From: Gerald Keller (gerald.keller_at_uni-wuerzburg.de)
Date: Wed Dec 18 2019 - 05:33:21 CST
the GPUs are selected properly. NAMD is executed with a bash script that sets amongst other things the global variable
>>> David Hardy <dhardy_at_ks.uiuc.edu> 12/16/19 7:36 PM >>>
I think your slow down might be due to accidentally using both GPUs for each process.
By default, NAMD will use all devices that it finds. You should add to the first invocation of NAMD "+devices 0" to
restrict to using only GPU 0 and to the second "+devices 1" to restrict to using only GPU 1.
NAMD is already CPU-intensive enough on each thread that it generally does not benefit from hyperthreading.
David J. Hardy, Ph.D.
University of Illinois at Urbana-Champaign
405 N. Mathews Ave., Urbana, IL 61801
On Dec 14, 2019, at 9:51 AM, Gerald Keller <gerald.keller_at_uni-wuerzburg.de> wrote:
Thank you all for your suggestions!
I tried out to set cpu affinity but the simulation speed still slows down when starting the second replica.
On a node with Intel(R) Core(TM) i9-7940X CPU @ 3.10GHz (1 socket, 14 cores, 28 with hyperthreading) i tried,
For the first replica on GPU 0 I used: namd2 +setcpuaffinity +pemap 0-11 +p 12 +idlepoll
The second on GPU 1: namd2 +setcpuaffinity +pemap 11-23 +p 12 +idlepoll
1st repilca: namd2 +setcpuaffinity +pemap 0-11:2 +p 6 +idlepoll
2nd repilca: namd2 +setpcuaffinity +pemap 11-23:2 +p 6 + idlepoll
Giacomo mentioned that hyperthreading has to be disabled. I thaught namd would support hyperthreading?
>>> Giacomo Fiorin <giacomo.fiorin_at_gmail.com> 12.12.19 20.27 Uhr >>>
Hello Gerald, I would go with Victor's and Julio's suggestion, but also try making sure that HyperThreading is disabled
i.e. there are 40 CPU physical cores and not 20. In /proc/cpuinfo look for the keyword "ht" among the CPU features.
It is likewise good to keep in mind that unless a program runs entirely on the GPU, transferring data between the GPU
and the CPU goes via circuitry that is most of the time shared among the devices on one motherboard.
On Thu, Dec 12, 2019 at 2:14 PM Julio Maia <jmaia_at_ks.uiuc.edu> wrote:
If you’re not setting the correct affinities, PEs from different replicas might compete for the same cores in your
Please try to set CPU affinities for PEs for each replica and try again. You can check how it’s done here:
On Dec 12, 2019, at 2:09 AM, Gerald Keller <gerald.keller_at_uni-wuerzburg.de> wrote:
in our working group we compute on our own GPU nodes, with no queue system and do not compute on multiple nodes.
When we calculate two replicas of plain MD runs on 1 node with in total 2 GPUs and 40 CPUs we recognized that the
simulation speed slows down when starting the second replica.
1x NAMD on 1 node using 1 GPU and 18 CPUs:
Info: Benchmark time: 18 CPUs 0.00742875 s/step
Info: Benchmark time: 18 CPUs 0.0073947 s/step
Info: Benchmark time: 18 CPUs 0.00747593 s/step
Info: Benchmark time: 18 CPUs 0.00752931 s/step
Info: Benchmark time: 18 CPUs 0.00744549 s/step
Info: Benchmark time: 18 CPUs 0.00746218 s/step
TIMING: 500 CPU: 3.86542, 0.0073741/step Wall: 3.90971, 0.0074047/step
TIMING: 980 CPU: 7.43293, 0.00730715/step Wall: 7.49914, 0.00738945/step
TIMING: 1000 CPU: 7.58503, 0.007605/step Wall: 7.65193, 0.0076393/step
TIMING: 1500 CPU: 11.2973, 0.0073617/step Wall: 11.3969, 0.00763561/step
TIMING: 2000 CPU: 15.0195, 0.00745355/step Wall: 15.1411, 0.0075375/step
2x NAMD on 1 node 1 GPU and 18 CPUs for each replica:
Info: Benchmark time: 18 CPUs 0.0115988 s/step
Info: Benchmark time: 18 CPUs 0.0116316 s/step
Info: Benchmark time: 18 CPUs 0.0118586 s/step
Info: Benchmark time: 18 CPUs 0.0115375 s/step
Info: Benchmark time: 18 CPUs 0.0114114 s/step
Info: BenchmTIMING: 1000 CPU: 11.8594, 0.0126053/step Wall: 12.0109, 0.0127244/step
TIMING: 1500 CPU: 17.564, 0.0114935/step Wall: 17.7579, 0.0116048/step
TIMING: 2000 CPU: 23.3157, 0.0119276/step Wall: 23.5628, 0.0119936/step
If we run 1x NAMD on 1 node using 1 GPU and 18 CPUs and start another simulation with amber on the other GPU, there is
no influence on the namd simulation speed.
Does anyone have an idea why this is happening and how to solve that problem? Because of limited resources, somtimes we
have to run only one simulation per GPU.
Thank you in advance for your suggestions!
-- Giacomo Fiorin Associate Professor of Research, Temple University, Philadelphia, PA Research collaborator, National Institutes of Health, Bethesda, MD http://goo.gl/Q3TBQU https://github.com/giacomofiorin
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