NAMD

NAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems.\nNAMD uses the popular molecular graphics program VMD for simulation setup and trajectory analysis, but is also\nfile-compatible with AMBER, CHARMM, and X-PLOR.

System requirements

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

One of the following container runtimes\n
- NVIDIA Container Toolkit
- 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 AVX2 instruction support
One of the following CUDA driver versions\n
- r560 (>=.35.05)
- >= 560.35.05
\n

Running NAMD Examples

Download Dataset

The ApoA1 benchmark consists of 92,224 atoms and has been a standard NAMD cross-platform benchmark for years. Follow\nthe steps below to use the APOA1 input dataset to test the NGC NAMD container.

Download the APOA1 dataset to your current directory:

wget -O - https://gitlab.com/NVHPC/ngc-examples/raw/master/namd/3.0/get_apoa1.sh | bash\n

Take a moment to inspect the shell script above. In particular, it injects the CUDASOAintegrate on in the configuration file, which enables the NAMD 3.0 GPU-resident mode code path.

Replace {input_file} in the examples below with the path to the apoa1 namd input file:

/host_pwd/apoa1/apoa1_nve_cuda_soa.namd\n

Select Tag

Several NAMD images are available, depending on your needs. Set the following environment variable which will be used in the example below.

export NAMD_TAG={TAG}\n

Where {TAG} is 3.0-beta5 or any other tag previously posted on NGC.

Set the executable name depending on the chosen tag.

export NAMD_EXE=namd3\n

Running with the NVIDIA Container Toolkit

NGC supports the Docker runtime through NVIDIA Container Toolkit plugin.

docker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG \n

Usage

Launch NAMD with 1 CPU thread, utilizing 1 GPU (simplest way for NAMD versions >= 3.0), on your local machine or single node:

docker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG ${NAMD_EXE} +p1 +devices 0 +setcpuaffinity {input_file}\n

The +p argument is used to specify the number of cores to be used, and +devices specify the GPUs used. To use 2 CPU threads and 2 GPUs:

docker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG ${NAMD_EXE} +p2 +devices 0,1 +setcpuaffinity {input_file}\n

It is recommanded to run NAMD 2.x for very large systems, multi-node simulations, or Pascal GPUs.\nIn NAMD 3, this can be achieved by setting CUDASOAintegrate off or simply not setting it in the configuration file.\nThe input file /host_pwd/apoa1/apoa1_nve_cuda.namd (note the lack of _soa in file name) in the APOA1 dataset can be used to test this:

docker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG ${NAMD_EXE} +ppn $(nproc) +setcpuaffinity +idlepoll {input_file}\n

The nproc command is used to specify all available CPU cores should be used. Depending on system setup manually specifying the number of PE's may yield better performance.

Running with Singularity

singularity run --nv -B $PWD:/host_pwd --pwd /host_pwd nvcr.io/hpc/namd:$NAMD_TAG\n

Launch NAMD with 1 CPU thread, utilizing 1 GPU (simplest way for NAMD versions >= 3.0), on your local machine or single node:

${SINGULARITY} ${NAMD_EXE} +p1 +devices 0 +setcpuaffinity {input_file}\n

The +p argument is used to specify the number of cores to be used, and +devices specify the GPUs used. To use 2 CPU threads and 2 GPUs:

${SINGULARITY} ${NAMD_EXE} +p2 +devices 0,1 +setcpuaffinity {input_file}\n

In addition, for very large systems, multi-node simulations, or Pascal GPUs, it is recommanded to run NAMD 2.x.\nIn NAMD 3, this can be achieved by setting CUDASOAintegrate off or simply not setting it in the configuration file.\nThe input file /host_pwd/apoa1/apoa1_nve_cuda.namd (note the lack of _soa in file name) in the APOA1 dataset can be used to test this:

${SINGULARITY} ${NAMD_EXE} +ppn $(nproc) +setcpuaffinity +idlepoll {input_file}\n

The nproc command is used to specify all available CPU cores should be used. Depending on system setup manually specifying the number of PE's may yield better performance.

Note: Singularity 3.1.x - 3.5.x

There is currently a bug in Singularity 3.1.x and 3.2.x causing the LD_LIBRARY_PATH to be incorrectly set within the container environment.\nAs a workaround The LD_LIBRARY_PATH must be unset before invoking Singularity:

$ LD_LIBRARY_PATH=\"\" singularity exec ...\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

Note: apoa1_nve_cuda_soa.namd needs to be modified to remove the outputName parameter due to the nature of the mounted read-only dataset directory.

Upload the apoa1 dataset using the command below

ngc dataset upload --source ./apoa1/ --desc \"NAMD dataset\" namd_dataset\n

Running NAMD on BCP

Single node on a single GPU running the apoa1 dataset

--instance dgxa100.80g.1.norm --commandline "namd3 +p1 +devices 0 +setcpuaffinity --outputName /results/namd_output /work/apoa1_nve_cuda_soa.namd" --result /results/ --image "hpc/namd:${NAMD_TAG}" --org --datasetid :/work/\n\n\">

ngc batch run --name \"NAMD_single_gpu\" --priority NORMAL --order 50 --preempt RUNONCE --min-timeslice 0s --total-runtime 0s --ace <your-ace> --instance dgxa100.80g.1.norm --commandline \"namd3 +p1 +devices 0 +setcpuaffinity --outputName /results/namd_output /work/apoa1_nve_cuda_soa.namd\" --result /results/ --image \"hpc/namd:${NAMD_TAG}\" --org <your-org> --datasetid <datasetid>:/work/\n\n

Known Issue for tag `3.0-beta2`

Using more than one thread per GPU will result in an error at the end of execution. This bug will be fixed in a coming version. To avoid this error, either use tag 3.0-beta5 or specify both GPU-resident mode (CUDASOAintegrate on) and device migration (DeviceMigration on).

Suggested Reading

NAMD

NAMD Manual

BCP User Guide

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NAMD uses the popular molecular graphics program VMD for simulation setup and trajectory analysis.","## NAMD\n\nNAMD is a parallel molecular dynamics code designed for high-performance simulation of large biomolecular systems.\nNAMD uses the popular molecular graphics program VMD for simulation setup and trajectory analysis, but is also\nfile-compatible with AMBER, CHARMM, and X-PLOR.\n\n\n\n# System requirements\nBefore running the NGC NAMD container please ensure your system meets the following requirements.\n\n* One of the following container runtimes\n * [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit)\n * [Singularity](https://www.sylabs.io/docs/) >= 3.1\n* One of the following NVIDIA GPU(s)\n * Pascal(sm60)\n * Volta (sm70)\n * Ampere (sm80)\n * Hopper (sm90)\n\n## x86_64\n* CPU with AVX2 instruction support\n* One of the following CUDA driver versions\n * r560 (>=.35.05)\n * \\>= 560.35.05\n\n\n## Running NAMD Examples\n\n#### Download Dataset\n\nThe ApoA1 benchmark consists of 92,224 atoms and has been a standard NAMD cross-platform benchmark for years. Follow\nthe steps below to use the APOA1 input dataset to test the NGC NAMD container.\n\nDownload the APOA1 dataset to your current directory:\n\n```bash\nwget -O - https://gitlab.com/NVHPC/ngc-examples/raw/master/namd/3.0/get_apoa1.sh | bash\n```\n\nTake a moment to inspect the shell script above. In particular, it injects the `CUDASOAintegrate on` in the configuration file, which enables the NAMD 3.0 GPU-resident mode code path.\n\n\nReplace `{input_file}` in the examples below with the path to the apoa1 namd input file:\n\n```bash\n/host_pwd/apoa1/apoa1_nve_cuda_soa.namd\n```\n\n#### Select Tag\n\nSeveral NAMD images are available, depending on your needs. Set the following environment variable which will be used in the example below.\n\n```bash\nexport NAMD_TAG={TAG}\n```\n\nWhere `{TAG}` is `3.0-beta5` or any other tag previously posted on NGC.\n\nSet the executable name depending on the chosen tag.\n\n```bash\nexport NAMD_EXE=namd3\n```\n\n# Running with the NVIDIA Container Toolkit\n\nNGC supports the Docker runtime through [NVIDIA Container Toolkit](https://github.com/NVIDIA/nvidia-container-toolkit) plugin.\n\n```bash\ndocker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG \n```\n\n### Usage\n\nLaunch NAMD with 1 CPU thread, utilizing 1 GPU (simplest way for NAMD versions >= 3.0), on your local machine or single node:\n\n```bash\ndocker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG ${NAMD_EXE} +p1 +devices 0 +setcpuaffinity {input_file}\n```\n\nThe `+p` argument is used to specify the number of cores to be used, and `+devices` specify the GPUs used. To use 2 CPU threads and 2 GPUs:\n\n```bash\ndocker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG ${NAMD_EXE} +p2 +devices 0,1 +setcpuaffinity {input_file}\n```\n\n\nIt is recommanded to run NAMD 2.x for very large systems, multi-node simulations, or Pascal GPUs.\nIn NAMD 3, this can be achieved by setting `CUDASOAintegrate off` or simply not setting it in the configuration file. \nThe input file `/host_pwd/apoa1/apoa1_nve_cuda.namd` (note the lack of `_soa` in file name) in the APOA1 dataset can be used to test this:\n\n```bash\ndocker run --rm --runtime=nvidia --gpus all --ipc=host -v $PWD:/host_pwd -w /host_pwd nvcr.io/hpc/namd:$NAMD_TAG ${NAMD_EXE} +ppn $(nproc) +setcpuaffinity +idlepoll {input_file}\n```\n\nThe `nproc` command is used to specify all available CPU cores should be used. Depending on system setup manually specifying the number of PE's may yield better performance.\n\n## Running with Singularity\n\n```bash\nsingularity run --nv -B $PWD:/host_pwd --pwd /host_pwd nvcr.io/hpc/namd:$NAMD_TAG\n```\n\nLaunch NAMD with 1 CPU thread, utilizing 1 GPU (simplest way for NAMD versions >= 3.0), on your local machine or single node:\n\n```bash\n${SINGULARITY} ${NAMD_EXE} +p1 +devices 0 +setcpuaffinity {input_file}\n```\n\nThe `+p` argument is used to specify the number of cores to be used, and `+devices` specify the GPUs used. To use 2 CPU threads and 2 GPUs:\n\n```bash\n${SINGULARITY} ${NAMD_EXE} +p2 +devices 0,1 +setcpuaffinity {input_file}\n```\n\nIn addition, for very large systems, multi-node simulations, or Pascal GPUs, it is recommanded to run NAMD 2.x. \nIn NAMD 3, this can be achieved by setting `CUDASOAintegrate off` or simply not setting it in the configuration file. \nThe input file `/host_pwd/apoa1/apoa1_nve_cuda.namd` (note the lack of `_soa` in file name) in the APOA1 dataset can be used to test this:\n\n```bash\n${SINGULARITY} ${NAMD_EXE} +ppn $(nproc) +setcpuaffinity +idlepoll {input_file}\n```\n\nThe `nproc` command is used to specify all available CPU cores should be used. Depending on system setup manually specifying the number of PE's may yield better performance.\n\n#### Note: Singularity 3.1.x - 3.5.x\n\nThere is currently a bug in Singularity 3.1.x and 3.2.x causing the `LD_LIBRARY_PATH` to be incorrectly set within the container environment.\nAs a workaround The `LD_LIBRARY_PATH` must be unset before invoking Singularity:\n\n```bash\n$ LD_LIBRARY_PATH=\"\" singularity exec ...\n```\n\n## Running on Base Platform Command\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\n\n**Note: apoa1_nve_cuda_soa.namd needs to be modified to remove the `outputName` parameter due to the nature of the mounted read-only dataset directory.**\n\nUpload the `apoa1` dataset using the command below\n\n```bash\nngc dataset upload --source ./apoa1/ --desc \"NAMD dataset\" namd_dataset\n```\n\n#### Running NAMD on BCP\n\nSingle node on a single GPU running the `apoa1` dataset\n\n```bash\nngc batch run --name \"NAMD_single_gpu\" --priority NORMAL --order 50 --preempt RUNONCE --min-timeslice 0s --total-runtime 0s --ace --instance dgxa100.80g.1.norm --commandline \"namd3 +p1 +devices 0 +setcpuaffinity --outputName /results/namd_output /work/apoa1_nve_cuda_soa.namd\" --result /results/ --image \"hpc/namd:${NAMD_TAG}\" --org --datasetid :/work/\n\n```\n\n#### Known Issue for tag `3.0-beta2`\n\n* Using more than one thread per GPU will result in an error at the end of execution. This bug will be fixed in a coming version. To avoid this error, either use tag `3.0-beta5` or specify both GPU-resident mode (`CUDASOAintegrate on`) and device migration (`DeviceMigration on`).\n\n\n## Suggested Reading\n\n[NAMD](https://www.ks.uiuc.edu/Research/namd/)\n\n[NAMD Manual](https://www.ks.uiuc.edu/Research/namd/cvs/ug.pdf)\n\n[BCP User Guide](https://docs.nvidia.com/base-command-platform/index.html#nvidia-base-command-platform)","NAMD",["D",1738707882796],"UIUC",[284,198,281,279,277,275,273,271,269,267,265,263,260],"/api/image/hpc/-/containers/namd",1788652834,"hasSignedTag",[],[309,310],[],[305],[],{"_92":306,"_127":306,"_128":307,"_130":308},"signed images",{"_132":308,"_127":306},"system",{"_92":315,"_127":315,"_128":316,"_130":131},{"_92":311,"_127":312,"_128":313,"_130":131},"High Performance Computing","uscs_high_performance_computing",{"_132":314,"_127":312},"application","DGX Cloud Supported",{"_132":131,"_127":315},{"_165":1295,"_167":1296},"ngcApps",[1248],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1229":1230,"_1231":1232,"_1233":1234,"_1235":1236,"_1237":328,"_197":352,"_1238":1239,"_1240":51,"_1241":144,"_1242":144,"_1243":1244,"_1245":144,"_1246":1247},"amplitude",{"_1226":1227,"_867":-7,"_1228":26},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1220":1221,"_1222":1223,"_1224":1225},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1107":434,"_1108":1109,"_1110":1111,"_1112":1113},"announcements",[],"consentState",{"_391":1097,"_1098":26,"_1099":26,"_1100":144,"_1101":1102},"consentConfig",{"_373":374,"_375":376,"_377":378,"_379":380,"_381":382,"_383":384,"_385":386,"_387":388,"_389":390,"_391":392},"showContactAdmin",1784668943028,[],[],[366,367,368,369,370,371,372],{"_40":62,"_42":67,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":65,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":62,"_42":63,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":60,"_44":64,"_46":-5,"_48":-5,"_50":51},{"_40":55,"_42":56,"_44":64,"_46":-5,"_48":-5,"_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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We and our advertising partners will use this information to make our websites and the advertising displayed on it, more relevant to your interests.",5,[531,532,533,534,535,536,537,538,539,540,541,542,543,544,545,546,547,548,549,550,551,552],[415,416,417,418,419,420,421,422,423,424,425,426,427,428,429,430,431],{"_432":526,"_92":526,"_91":434,"_405":527},{"_432":522,"_92":522,"_91":434,"_405":523},{"_432":517,"_92":517,"_91":434,"_405":518},{"_432":513,"_92":513,"_91":434,"_405":514},{"_432":508,"_92":508,"_91":434,"_405":509},{"_432":503,"_92":503,"_91":434,"_405":504},{"_432":495,"_92":495,"_91":434,"_405":496},{"_432":487,"_92":487,"_91":434,"_405":488},{"_432":482,"_92":482,"_91":434,"_405":483},{"_432":477,"_92":477,"_91":434,"_405":478},{"_432":472,"_92":472,"_91":434,"_405":473},{"_432":467,"_92":467,"_91":434,"_405":468},{"_432":462,"_92":462,"_91":434,"_405":463},{"_432":455,"_92":455,"_91":434,"_405":456},{"_432":450,"_92":450,"_91":434,"_405":451},{"_432":443,"_92":443,"_91":434,"_405":444},{"_432":433,"_92":433,"_91":434,"_405":435},"hostName","amazon-adsystem.com","",[436,437],{"_261":442,"_439":433,"_440":26,"_91":441},{"_261":438,"_439":433,"_440":26,"_91":441},"ad-privacy","host","isSession","This domain is owned by online retailer Amazon and is used as part of its affiliate marketing programme.","ad-id","doubleclick.net",[445,446],{"_261":449,"_439":443,"_440":26,"_91":448},{"_261":447,"_439":443,"_440":26,"_91":448},"IDE","This domain is owned by Doubleclick (Google). 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This tracking cookie allows a website to link visitor behaviour to the recipient of an email marketing campaign, to measure campaign effectiveness.","investor.nvidia.com","This cookie is associated with Eventbrite and is used to deliver content tailored to the end user's interests and improve content creation. This cookie is also used for event-booking purposes.","_gat_gtag_UA_140825383_2","Google Analytics","_rdt_uuid","This cookie is set by Reddit and is used for tracking the views of embedded videos and performance of the advertisements.","catalog.ngc.nvidia.com","_uetsid","This cookie is used by Bing to determine what ads should be shown that may be relevant to the end user perusing the site. 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If you do not allow these cookies, then some or all of these services may not function properly.",[623,624,625,626,627,628,629,630,631,632,633,634,635,636,637,638,639,640,641,642,643,644,645,646,647],[594,595,596,597,598],{"_432":508,"_92":508,"_91":434,"_405":618},{"_432":482,"_92":482,"_91":434,"_405":613},{"_432":608,"_92":608,"_91":434,"_405":609},{"_432":603,"_92":603,"_91":434,"_405":604},{"_432":599,"_92":599,"_91":434,"_405":600},"nvidia.partners",[601],{"_261":602,"_439":599,"_440":26,"_91":476},"edge","app.hushly.com",[605],{"_261":606,"_439":603,"_440":26,"_91":607},"*CORS","This domain is associated with Hushly, a platform that provides lead conversion solutions for content marketing by using personalization and content engagement strategies.","github.com",[610],{"_261":611,"_439":608,"_440":26,"_91":612},"_octo","This domain is owned by Github. 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These cookies may store a unique ID so that our system will remember you when you return. Information collected with these cookies is used to measure and find ways to improve website performance.",3,[707,708,709,710,711,712,713,714,715,716,717,718,719,720,721,722,723,724,725,726,727,728,729,730,731,732,733,734,735],[687,688,689,690],{"_432":702,"_92":702,"_91":434,"_405":703},{"_432":697,"_92":697,"_91":434,"_405":698},{"_432":495,"_92":495,"_91":434,"_405":694},{"_432":608,"_92":608,"_91":434,"_405":691},[692],{"_261":693,"_439":608,"_440":144,"_91":612},"_gh_sess",[695],{"_261":696,"_439":495,"_440":144,"_91":502},"YSC","bizible.com",[699],{"_261":700,"_439":697,"_440":26,"_91":701},"_BUID","Bizible is a marketing attribution software that tracks the performance of marketing efforts and connects them to revenue, primarily used for B2B environments.","a.usbrowserspeed.com",[704],{"_261":705,"_439":702,"_440":26,"_91":706},"tuid","This domain is owned by Ookla which provides internet speed testing services through its Speedtest application.",{"_261":777,"_439":554,"_440":26,"_91":778},{"_261":762,"_439":776,"_440":144,"_91":763},{"_261":745,"_439":557,"_440":26,"_91":669},{"_261":745,"_439":657,"_440":26,"_91":669},{"_261":774,"_439":554,"_440":26,"_91":775},{"_261":762,"_439":560,"_440":144,"_91":763},{"_261":772,"_439":554,"_440":26,"_91":773},{"_261":762,"_439":557,"_440":144,"_91":763},{"_261":770,"_439":656,"_440":26,"_91":771},{"_261":762,"_439":678,"_440":144,"_91":763},{"_261":742,"_439":557,"_440":26,"_91":744},{"_261":768,"_439":554,"_440":144,"_91":769},{"_261":766,"_439":554,"_440":26,"_91":767},{"_261":764,"_439":554,"_440":144,"_91":765},{"_261":762,"_439":652,"_440":144,"_91":763},{"_261":760,"_439":554,"_440":26,"_91":761},{"_261":758,"_439":554,"_440":26,"_91":759},{"_261":756,"_439":554,"_440":144,"_91":757},{"_261":754,"_439":661,"_440":26,"_91":755},{"_261":742,"_439":652,"_440":26,"_91":744},{"_261":752,"_439":554,"_440":26,"_91":753},{"_261":750,"_439":554,"_440":144,"_91":751},{"_261":748,"_439":554,"_440":26,"_91":749},{"_261":746,"_439":554,"_440":26,"_91":747},{"_261":745,"_439":554,"_440":26,"_91":669},{"_261":742,"_439":743,"_440":26,"_91":744},{"_261":740,"_439":554,"_440":26,"_91":741},{"_261":738,"_439":554,"_440":144,"_91":739},{"_261":736,"_439":554,"_440":26,"_91":737},"_ga_xxxxxxxxxx","Used by Google Analytics to identify and track an individual session with your device.","s_ppvl","This cookie is set by Adobe Analytics to store information on the percentage of the page displayed to the end user.","coveo_visitorId","This cookie name is associated with Coveo which is used to store a unique user ID for marketing purposes and to track the number of distinct users who submitted a query or clicked an item, counting them only once even if they did the same query or clicked the same item over the course of many visits.","_dd_s","marketplace.nvidia.com","Datadog Cookie used to group all events generated from a unique user session across multiple pages. It contains the current session ID, whether the session is excluded due to sampling, and the expiration date of the session. The cookie is extended for an extra 15 minutes every time the user interacts with the website, up to the maximum user session duration (4 hours).","nvweb_A","_ga","This cookie name is associated with Google Universal Analytics - which is a significant update to Google's more commonly used analytics service. This cookie is used to distinguish unique users by assigning a randomly generated number as a client identifier. It is included in each page request in a site and used to calculate visitor, session and campaign data for the sites analytics reports. By default it is set to expire after 2 years, although this is customisable by website owners.","_uetvid","This is a cookie utilised by Microsoft Bing Ads and is a tracking cookie. It allows us to engage with a user that has previously visited our website.","tp","This cookie is associated with Adobe Analytics site tracking. It registers if the website's scroll-depth detection is active. This function remembers how far the user has scrolled on the current session across the website's sub-pages.","_hly_vid","This cookie _hly_vid is used to record website and server performance with each visit to the site. The data is aggregated and analyzed; therefore, it remains anonymous upon use.","_session_id","This cookie creates an interim session ID used as an in-session user ID.\n","s_cc","This cookie is associated with the Adobe Site Catalyst. It determines whether cookies are enabled in the web browser.","s_ecid","Adobe Analytics cookie containing the the Adobe Experience Cloud 1st party visitor id if the Analytics AMCV visitor id is not found.","_hjSessionUser_xxxxxx","Hotjar cookie that is set when a user first lands on a page with the Hotjar script. 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NAMD

System requirements

x86_64

Running NAMD Examples

Download Dataset

Select Tag

Running with the NVIDIA Container Toolkit

Usage

Running with Singularity

Note: Singularity 3.1.x - 3.5.x

Running on Base Platform Command

Uploading the Dataset to BCP

Running NAMD on BCP

Known Issue for tag 3.0-beta2

Suggested Reading

Known Issue for tag `3.0-beta2`