NAMD is a molecular dynamics package designed for simulating the movement of biomolecules over time. It is suited for large biomolecular systems, and it has been used to simulate systems with over 1 billion atoms, presenting stellar scalability on thousands of CPU cores and GPUs. NAMD’s inherent scalability has been awarded with two Gordon Bell prizes.
NAMD is distributed free of charge. User(s) of the NAMD container(s) are reminded to register on the NAMD download site if they have not done so already.
| CPUs | GPUs | Operating Systems | ROCm™ Driver | Container Runtimes |
|---|---|---|---|---|
| X86_64 CPU(s) | AMD Instinct MI200 GPU(s) AMD Instinct MI100 GPU(s) |
Ubuntu 18.04 Cento 8.3 |
ROCm v4.5 compatibility | Docker Engine Singularity |
For ROCm installation procedures and validation checks, see:
In order to assess performance of the container image, NAMD standard benchmark systems were made available in the /examples directory, with sizes ranging from 23 thousand up to a million atoms. If the user wants to collect figure of merit numbers (nanoseconds of simulated time per day) on a machine containing 64-cores and one AMD GPU, it is possible using the following processes for NAMD 2 or NAMD 3.
docker pull amdih/namd:2.15a2-20211101
In order to assess performance of the container image, NAMD standard benchmark systems were made available in the /examples directory, with sizes ranging from 23 thousand up to a million atoms. If the user wants to collect figure of merit numbers (nanoseconds of simulated time per day) on a machine containing 64-cores and one AMD GPU, it is possible using the following process.
Begin by launching a container interactively:
docker run --rm -it --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined amdih/namd:2.15a2-20211101 /bin/bash
Then in the container navigate into the /examples directory and run the benchmarks:
cd /examples
/opt/namd/bin/namd2 jac/jac.namd +p64 +setcpuaffinity +devices 0 > jac.log
/opt/namd/bin/namd2 apoa1/apoa1.namd +p64 +setcpuaffinity +devices 0 > apoa1.log
/opt/namd/bin/namd2 f1atpase/f1atpase.namd +p64 +setcpuaffinity +devices 0 > f1atpase.log
/opt/namd/bin/namd2 stmv/stmv.namd +p64 +setcpuaffinity +devices 0 > stmv.log
The following commands generate NAMD log files with timing information for each one of the benchmarks and averages that information using the ns_per_day.py Python utility, calculating the overall nanoseconds of simulated time per day on that run.
Benchmarking everything might take many minutes depending on how fast your computational resources are.
./ns_per_day.py jac.log
./ns_per_day.py apoa1.log
./ns_per_day.py f1atpase.log
./ns_per_day.py stmv.log
docker pull amdih/namd3:3.0a9
Begin by launching a container interactively:
docker run --rm -it --ipc=host --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined amdih/namd3:3.0a9 /bin/bash
Then in the container navigate into the /examples directory and run the benchmarks:
cd /examples
namd3 jac/jac.namd +p1 +setcpuaffinity --CUDASOAintegrate on +devices 0 > jac.log
namd3 apoa1/apoa1.namd +p1 +setcpuaffinity --CUDASOAintegrate on +devices 0 > apoa1.log
namd3 f1atpase/f1atpase.namd +p1 +setcpuaffinity --CUDASOAintegrate on +devices 0 > f1atpase.log
namd3 stmv/stmv.namd +p1 +setcpuaffinity --CUDASOAintegrate on +devices 0 > stmv.log
The following commands generate NAMD log files with timing information for each one of the benchmarks and averages that information using the ns_per_day.py Python utility, calculating the overall nanoseconds of simulated time per day on that run.
Benchmarking everything might take many minutes depending on how fast your computational resources are.
./ns_per_day.py jac.log
./ns_per_day.py apoa1.log
./ns_per_day.py f1atpase.log
./ns_per_day.py stmv.log
For multiple-GPU simulations, NAMD 3.0 needs to run with exactly one CPU core per GPU. For a 4-GPU simulation, here’s how one would invoke NAMD 3.0 in GPU-resident mode.
cd /examples
namd3 stmv/stmv.namd +p4 +pemap 0-3 --CUDASOAintegrate on +devices 0,1,2,3 > stmv.log
Benchmarking everything might take many minutes depending on how fast your computational resources are.
Your access and use of this application is subject to the terms of the applicable component-level license identified below. To the extent any subcomponent in this container requires an offer for corresponding source code, AMD hereby makes such an offer for corresponding source code form, which will be made available upon request. By accessing and using this application, you are agreeing to fully comply with the terms of this license. If you do not agree to the terms of this license, do not access or use this application.
The application is provided in a container image format that includes the following separate and independent components:
| Package | License | URL |
|---|---|---|
| Ubuntu | Creative Commons CC-BY-SA Version 3.0 UK License | Ubuntu Legal |
| CMAKE | OSI-approved BSD-3 clause | CMake License |
| OpenMPI | BSD 3-Clause | OpenMPI License OpenMPI Dependencies Licenses |
| OpenUCX | BSD 3-Clause | OpenUCX License |
| ROCm | Custom/MIT/Apache V2.0/UIUC OSL | ROCm Licensing Terms |
| Charm++ | Charm++/Converse | Charm++ Charm++ License |
| NAMD | MITx11 | NAMD NAMD License |
Additional third-party content in this container may be subject to additional licenses and restrictions. The components are licensed to you directly by the party that owns the content pursuant to the license terms included with such content and is not licensed to you by AMD. ALL THIRD-PARTY CONTENT IS MADE AVAILABLE BY AMD “AS IS” WITHOUT A WARRANTY OF ANY KIND. USE OF SUCH THIRD-PARTY CONTENT IS DONE AT YOUR SOLE DISCRETION AND UNDER NO CIRCUMSTANCES WILL AMD BE LIABLE TO YOU FOR ANY THIRD-PARTY CONTENT. YOU ASSUME ALL RISK AND ARE SOLELY RESPONSIBLE FOR ANY DAMAGES THAT MAY ARISE FROM YOUR USE OF THIRD-PARTY CONTENT.
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