GROMACS

GROMACS is a molecular dynamics application designed to simulate Newtonian\nequations of motion for systems with hundreds to millions of particles.\nGROMACS is designed to simulate biochemical molecules like proteins, lipids,\nand nucleic acids that have a lot of complicated bonded interactions.

System requirements

Before running the NGC GROMACS container please ensure your system meets the following requirements.

One of the following container runtimes\n
- nvidia-docker
- Singularity >= 3.1
\n
One of the following NVIDIA GPU(s)\n
- Pascal(sm60)
- Volta (sm70)
- Ampere (sm80)
- Hopper (sm90)
\n

x86_64

CPU with AVX instruction support
One of the following CUDA driver versions\n
- r520 (>=.61.05)
- >= 450.80.02
\n

arm64

Marvell ThunderX2 CPU
CUDA driver version >= r460

Examples

The following examples demonstrate using the NGC GROMACS container to run the STMV benchmark. Reference performance, on a range of systems, can be found at https://developer.nvidia.com/hpc-application-performance.\nThroughout this example the container version will be referenced as $GROMACS_TAG, replace this with the tag you wish to run.

Download the STMV benchmark:

wget https://zenodo.org/record/3893789/files/GROMACS_heterogeneous_parallelization_benchmark_info_and_systems_JCP.tar.gz \ntar xf GROMACS_heterogeneous_parallelization_benchmark_info_and_systems_JCP.tar.gz \ncd GROMACS_heterogeneous_parallelization_benchmark_info_and_systems_JCP/stmv\n

Running

Run GROMACS using 4 GPUs (with IDs 0,1,2,3). Here we use 2 thread-MPI tasks per GPU (-ntmpi 8), which we find gives good performance. We set 16 OpenMP threads per thread-MPI task (assuming at least 128 CPU cores in the system). These can be adjusted to map to any specific hardware system, and experimented with for best performance.

Docker

export GMX_ENABLE_DIRECT_GPU_COMM=1 \nDOCKER=\"docker run --gpus all -it --rm -v ${PWD}:/host_pwd --workdir /host_pwd nvcr.io/hpc/gromacs:${GROMACS_TAG}\"\n${DOCKER} gmx mdrun -ntmpi 8 -ntomp 16 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123  \n

Singularity

export GMX_ENABLE_DIRECT_GPU_COMM=1\nSINGULARITY=\"singularity run --nv -B ${PWD}:/host_pwd --pwd /host_pwd docker://nvcr.io/hpc/gromacs:${GROMACS_TAG}\"\n${SINGULARITY} gmx mdrun -ntmpi 8 -ntomp 16 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123 \n

Running on Base Platform Command

NVIDIA Base Command Platform (BCP) offers a ready-to-use cloud-hosted solution that manages the end-to-end lifecycle of development, workflows, and resource management. Before running the commands below, install and configure the ngc cli, more information can be found here.

Uploading the Dataset to BCP

Upload the stmv dataset using the command below

ngc dataset upload --source ./stmv/ --desc \"GROMACS stmv dataset\" gromacs_dataset\n

Running GROMACS on BCP

As a note: we must include the -g <md-log-path> and -e <energy log path> to the run command because the mounted working directory is read-only, we must set the paths for the output logs to a writable mounted directory

Single node on a single GPU running the stmv dataset on 4 GPUs with 2 MPI threads per GPU and 15 OpenMP threads per thread-MPI task\nfor a total of 120 CPU cores.

--instance dgxa100.80g.4.norm --commandline "/usr/bin/nventry -build_base_dir=/usr/local/gromacs -build_default=avx2_256 gmx mdrun -g /results/md.log -e /results/ener.edr -ntmpi 8 -ntomp 15 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123" --result /results/ --image "hpc/gromacs:2023.2" --org --datasetid :/host_pwd/\n\">

ngc batch run --name \"gromacs_reducentomp120cores\" --priority NORMAL --order 50 --preempt RUNONCE --min-timeslice 0s --total-runtime 0s --ace <your-ace> --instance dgxa100.80g.4.norm --commandline \"/usr/bin/nventry -build_base_dir=/usr/local/gromacs -build_default=avx2_256 gmx mdrun -g /results/md.log -e /results/ener.edr -ntmpi 8 -ntomp 15 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123\" --result /results/ --image \"hpc/gromacs:2023.2\" --org <your-org> --datasetid <dataset-id>:/host_pwd/\n

Suggested Reading

GROMACS

GROMACS GitHub

GROMACS Documentation

GROMACS GPU Acceleration

GROMACS 2020 GPU optimization

Maximizing GROMACS Throughput with Multiple Simulations per GPU Using MPS and MIG | NVIDIA Technical Blog

Massively Improved Multi-node NVIDI

BCP User Guide

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Reference performance, on a range of systems, can be found at https://developer.nvidia.com/hpc-application-performance.\nThroughout this example the container version will be referenced as `$GROMACS_TAG`, replace this with the tag you wish to run.\n\nDownload the `STMV` benchmark:\n\n```bash\nwget https://zenodo.org/record/3893789/files/GROMACS_heterogeneous_parallelization_benchmark_info_and_systems_JCP.tar.gz \ntar xf GROMACS_heterogeneous_parallelization_benchmark_info_and_systems_JCP.tar.gz \ncd GROMACS_heterogeneous_parallelization_benchmark_info_and_systems_JCP/stmv\n```\n\n## Running\n\nRun GROMACS using 4 GPUs (with IDs 0,1,2,3). Here we use 2 thread-MPI tasks per GPU (-ntmpi 8), which we find gives good performance. We set 16 OpenMP threads per thread-MPI task (assuming at least 128 CPU cores in the system). These can be adjusted to map to any specific hardware system, and experimented with for best performance.\n\n### Docker\n\n```bash\nexport GMX_ENABLE_DIRECT_GPU_COMM=1 \nDOCKER=\"docker run --gpus all -it --rm -v ${PWD}:/host_pwd --workdir /host_pwd nvcr.io/hpc/gromacs:${GROMACS_TAG}\"\n${DOCKER} gmx mdrun -ntmpi 8 -ntomp 16 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123 \n```\n\n### Singularity\n\n```bash\nexport GMX_ENABLE_DIRECT_GPU_COMM=1\nSINGULARITY=\"singularity run --nv -B ${PWD}:/host_pwd --pwd /host_pwd docker://nvcr.io/hpc/gromacs:${GROMACS_TAG}\"\n${SINGULARITY} gmx mdrun -ntmpi 8 -ntomp 16 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123 \n```\n\n### Running on Base Platform Command\n\nNVIDIA Base Command Platform (BCP) offers a ready-to-use cloud-hosted solution that manages the end-to-end lifecycle of development, workflows, and resource management. Before running the commands below, install and configure the ngc cli, more information can be found [here](https://docs.nvidia.com/base-command-platform/user-guide/index.html#installing-ngc-cli).\n\n##### Uploading the Dataset to BCP\n\nUpload the `stmv` dataset using the command below\n\n```bash\nngc dataset upload --source ./stmv/ --desc \"GROMACS stmv dataset\" gromacs_dataset\n```\n\n##### Running GROMACS on BCP\n\n**As a note: we must include the `-g ` and `-e ` to the run command because the mounted working directory is read-only, we must set the paths for the output logs to a writable mounted directory**\n\nSingle node on a single GPU running the `stmv` dataset on 4 GPUs with 2 MPI threads per GPU and 15 OpenMP threads per thread-MPI task\nfor a total of 120 CPU cores.\n\n```bash\nngc batch run --name \"gromacs_reducentomp120cores\" --priority NORMAL --order 50 --preempt RUNONCE --min-timeslice 0s --total-runtime 0s --ace --instance dgxa100.80g.4.norm --commandline \"/usr/bin/nventry -build_base_dir=/usr/local/gromacs -build_default=avx2_256 gmx mdrun -g /results/md.log -e /results/ener.edr -ntmpi 8 -ntomp 15 -nb gpu -pme gpu -npme 1 -update gpu -bonded gpu -nsteps 100000 -resetstep 90000 -noconfout -dlb no -nstlist 300 -pin on -v -gpu_id 0123\" --result /results/ --image \"hpc/gromacs:2023.2\" --org --datasetid :/host_pwd/\n```\n\n## Suggested Reading\n\n[GROMACS](http://www.gromacs.org/)\n\n[GROMACS GitHub](https://github.com/gromacs/gromacs)\n\n[GROMACS Documentation](http://manual.gromacs.org/documentation/)\n\n[GROMACS GPU Acceleration](https://www.gromacs.org/GPU_acceleration)\n\n[GROMACS 2020 GPU optimization](https://devblogs.nvidia.com/creating-faster-molecular-dynamics-simulations-with-gromacs-2020/)\n\n[Maximizing GROMACS Throughput with Multiple Simulations per GPU Using MPS and MIG | NVIDIA Technical Blog](https://developer.nvidia.com/blog/maximizing-gromacs-throughput-with-multiple-simulations-per-gpu-using-mps-and-mig/)\n\n[Massively Improved Multi-node NVIDI](https://developer.nvidia.com/blog/massively-improved-multi-node-nvidia-gpu-scalability-with-gromacs/)\n\n[BCP User Guide](https://docs.nvidia.com/base-command-platform/index.html#nvidia-base-command-platform)","GROMACS",["D",1692318792078],"KTH Royal Institute of Technology",[286,284,282,280,278,198,275,273,271,269,266],"/api/image/hpc/-/containers/gromacs",425138797,"hasSignedTag",[],[306,307,308,309,310],[],[],[],{"_90":318,"_126":318,"_127":319,"_129":130},{"_90":316,"_126":316,"_127":317,"_129":130},{"_90":314,"_126":314,"_127":315,"_129":130},{"_90":85,"_126":85,"_127":313,"_129":130},{"_90":311,"_126":311,"_127":312,"_129":130},"x86_64",{"_131":130,"_126":311},{"_131":130,"_126":85},"DGX Cloud Supported",{"_131":130,"_126":314},"covid-19",{"_131":130,"_126":316},"arm64",{"_131":130,"_126":318},{"_165":1445,"_167":1446},"ngcApps",[1398],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1379":1380,"_1381":1382,"_1383":1384,"_1385":1386,"_1387":331,"_197":357,"_1388":1389,"_1390":51,"_1391":143,"_1392":143,"_1393":1394,"_1395":143,"_1396":1397},"amplitude",{"_1376":1377,"_993":-7,"_1378":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0yJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1370":1371,"_1372":1373,"_1374":1375},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"manageableExternalUserGroups",[],"ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"now",1793363631278,"1.29.0","defaultSearchSuggestions",{"_1256":438,"_1257":1258,"_1259":1260,"_1261":1262},"announcements",[],"consentState",{"_395":1251,"_1252":26,"_1253":26,"_1254":143,"_1255":-5},"consentConfig",{"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392,"_393":394,"_395":396},"showContactAdmin",[],[],[370,371,372,373,374,375,376],{"_40":62,"_42":66,"_44":57,"_46":58,"_48":160,"_50":51},{"_40":62,"_42":64,"_44":57,"_46":58,"_48":65,"_50":51},{"_40":62,"_42":63,"_44":57,"_46":58,"_48":228,"_50":51},{"_40":55,"_42":60,"_44":57,"_46":58,"_48":227,"_50":51},{"_40":55,"_42":56,"_44":57,"_46":58,"_48":59,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":54,"_50":51},{"_40":41,"_42":43,"_44":45,"_46":47,"_48":49,"_50":51},"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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These cookies may store a unique ID so that our system will remember you when you return. 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