NAMD is a parallel molecular dynamics program for UNIX platforms designed for high-performance simulations in structural biology. It is developed by the Theoretical Biophysics Group at the Beckman Center, University of Illinois.
NAMD was developed to be compatible with existing molecular dynamics packages, especially the packages X-PLOR and CHARMM, so it will accept X-PLOR and CHARMM input files. The output files produced by NAMD are also compatible with X-PLOR and CHARMM.
NAMD is closely integrated with with VMD for visualization and analysis.
There are several versions of NAMD available on Biowulf. You can check the available versions with
module avail NAMD
Important: Please read the webpage Making efficient use of Biowulf's Multinode Partition before running large parallel jobs.
Following the bug reported by NAMD Developers in version 3.0, version 3.0.1 multicore-CUDA is now available on Biowulf.
Read more about the NAMD 3.0.1 Announcement here
Please be advised 3.0.1 scales with only NVLink supported hardware- a100 and v100x GPU nodes
The latest GPU-resident single-node-per-replicate computation mode NAMD 3.0.1 build is available via
module load NAMD/3.0.1/multicore-CUDA.
The latest benchmarking tests from NAMD are in /usr/local/apps/NAMD/TESTDATA/stmv_gpu.tar.gz To learn more about GPU resident mode, visit the NAMD benchmarks pageSample script, NAMD 3.0.1 on Multiple GPUs
#!/bin/bash cd /data/$USER/mydir module load NAMD/3.0.1/multicore-CUDA # 1 GPU echo STMV NVE GPU-resident on 1 GPU namd3 +p8 +setcpuaffinity +devices 0 new.namd # 2 GPU echo STMV NVE GPU-resident on 2 GPUs namd3 +p15 +pmepes 7 +setcpuaffinity +devices 0,1 new.namd # 4 GPU echo STMV NVE GPU-resident on 4 GPUs namd3 +p29 +pmepes 5 +setcpuaffinity +devices 0,1,2,3 new.namdSubmit with, for example:
sbatch -p gpu --gres=gpu:v100x:1 --cpus-per-task=8 --nodes=1 my_batch_script.sh # 1 v100x GPU sbatch -p gpu --gres=gpu:v100x:2 --cpus-per-task=32 --nodes=1 my_batch_script.sh # 2 v100x GPU sbatch -p gpu --gres=gpu:a100:2 --cpus-per-task=32 --nodes=1 my_batch_script.sh # 2 a100 GPU sbatch -p gpu --gres=gpu:a100:4 --cpus-per-task=64 --nodes=1 my_batch_script.sh # all 4 a100 GPU on a single nodeSample script, single-node GPU job, NAMD 3.0.1
#!/bin/bash
module load NAMD/3.0.1/multicore-CUDA
# 1 GPU
echo STMV NVE GPU-resident on 1 GPU
namd3 +p${SLURM_CPUS_PER_TASK} +setcpuaffinity new.namd
Submit with, for example:
sbatch -p gpu --gres=gpu:v100x:1 --cpus-per-task=8 --nodes=1 my_batch_script.sh # 1 v100x GPU sbatch -p gpu --gres=gpu:a100:2 --cpus-per-task=16 --nodes=1 my_batch_script.sh # 2 a100 GPU
See the benchmarks page for some samples of performance.
Single-node GPU job, NAMD 2.14
To run a single-node GPU job, that will run on a single K80, P100, V100 or V100x node, create a batch script along the
following lines. Note thatn NAMD 2.14 can run (and it is recommended to) multiple ntasks per GPU.
#!/bin/bash cd /data/$USER/mydir module load NAMD/2.14-verbs-CUDA charmrun ++local `which namd3` +p $SLURM_NTASKS +setcpuaffinity stmv.namdThe environment variable $CUDA_VISIBLE_DEVICES will be set by Slurm to the GPU devices that are allocated to the job.
To submit to 2 GPU devices and half the CPUs on a K80 node:
sbatch --partition=gpu --gres=gpu:k80:2 --ntasks=14 --ntasks-per-core=1 jobscript
To submit to all 4 GPU devices and all the CPUs on a V100 node:
sbatch --partition=gpu --gres=gpu:v100:4 --ntasks=28 --ntasks-per-core=1 --exclusive jobscript
As per the NAMD GPU documentation, multiple NAMD threads can utilize the same set of GPUs, and the tasks are equally distributed among the allocated GPUs on a node.
Multi-node GPU job
While it is possible to run a multinode GPU NAMD job, please be sure that your NAMD job scales to more than 1 GPU node before submitting multinode GPU jobs. (See our benchmarks for details). To submit a multinode job, you could use a script like the following:
#!/bin/bash cd /data/$USER/mydir module load NAMD/2.14-verbs-CUDA # on a K80 node make-namd-nodelist charmrun ++nodelist ~/namd.$SLURM_JOBID ++p $SLURM_NTASKS `which namd2` ++ppn 28 input.namdTo submit to 2 K80 nodes:
sbatch ---partition=gpu --gres=gpu:k80:4 --ntasks=56 --ntasks-per-core=1 --nodes=2 --exclusive jobscriptNote that the number of ntasks is set to the number of cores on 2 nodes, i.e. 56.
Monitoring GPU jobs
To monitor your GPU jobs, use 'jobload' to see the CPU utilization (should be ~ 50%), and 'ssh nodename nvidia-smi' to see the GPU utilization. In the example below, a NAMD job is submitted to 8 GPUs (2 nodes) and 56 cores (all cores on the 2 nodes).
[biowulf]$ sbatch --partition=gpu --gres=gpu:k80:4 --ntasks=56 --ntasks-per-core=1 --nodes=2 run.gpu 129566Jobload shows that the job is utilizing all cores:
[biowulf]$ jobload -u user
JOBID RUNTIME NODES CPUS AVG CPU% MEMORY
Used/Alloc
129566 00:00:26 cn0603 56 50.00 836.9 MB/62.5 GB
00:00:26 cn0604 56 50.06 644.6 MB/62.5 GB
The 'nvidia-smi' command shows that there are 4 NAMD processes running on the 4 GPUs of the node. The 'GPU-Util' value will bounce around, so is not very meaningful.
[biowulf]$ ssh cn3084 nvidia-smi Sun Feb 26 15:19:07 2017 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 367.48 Driver Version: 367.48 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K80 On | 0000:83:00.0 Off | Off | | N/A 48C P0 58W / 149W | 91MiB / 12205MiB | 15% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K80 On | 0000:84:00.0 Off | Off | | N/A 35C P0 75W / 149W | 91MiB / 12205MiB | 15% Default | +-------------------------------+----------------------+----------------------+ | 2 Tesla K80 On | 0000:8A:00.0 Off | Off | | N/A 51C P0 62W / 149W | 90MiB / 12205MiB | 12% Default | +-------------------------------+----------------------+----------------------+ | 3 Tesla K80 On | 0000:8B:00.0 Off | Off | | N/A 39C P0 76W / 149W | 91MiB / 12205MiB | 14% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 40816 C ..._2.12_Linux-x86_64-ibverbs-smp-CUDA/namd2 87MiB | | 1 40816 C ..._2.12_Linux-x86_64-ibverbs-smp-CUDA/namd2 87MiB | | 2 40816 C ..._2.12_Linux-x86_64-ibverbs-smp-CUDA/namd2 86MiB | | 3 40816 C ..._2.12_Linux-x86_64-ibverbs-smp-CUDA/namd2 87MiB | +-----------------------------------------------------------------------------+
The following example uses the ApoA1 benchmark example from the NAMD site. It is available on Biowulf in
/usr/local/apps/NAMD/TESTDATA
Specifying a homogenous set of nodes
The 'multinode' partition, to which all jobs that require more than a single node must be submitted, is heterogenous. For efficient multinode parallel jobs, you need to ensure that you request nodes of a single CPU type. For example, at the time of writing this webpage, the 'freen' command displays:
biowulf% freen Partition FreeNds FreeCPUs Cores CPUs Mem Disk Features ------------------------------------------------------------------------------------------------------- ... multinode 65/466 3640/26096 28 56 248g 400g cpu56,core28,g256,ssd400,x2695,ibfdr multinode 4/190 128/6080 16 32 60g 800g cpu32,core16,g64,ssd800,x2650,ibfdr multinode 312/539 17646/30184 28 56 250g 800g cpu56,core28,g256,ssd800,x2680,ibfdr ...These lines indicate that there are 3 kinds of nodes in the multinode partition. You should submit your job exclusively to one kind of node by specifying --constraint=x2695, --constraint=x2650, or --constraint=x2680 as in the examples below.
Sample batch script for the ibverbs version:
#!/bin/bash cd /data/$USER/mydir module load NAMD/2.14-verbs make-namd-nodelist charmrun ++nodelist ~/namd.$SLURM_JOBID ++p $SLURM_NTASKS `which namd2` +setcpuaffinity input.namd # delete the NAMD-specific node list rm ~/namd.$SLURM_JOBIDNote: The NAMD +setcpuaffinity flag should be used for the ibverbs version for performance improvement. This flag should not be used when running the OpenMPI/Intel compiled version, since OpenMPI enforces its own cpu affinity. It should also not be used when you are not allocating all the CPUs on a node, since it assigns the cpu affinity in a round-robin fashion. See https://www.ks.uiuc.edu/Research/namd/2.9/ug/node87.html .
Sample batch script for the OpenMPI 2.0/Intel-compiler version compiled on Biowulf
Note: in our benchmarks, this version was slightly slower than the ibverbs version, so most users will want to use the ibverbs version.
#!/bin/bash # cd /data/$USER/mydir module load NAMD/2.14-openmpi mpirun -np $SLURM_NTASKS `which namd2` input.namd
Submit this job with:
sbatch --partition=multinode --constraint=x2680 --ntasks=# --ntasks-per-core=1 --time=168:00:00 --exclusive jobscript
where:
Due to a technical complication, 'jobload' may report incorrect results for a NAMD parallel job. Here is a typical NAMD ibverbs run with the jobload showing as 0:
JOBID TIME NODES CPUS THREADS LOAD MEMORY
Elapsed / Wall Alloc Active Used / Alloc
32072214 00:03:29 / 08:00:00 cn1517 56 0 0% 0.0 / 56.0 GB
00:03:29 / 08:00:00 cn1518 0 0 0% 0.0 / 0.0 GB
Nodes: 2 CPUs: 112 Load Avg: 0%
However, 'ssh cn1517 ps -C namd2' will show that there are 28 namd2 processes on each node. The NAMD output file will also report details such as:
Charm++> Running on 2 unique compute nodes (56-way SMP).
Sample Replica Exchange job script using the OpenMPI version on Infiniband (multinode) partition
#!/bin/bash cd /data/$USER/mydir module load NAMD/2.13b2-openmpi mkdir output (cd output; mkdir 0 1 2 3 4 5 6 7) mpirun namd2 +replicas 8 job0.conf +stdout output/%d/job0.%d.logThe number of MPI ranks must be a multiple of the number of replicas. Thus, for the 8 replicas above, you could submit with:
sbatch --partition=multinode --ntasks=24 --ntasks-per-core=1 --nodes=1 --exclusive jobscriptusing 24 of the 28 physical cores on a single node.
Replica Exchange job using the verbs-CUDA version on multiple GPUs
#!/bin/bash
cd $SLURM_SUBMIT_DIR
# this run uses the fold_alanin example provided by NAMD
# download the example files and set up the output directories
tar xvf /usr/local/apps/NAMD/2.13b2/replica_example.tar.gz
cd replica/example
mkdir output
(cd output; mkdir 0 1 2 3 4 5 6 7)
module load NAMD/NAMD/2.13b2-verbs-CUDA
make-namd-nodelist
charmrun ++nodelist ~/namd.$SLURM_JOBID +p8 \
`which namd2` +replicas 8 job0.conf +stdout output/%d/job0.%d.log
To run the above on 8 k80 GPUs (2 nodes), you would submit with:
sbatch --partition=gpu --gres=gpu:k80:4 --nodes=2 --ntasks=32 --exclusive jobscriptNote that jobload will report incorrect usage for this job. It will look like:
JOBID TIME NODES CPUS THREADS LOAD MEMORY
Elapsed / Wall Alloc Active Used / Alloc
48970813 00:16:44 / 02:00:00 cn4200 56 0 0% 0.0 / 112.0 GB
00:16:44 / 02:00:00 cn4201 0 0 0% 0.0 / 0.0 GB
However, the appropriate processes and GPU usage can be checked with commands such as the following. For the example above (8 replicas, 8 GPUs), you should see
4 namd2 processes on the node CPUs, and 4 namd2 processes on the GPUs of each node.
biowulf% ssh cn4200 ps auxw | grep namd user 51222 0.0 0.0 17168 1496 ? S 11:18 0:00 charmrun ++nodelist /home/user/namd.48970813 +p8 /usr/local/apps/NAMD/2.13b2/NAMD_2.13_Linux-x86_64-verbs-smp-CUDA/namd2 +replicas 8 job0.conf +stdout output/%d/job0.%d.log user 51297 100 0.1 323977568 299836 ? Rl 11:18 4:29 /usr/local/apps/NAMD/2.13b2/NAMD_2.13_Linux-x86_64-verbs-smp-CUDA/namd2 +replicas 8 job0.conf +stdout output/%d/job0.%d.log user 51299 100 0.1 323977572 297048 ? Rl 11:18 4:29 /usr/local/apps/NAMD/2.13b2/NAMD_2.13_Linux-x86_64-verbs-smp-CUDA/namd2 +replicas 8 job0.conf +stdout output/%d/job0.%d.log user 51300 100 0.1 323977572 297344 ? Rl 11:18 4:29 /usr/local/apps/NAMD/2.13b2/NAMD_2.13_Linux-x86_64-verbs-smp-CUDA/namd2 +replicas 8 job0.conf +stdout output/%d/job0.%d.log user 51301 100 0.1 323977568 296996 ? Rl 11:18 4:29 /usr/local/apps/NAMD/2.13b2/NAMD_2.13_Linux-x86_64-verbs-smp-CUDA/namd2 +replicas 8 job0.conf +stdout output/%d/job0.%d.log biowulf% ssh cn4200 nvidia-smi Wed Feb 19 11:23:33 2020 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 440.33.01 Driver Version: 440.33.01 CUDA Version: 10.2 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Tesla K80 On | 00000000:83:00.0 Off | Off | | N/A 64C P0 75W / 149W | 414MiB / 12206MiB | 91% Default | +-------------------------------+----------------------+----------------------+ | 1 Tesla K80 On | 00000000:84:00.0 Off | Off | | N/A 30C P8 34W / 149W | 11MiB / 12206MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 2 Tesla K80 On | 00000000:8A:00.0 Off | Off | | N/A 42C P8 27W / 149W | 11MiB / 12206MiB | 0% Default | +-------------------------------+----------------------+----------------------+ | 3 Tesla K80 On | 00000000:8B:00.0 Off | Off | | N/A 37C P8 34W / 149W | 11MiB / 12206MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | 0 51297 C ..._2.13_Linux-x86_64-verbs-smp-CUDA/namd2 100MiB | | 1 51299 C ..._2.13_Linux-x86_64-verbs-smp-CUDA/namd2 100MiB | | 2 51300 C ..._2.13_Linux-x86_64-verbs-smp-CUDA/namd2 100MiB | | 3 51301 C ..._2.13_Linux-x86_64-verbs-smp-CUDA/namd2 100MiB | +-----------------------------------------------------------------------------+
NAMD version 2.9 has been compiled with support for Plumed 2.4.2, a library for performing free energy calculation as part of molecular simulations. To use Plumed, you must load the NAMD/2.9-plumed module. This version is compiled with OpenMPI to allow parallelization of Plumed, and therefore, the sample batch job for the OpenMPI version given above should be used as the basis of scripts.
An example submission script using Plumed would look like:
#!/bin/bash #SBATCH --nodes=4 #SBATCH --ntasks-per-core=1 #SBATCH --ntasks-per-node=16 #SBATCH --ntasks=64 #SBATCH --partition=multinode #SBATCH --constraint=x2650 #SBATCH --mem=48g module load NAMD/2.9-plumed mpirun -np $SLURM_NTASKS --mca btl self,sm,openib --mca btl_openib_if_exclude "mlx4_0:2" `which namd2` input
Note that the necessary compiler, MPI, and FFTW modules are loaded automatically by the NAMD module.
There are walltime limits on most Biowulf partitions. Use 'batchlim' to see the current walltime limits.
An example namd config file for running a second simulation starting from the last timestep and the restart files of a previous simulation is available at http://www.ks.uiuc.edu/~timisgro/sample.conf.
If restarting a NAMD REMD job, be sure to comment out the 'bincoordinates' and 'extendedsystem' parameters in your NAMD configuration file, if applicable
After an initial run has produced a set of restart files, you would submit future runs using a batch script along these lines:
#!/bin/bash module load NAMD/2.10 # Create host file (required) make-namd-nodelist mpirun -n $SLURM_NTASKS `which namd2` myjob.restart.namd > out.log rm -f ~/namd.$SLURM_JOBID # this script resubmits itself to the batch queue. # The NAMD config file is set up to start the simulation from the last timestep # in the previous simulation sbatch --partition=multinode --constraint=x2650 --ntasks=$SLURM_NTASKS --ntasks-per-core=1 --time=168:00:00 --exclusive this_job_scriptSubmit this script, as usual, with a command like:
sbatch --partition=multinode --constraint=x2650 --ntasks=64 --ntasks-per-node=1 --time=168:00:00 --exclusive this_job_scriptThe NAMD 2.10 replica.namd file is at /usr/local/apps/NAMD/NAMD_2.10_Linux-x86_64-ibverbs/lib/replica/replica.namd.
The most important points here are:
- Ensure you do not delete your replica folders when you run the restart (as this is usually done when you start a new REMD simulation)
- In your job0.conf (or whatever you name it) file, include the following two lines after the line referencing the NAMD configuration
source [format $output_root.job0.restart20.tcl ""] set num_runs 10000The items in bold will likely be subjective. And the number of runs, is the TOTAL number of runs for the simualtion, not the number of runs to run from that point forward. So in the example above, the restart will begin at the 20th run and continue till it reaches the 10,000th run.
This thread in the NAMD mailing list may help in debugging problems.
Theoretical and Computational Biophysics group at UIUC, the NAMD/VMD developers.