From: Norman Geist (norman.geist_at_uni-greifswald.de)
Date: Wed Dec 18 2019 - 06:06:18 CST
You’d also need to find out the real physical->logical core mapping (HT). Each physical core is spitted into 2 virtual cores. Therefore using two virtual cores that map to the same physical core will result in slow down. This layout can be different on various architecture and can be very tricky when having multiple sockets (not in your case). In you case the mapping can be two options (simplified):
1. The mapping is 1-15 real and 16-27 virtual
2. The mapping is each even real, each uneven virtual
You want to use physical core exclusively.
So to map the 1. option, you’d have to map the following for two simulations each using 6 cores:
1st replica : 0-5
2nd replica: 6-11
So to map the 2. option, you’d have to map the following for two simulations each using 6 cores:
1st replica : 0,2,4,6,8,10
2nd replica: 12,14,16,18,20,22
Away from this, you can still have a bottleneck in the memory bandwidth when too many cores are active.
Von: owner-namd-l_at_ks.uiuc.edu [mailto:owner-namd-l_at_ks.uiuc.edu] Im Auftrag von Gerald Keller
Gesendet: Mittwoch, 18. Dezember 2019 12:33
An: dhardy_at_ks.uiuc.edu; namd-l_at_ks.uiuc.edu
Betreff: Re: namd-l: slow down when running 2 simulations on 1 node
the GPUs are selected properly. NAMD is executed with a bash script that sets amongst other things the global variable CUDA_VISIBLE_DEVICES.
>>> David Hardy <dhardy_at_ks.uiuc.edu <mailto: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. Beckman Institute University of Illinois at Urbana-Champaign 405 N. Mathews Ave., Urbana, IL 61801 dhardy_at_ks.uiuc.edu <mailto:dhardy_at_ks.uiuc.edu> , http://www.ks.uiuc.edu/~dhardy/ On Dec 14, 2019, at 9:51 AM, Gerald Keller <gerald.keller_at_uni-wuerzburg.de <mailto: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 also tried: 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? Best regards Gerald >>> Giacomo Fiorin <giacomo.fiorin_at_gmail.com <mailto: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. Giacomo On Thu, Dec 12, 2019 at 2:14 PM Julio Maia <jmaia_at_ks.uiuc.edu <mailto:jmaia_at_ks.uiuc.edu> > wrote: Hi, If you’re not setting the correct affinities, PEs from different replicas might compete for the same cores in your machine. Please try to set CPU affinities for PEs for each replica and try again. You can check how it’s done here: https://www.ks.uiuc.edu/Research/namd/2.13/ug/node105.html Thanks, On Dec 12, 2019, at 2:09 AM, Gerald Keller <gerald.keller_at_uni-wuerzburg.de <mailto:gerald.keller_at_uni-wuerzburg.de> > wrote: Hi everyone, 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: Benchmark time: 18 CPUs 0.0117798 s/step TIMING: 500 CPU: 6.0915, 0.0113823/step Wall: 6.18421, 0.0114815/step TIMING: 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! Best regards Gerald -- Giacomo Fiorin Associate Professor of Research, Temple University, Philadelphia, PA Research collaborator, National Institutes of Health, Bethesda, MD http://goo.gl/Q3TBQU <https://urldefense.proofpoint.com/v2/url?u=http-3A__goo.gl_Q3TBQU&d=DwMFaQ&c=OCIEmEwdEq_aNlsP4fF3gFqSN-E3mlr2t9JcDdfOZag&r=jUfnSyKkfkyVRBIUzlG1GSGGZAZGcznwr8YliSSCjPc&m=l2Cwbk2f0k2qYMVJj3K4Xy91p3coumyOtDd_gRZeKdk&s=cOJ1vDHtAz_1fJPS-_lGYSP0M_0Ig4B8eOmwmUtuAP4&e=> https://github.com/giacomofiorin <https://urldefense.proofpoint.com/v2/url?u=https-3A__github.com_giacomofiorin&d=DwMFaQ&c=OCIEmEwdEq_aNlsP4fF3gFqSN-E3mlr2t9JcDdfOZag&r=jUfnSyKkfkyVRBIUzlG1GSGGZAZGcznwr8YliSSCjPc&m=l2Cwbk2f0k2qYMVJj3K4Xy91p3coumyOtDd_gRZeKdk&s=p7Ls704FOJkeoxhyLWDuG1wdAaYoKb-VCZ5QBhgUizg&e=>
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