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",[276,274,272,270,268,266,196,263,261,259,256],"/api/image/hpc/-/containers/gromacs",425138797,"hasSignedTag",[],[297,298,299,300,301],[],[],[],{"_91":309,"_126":309,"_127":310,"_129":130},{"_91":307,"_126":307,"_127":308,"_129":130},{"_91":305,"_126":305,"_127":306,"_129":130},{"_91":86,"_126":86,"_127":304,"_129":130},{"_91":302,"_126":302,"_127":303,"_129":130},"x86_64",{"_131":130,"_126":302},{"_131":130,"_126":86},"DGX Cloud Supported",{"_131":130,"_126":305},"covid-19",{"_131":130,"_126":307},"arm64",{"_131":130,"_126":309},{"_163":1483,"_165":1484},"ngcApps",[1436],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1417":1418,"_1419":1420,"_1421":1422,"_1423":1424,"_1425":322,"_195":346,"_1426":1427,"_1428":51,"_1429":143,"_1430":143,"_1431":1432,"_1433":143,"_1434":1435},"amplitude",{"_1414":1415,"_1019":-7,"_1416":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBdHJ1ZSU3RA==","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1408":1409,"_1410":1411,"_1412":1413},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.7.3","defaultSearchSuggestions",{"_1294":427,"_1295":1296,"_1297":1298,"_1299":1300},"announcements",[],"consentState",{"_385":1284,"_1285":143,"_1286":26,"_1287":26,"_1288":1289},"consentConfig",{"_367":368,"_369":370,"_371":372,"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386},"showContactAdmin",1784105629808,[],[],[360,361,362,363,364,365],{"_40":61,"_42":66,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":64,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":61,"_42":62,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":54,"_42":59,"_44":63,"_46":-5,"_48":-5,"_50":51},{"_40":52,"_42":53,"_44":45,"_46":47,"_48":366,"_50":51},{"_40":41,"_42":43,"_44":63,"_46":-5,"_48":-5,"_50":51},4,"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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As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\nThis is a CloudFoundry cookie","OptanonConsent","nvid.nvidia.com","This cookie is set by the cookie compliance solution from OneTrust. It stores information about the categories of cookies the site uses and whether visitors have given or withdrawn consent for the use of each category. This enables site owners to prevent cookies in each category from being set in the users browser, when consent is not given. The cookie has a normal lifespan of one year, so that returning visitors to the site will have their preferences remembered. It contains no information that can identify the site visitor.","events.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. As part of our bot management service, this cookie helps manage incoming traffic that matches criteria associated with bots.\n","This cookie name is used by DataDog to determine if cookies are supported on the website.dd_cookie_test","AMP_TLDTEST_xxxxxxxx","SESSlsd","This cookie is likely to be a session identifier used to maintain user session state across page requests.","mboxEdgeCluster","This cookie name is associated with Adobe Target which is used to communicate the correct edge cluster to at.js so that Target profiles can stay in sync as users navigate across a site.","_biz_pendingA","_rdt_em","This cookie name is associated with Reddit which is used to manage session state and user interactions.","megaNavCollapsed","_abck","The _abck cookie is associated with Akamai's Bot Manager, which is used to prevent fraud and abuse by safeguarding online interactions and detecting irregularities that could suggest malicious activity, such as bot-driven attacks or hacking attempts.","AMP_TEST","This cookie name AMP_TEST is used for internal analytics by the website operator, necessary for the functionality of the website's chat-box function, and to check whether the cookie storage is working properly.","idccsrf","This cookie name is associated with Salesforce which is used to track CrossSiteRequestForgery validation for certain single sign-on (SSO) flows.","_biz_flagsA","This cookie is used to remember users' settings as well as for authentication and analytics.","ApplicationGatewayAffinity","partners.nvidia.com","This cookie is used to maintain a user session by the server in a load balanced environment, to ensure the user has a consistent experience.","AMP_TEST_xxxxxxxx","bm_lso","This cookie name is associated with Akamai which is used to load the site as quickly as possible and to recognize bots.","SSESS###########","This cookie is likely to be a session cookie used to maintain an anonymized user session by the server in a secure environment, often seen in sites built with the Drupal content management system.","nvdeveloper.nvidia.com","__uxq412__id.88c1","QueueITAccepted-SDFrts345E-V3_{eventId}","TEST_AMCV_COOKIE_WRITE","This cookie is used to test whether cookies are enabled in the browser. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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