Would you care to guess what the speedup would be on Amber18 based on
the speedup of the (formerly?) double-precision?
What speedup does GPU give for dihedrals? Could the old CPU code handle
them faster on today's CPU?
Bill
On 8/6/20 2:11 AM, David Cerutti wrote:
> Ah, indeed! The GPU is calculating bonded terms, but the dihedrals in
> particular are a non-trivial part of the calculation, and up to 40% of
> Amber16's calculations in a membrane system. Also the bonded terms were
> all double-precision in Amber16, and that is not a GTX-1080Ti card's
> forte. This was one of the optimizations made in Amber18, so that is
> probably something that could do it.
>
> Dave
>
>
> On Thu, Aug 6, 2020 at 5:08 AM Bill Ross <ross.cgl.ucsf.edu> wrote:
>
>> Does GPU calc bonded terms? I the topology diff is more of these, and
>> CPU could/does handle those bond-related terms, could a faster CPU keep
>> the GPU busy?
>>
>> Bill
>>
>> On 8/6/20 2:02 AM, David Cerutti 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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Received on Thu Aug 06 2020 - 02:30:04 PDT
