at 2:52 AM Bill Ross <ross.cgl.ucsf.edu> wrote:
>
>> It might help more-informed people to mention your topology.
>>
>> AMBER Cuda is after my time, but I'll go ahead and speculate that the
>> nonbonded ops on cuda itself should be the same-ish for any N atoms, but
>> that if your topology is off the developers' beaten track, maybe there's
>> a bottleneck in collecting the data. What does nvidia-smi say about GPU
>> usage?
>>
>> Bill
>>
>> (I play cuda for tensorflow.)
>>
>>
>> On 8/5/20 11:43 PM, 李奕言 wrote:
>> > Thanks again for your kind reply.
>> >
>> > As much as I wish to get an upgrade, I am still wondering if there is
>> any solution within Amber16.
>> > The benchmark tests went well, but the actual system with similar
>> number of atoms got much slower -- Is this a general problem in Amber16
>> that can only be fixed in newer versions? Does the performance of
>> pmemd.cuda differ among systems, or does it simply depend on the atom
>> number?
>> >
>> > I would be grateful if you could help.
>> >
>> >> -----Original Messages-----
>> >> From: "David Cerutti" <dscerutti.gmail.com>
>> >> Sent Time: 2020-08-06 10:48:36 (Thursday)
>> >> To: "AMBER Mailing List" <amber.ambermd.org>
>> >> Cc:
>> >> Subject: Re: [AMBER] Reduced Performance with pmemd.cuda Compared With
>> Benchmarks.
>> >>
>> >> We made some performance improvements in Amber18 that will carry over
>> to
>> >> Amber20 once the patch hits. The sooner you can upgrade, the better,
>> but
>> >> the performance of Amber20 will probably still be better than Amber16
>> on a
>> >> GTX-1080Ti even without the patch.
>> >>
>> >> Dave
>> >>
>> >>
>> >> On Wed, Aug 5, 2020 at 9:59 PM 李奕言 <liyiyan.pku.edu.cn> wrote:
>> >>
>> >>> Thank you for the information.
>> >>> Unfortunately I was using Amber16. Does the same problem occur in
>> Amber16?
>> >>>
>> >>>
>> >>>> -----Original Messages-----
>> >>>> From: "David Cerutti" <dscerutti.gmail.com>
>> >>>> Sent Time: 2020-08-04 12:40:51 (Tuesday)
>> >>>> To: "AMBER Mailing List" <amber.ambermd.org>
>> >>>> Cc:
>> >>>> Subject: Re: [AMBER] Reduced Performance with pmemd.cuda Compared
>> With
>> >>> Benchmarks.
>> >>>> This is a known problem in Amber20, which has yet to be fixed pending
>> >>> some
>> >>>> other developments in the master branch. We know the solution, but
>> the
>> >>>> release version for now is taking precautions that will only become
>> >>>> necessary in CUDA11 and later. There is nothing wrong with the
>> code, but
>> >>>> we are still deliberating what patch to make.
>> >>>>
>> >>>> Dave
>> >>>>
>> >>>>
>> >>>> On Sun, Aug 2, 2020 at 11:22 PM 李奕言 <liyiyan.pku.edu.cn> wrote:
>> >>>>
>> >>>>> Dear all,
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> Here is a new Amber user, who has been doing MD simulations on GTX
>> >>> 1080 Ti
>> >>>>> using pmemd.cuda in Amber 16 and have encountered considerable
>> >>> reduction in
>> >>>>> performance.
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> First I ran the benchmark sets of AMBER 16 GPU acceleration. For
>> NPT
>> >>>>> simulation of Factor IX with 90,906 atoms, I have got ~89 ns/day on
>> >>> single
>> >>>>> GPU, slightly less than the benchmark performance (92.08 ns/day),
>> >>> which is
>> >>>>> acceptable. Since the benchmark results were OK, I was not
>> suspecting
>> >>> any
>> >>>>> problem with the GPU.
>> >>>>>
>> >>>>> When it came to my membrane protein system, which contains a GPCR
>> dimer
>> >>>>> protein (ffSB14), POPC lipids (lipid 14), waters (TIP3P) and ions,
>> and
>> >>> has
>> >>>>> a fairly similar atom number of 95,096, I was getting ~65 ns/day on
>> >>> single
>> >>>>> GPU. This was not what I was expecting, seeing that 95,096 was not
>> THAT
>> >>>>> larger than 90,906.
>> >>>>>
>> >>>>> My input file for the NPT simulation is as follows. I have tried to
>> >>> get
>> >>>>> as close as possible to the benchmark input.
>> >>>>>
>> >>>>> NPT Production Run
>> >>>>>
>> >>>>> &cntrl
>> >>>>> nstlim=250000000, dt=0.002, ntx=5, irest=1, ntpr=50000,
>> ntwr=50000000,
>> >>>>> ntwx=50000,
>> >>>>> temp0=300.0, ntt=1, tautp=10.0,
>> >>>>> ntb=2, ntp=1, barostat=2,
>> >>>>> ntc=2, ntf=2,
>> >>>>> ioutfm=1,
>> >>>>> &end
>> >>>>> /
>> >>>>> 1/ I have even cut down the writing of output and trajectories for
>> >>> better
>> >>>>> performance by raising ntpr and ntwx values, at the cost of
>> inadequate
>> >>>>> trajectory snapshots. Is this necessary?
>> >>>>>
>> >>>>> 2/ Also, currently I am running an aMD simulation with similar input
>> >>>>> settings, which resulted in ~42 ns/day. Is aMD performance
>> destined to
>> >>>>> drop compared to conventional MD?
>> >>>>>
>> >>>>> Production Run with Accelerated MD
>> >>>>> &cntrl
>> >>>>> nstlim=250000000, dt=0.002, ntx=5, irest=1, ntpr=1000,
>> ntwr=50000000,
>> >>>>> ntwx=1000, ntwprt=8873,
>> >>>>> temp0=300.0, ntt=1, tautp=10.0,
>> >>>>> ntb=2, ntp=1, barostat=2, cut=10.0,
>> >>>>> ntc=2, ntf=2,
>> >>>>> ioutfm=1,
>> >>>>> iamd=3,
>> >>>>> ethreshd=23916, alphad=1104,
>> >>>>> ethreshp=-187677, alphap=19011,
>> >>>>> &end
>> >>>>> /
>> >>>>>
>> >>>>> 3/ Does the performance of pmemd.cuda differ among systems, or does
>> it
>> >>>>> simply depend on the atom number?
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> I am hoping to get tips for improving the performance of my NPT
>> runs.
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> Many thanks for any reply.
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> Best regards,
>> >>>>>
>> >>>>> Ian
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>>
>> >>>>> _______________________________________________
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>> >>>>>
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>> --
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>>
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Received on Thu Aug 06 2020 - 02:30:03 PDT
