Scalable molecular dynamics on CPU and GPU architectures with NAMD
NAMD is a molecular dynamics program designed for high-performance simulations of very large biological objects on CPU- and GPU-based architectures. NAMD offers scalable performance on petascale parallel supercomputers consisting of hundreds of thousands of cores, as well as on inexpensive commodity...
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Veröffentlicht in: | Journal of Chemical Physics 2020-07, Vol.153 (4), p.044130-044130 |
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creator | Phillips, James C. Hardy, David J. Maia, Julio D. C. Stone, John E. Ribeiro, João V. Bernardi, Rafael C. Buch, Ronak Fiorin, Giacomo Hénin, Jérôme Jiang, Wei McGreevy, Ryan Melo, Marcelo C. R. Radak, Brian K. Skeel, Robert D. Singharoy, Abhishek Wang, Yi Roux, Benoît Aksimentiev, Aleksei Luthey-Schulten, Zaida Kalé, Laxmikant V. Schulten, Klaus Chipot, Christophe Tajkhorshid, Emad |
description | NAMD is a molecular dynamics program designed for high-performance simulations of very
large biological objects on CPU- and GPU-based architectures. NAMD offers scalable
performance on petascale parallel supercomputers consisting of hundreds of thousands of
cores, as well as on inexpensive commodity clusters commonly found in academic
environments. It is written in C++ and leans on Charm++ parallel objects for optimal
performance on low-latency architectures. NAMD is a versatile, multipurpose code that
gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic
ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force
fields. Here, we review the main features of NAMD that allow both equilibrium and
enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe
the underlying concepts utilized by NAMD and their implementation, most notably for
handling long-range electrostatics; controlling the temperature, pressure, and pH;
applying external potentials on tailored grids; leveraging massively parallel resources in
multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical
descriptions. We detail the variety of options offered by NAMD for enhanced-sampling
simulations aimed at determining free-energy differences of either alchemical or
geometrical transformations and outline their applicability to specific problems. Last, we
discuss the roadmap for the development of NAMD and our current efforts toward achieving
optimal performance on GPU-based architectures, for pushing back the limitations that have
prevented biologically realistic billion-atom objects to be fruitfully simulated, and for
making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD
is distributed free of charge with its source code at www.ks.uiuc.edu. |
doi_str_mv | 10.1063/5.0014475 |
format | Article |
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large biological objects on CPU- and GPU-based architectures. NAMD offers scalable
performance on petascale parallel supercomputers consisting of hundreds of thousands of
cores, as well as on inexpensive commodity clusters commonly found in academic
environments. It is written in C++ and leans on Charm++ parallel objects for optimal
performance on low-latency architectures. NAMD is a versatile, multipurpose code that
gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic
ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force
fields. Here, we review the main features of NAMD that allow both equilibrium and
enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe
the underlying concepts utilized by NAMD and their implementation, most notably for
handling long-range electrostatics; controlling the temperature, pressure, and pH;
applying external potentials on tailored grids; leveraging massively parallel resources in
multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical
descriptions. We detail the variety of options offered by NAMD for enhanced-sampling
simulations aimed at determining free-energy differences of either alchemical or
geometrical transformations and outline their applicability to specific problems. Last, we
discuss the roadmap for the development of NAMD and our current efforts toward achieving
optimal performance on GPU-based architectures, for pushing back the limitations that have
prevented biologically realistic billion-atom objects to be fruitfully simulated, and for
making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD
is distributed free of charge with its source code at www.ks.uiuc.edu.</description><identifier>ISSN: 0021-9606</identifier><identifier>EISSN: 1089-7690</identifier><identifier>DOI: 10.1063/5.0014475</identifier><identifier>PMID: 32752662</identifier><identifier>CODEN: JCPSA6</identifier><language>eng</language><publisher>United States: American Institute of Physics</publisher><subject>Biological Physics ; Chemical Sciences ; high-performance computing ; Molecular dynamics simulation ; or physical chemistry ; Physics ; statistical mechanics ; Theoretical and</subject><ispartof>Journal of Chemical Physics, 2020-07, Vol.153 (4), p.044130-044130</ispartof><rights>Author(s)</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><rights>Copyright © 2020 Author(s)</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c506t-f4219e12605d84f9c9cf0e34924f24baf33b2b1ae435bb00d219a8a03bead1663</citedby><cites>FETCH-LOGICAL-c506t-f4219e12605d84f9c9cf0e34924f24baf33b2b1ae435bb00d219a8a03bead1663</cites><orcidid>0000-0002-5254-2712 ; 0000-0001-8628-5972 ; 0000-0002-2296-3591 ; 0000-0001-7707-6247 ; 0000-0002-9000-2397 ; 0000-0003-0675-5116 ; 0000-0002-8793-8645 ; 0000-0003-0758-2026 ; 0000-0002-4174-8790 ; 0000-0001-7215-762X ; 0000-0001-6901-1646 ; 0000-0001-8533-1367 ; 0000-0003-2540-4098 ; 0000-0001-9749-8367 ; 0000-0001-9673-8445 ; 0000-0002-9122-1698 ; 0000-0002-4807-8988 ; 0000-0001-8434-1010 ; 0000-0001-7192-9632 ; 0000-0003-0579-1094 ; 0000-0002-0353-4126 ; 0000-0002-6042-8442 ; 0000000241748790 ; 0000000252542712 ; 0000000260428442 ; 0000000197498367 ; 0000000290002397 ; 0000000196738445 ; 0000000306755116 ; 0000000171929632 ; 0000000184341010 ; 0000000325404098 ; 0000000203534126 ; 0000000177076247 ; 0000000305791094 ; 0000000186285972 ; 0000000291221698 ; 0000000307582026 ; 0000000169011646 ; 0000000185331367 ; 0000000222963591 ; 000000017215762X ; 0000000287938645 ; 0000000248078988 ; 0000-0002-2683-9889</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://pubs.aip.org/jcp/article-lookup/doi/10.1063/5.0014475$$EHTML$$P50$$Gscitation$$H</linktohtml><link.rule.ids>230,314,780,784,794,885,4512,27924,27925,76384</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32752662$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://hal.science/hal-03032818$$DView record in HAL$$Hfree_for_read</backlink><backlink>$$Uhttps://www.osti.gov/biblio/1660560$$D View this record in Osti.gov$$Hfree_for_read</backlink></links><search><creatorcontrib>Phillips, James C.</creatorcontrib><creatorcontrib>Hardy, David J.</creatorcontrib><creatorcontrib>Maia, Julio D. C.</creatorcontrib><creatorcontrib>Stone, John E.</creatorcontrib><creatorcontrib>Ribeiro, João V.</creatorcontrib><creatorcontrib>Bernardi, Rafael C.</creatorcontrib><creatorcontrib>Buch, Ronak</creatorcontrib><creatorcontrib>Fiorin, Giacomo</creatorcontrib><creatorcontrib>Hénin, Jérôme</creatorcontrib><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>McGreevy, Ryan</creatorcontrib><creatorcontrib>Melo, Marcelo C. R.</creatorcontrib><creatorcontrib>Radak, Brian K.</creatorcontrib><creatorcontrib>Skeel, Robert D.</creatorcontrib><creatorcontrib>Singharoy, Abhishek</creatorcontrib><creatorcontrib>Wang, Yi</creatorcontrib><creatorcontrib>Roux, Benoît</creatorcontrib><creatorcontrib>Aksimentiev, Aleksei</creatorcontrib><creatorcontrib>Luthey-Schulten, Zaida</creatorcontrib><creatorcontrib>Kalé, Laxmikant V.</creatorcontrib><creatorcontrib>Schulten, Klaus</creatorcontrib><creatorcontrib>Chipot, Christophe</creatorcontrib><creatorcontrib>Tajkhorshid, Emad</creatorcontrib><creatorcontrib>Argonne National Lab. (ANL), Argonne, IL (United States)</creatorcontrib><title>Scalable molecular dynamics on CPU and GPU architectures with NAMD</title><title>Journal of Chemical Physics</title><addtitle>J Chem Phys</addtitle><description>NAMD is a molecular dynamics program designed for high-performance simulations of very
large biological objects on CPU- and GPU-based architectures. NAMD offers scalable
performance on petascale parallel supercomputers consisting of hundreds of thousands of
cores, as well as on inexpensive commodity clusters commonly found in academic
environments. It is written in C++ and leans on Charm++ parallel objects for optimal
performance on low-latency architectures. NAMD is a versatile, multipurpose code that
gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic
ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force
fields. Here, we review the main features of NAMD that allow both equilibrium and
enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe
the underlying concepts utilized by NAMD and their implementation, most notably for
handling long-range electrostatics; controlling the temperature, pressure, and pH;
applying external potentials on tailored grids; leveraging massively parallel resources in
multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical
descriptions. We detail the variety of options offered by NAMD for enhanced-sampling
simulations aimed at determining free-energy differences of either alchemical or
geometrical transformations and outline their applicability to specific problems. Last, we
discuss the roadmap for the development of NAMD and our current efforts toward achieving
optimal performance on GPU-based architectures, for pushing back the limitations that have
prevented biologically realistic billion-atom objects to be fruitfully simulated, and for
making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD
is distributed free of charge with its source code at www.ks.uiuc.edu.</description><subject>Biological Physics</subject><subject>Chemical Sciences</subject><subject>high-performance computing</subject><subject>Molecular dynamics simulation</subject><subject>or physical chemistry</subject><subject>Physics</subject><subject>statistical mechanics</subject><subject>Theoretical and</subject><issn>0021-9606</issn><issn>1089-7690</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><recordid>eNp9kc1u1DAUha0K1A6lC14ARawKUsr1b5JNpWGAFmn4kdquLcdxiFFiT-1kUN8eRzO0BQlWV7r-9F0fHYReYDjDIOhbfgaAGSv4AVpgKKu8EBU8QQsAgvNKgDhCz2L8AYkqCDtER5QUnAhBFujdlVa9qnuTDb43eupVyJo7pwarY-Zdtvp2kynXZBfzDLqzo9HjFEzMftqxy74sP79_jp62qo_mZD-P0c3HD9ery3z99eLTarnONQcx5i0juDKYCOBNydpKV7oFQ1lFWEtYrVpKa1JjZRjldQ3QJFyVCmhtVIOFoMfofOfdTPVgGm3cGFQvN8EOKtxJr6z888XZTn73W1nQipeUJcGrncDH0cqo5yyd9s6lSDJdAC4gQa93UPeX-3K5lvMOKFBS4nKLE3u6_1Hwt5OJoxxs1KbvlTN-ipIwCoIVRNAHrQ4-xmDaezcGOZcoudyXmNiXj4Pek79bS8CbHTBnUKP17r-2f8JbHx5AuWla-gth0bCJ</recordid><startdate>20200728</startdate><enddate>20200728</enddate><creator>Phillips, James C.</creator><creator>Hardy, David J.</creator><creator>Maia, Julio D. 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C. ; Stone, John E. ; Ribeiro, João V. ; Bernardi, Rafael C. ; Buch, Ronak ; Fiorin, Giacomo ; Hénin, Jérôme ; Jiang, Wei ; McGreevy, Ryan ; Melo, Marcelo C. R. ; Radak, Brian K. ; Skeel, Robert D. ; Singharoy, Abhishek ; Wang, Yi ; Roux, Benoît ; Aksimentiev, Aleksei ; Luthey-Schulten, Zaida ; Kalé, Laxmikant V. ; Schulten, Klaus ; Chipot, Christophe ; Tajkhorshid, Emad</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c506t-f4219e12605d84f9c9cf0e34924f24baf33b2b1ae435bb00d219a8a03bead1663</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>Biological Physics</topic><topic>Chemical Sciences</topic><topic>high-performance computing</topic><topic>Molecular dynamics simulation</topic><topic>or physical chemistry</topic><topic>Physics</topic><topic>statistical mechanics</topic><topic>Theoretical and</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Phillips, James C.</creatorcontrib><creatorcontrib>Hardy, David J.</creatorcontrib><creatorcontrib>Maia, Julio D. 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(ANL), Argonne, IL (United States)</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>Hyper Article en Ligne (HAL) (Open Access)</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Journal of Chemical Physics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Phillips, James C.</au><au>Hardy, David J.</au><au>Maia, Julio D. C.</au><au>Stone, John E.</au><au>Ribeiro, João V.</au><au>Bernardi, Rafael C.</au><au>Buch, Ronak</au><au>Fiorin, Giacomo</au><au>Hénin, Jérôme</au><au>Jiang, Wei</au><au>McGreevy, Ryan</au><au>Melo, Marcelo C. R.</au><au>Radak, Brian K.</au><au>Skeel, Robert D.</au><au>Singharoy, Abhishek</au><au>Wang, Yi</au><au>Roux, Benoît</au><au>Aksimentiev, Aleksei</au><au>Luthey-Schulten, Zaida</au><au>Kalé, Laxmikant V.</au><au>Schulten, Klaus</au><au>Chipot, Christophe</au><au>Tajkhorshid, Emad</au><aucorp>Argonne National Lab. (ANL), Argonne, IL (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Scalable molecular dynamics on CPU and GPU architectures with NAMD</atitle><jtitle>Journal of Chemical Physics</jtitle><addtitle>J Chem Phys</addtitle><date>2020-07-28</date><risdate>2020</risdate><volume>153</volume><issue>4</issue><spage>044130</spage><epage>044130</epage><pages>044130-044130</pages><issn>0021-9606</issn><eissn>1089-7690</eissn><coden>JCPSA6</coden><abstract>NAMD is a molecular dynamics program designed for high-performance simulations of very
large biological objects on CPU- and GPU-based architectures. NAMD offers scalable
performance on petascale parallel supercomputers consisting of hundreds of thousands of
cores, as well as on inexpensive commodity clusters commonly found in academic
environments. It is written in C++ and leans on Charm++ parallel objects for optimal
performance on low-latency architectures. NAMD is a versatile, multipurpose code that
gathers state-of-the-art algorithms to carry out simulations in apt thermodynamic
ensembles, using the widely popular CHARMM, AMBER, OPLS, and GROMOS biomolecular force
fields. Here, we review the main features of NAMD that allow both equilibrium and
enhanced-sampling molecular dynamics simulations with numerical efficiency. We describe
the underlying concepts utilized by NAMD and their implementation, most notably for
handling long-range electrostatics; controlling the temperature, pressure, and pH;
applying external potentials on tailored grids; leveraging massively parallel resources in
multiple-copy simulations; and hybrid quantum-mechanical/molecular-mechanical
descriptions. We detail the variety of options offered by NAMD for enhanced-sampling
simulations aimed at determining free-energy differences of either alchemical or
geometrical transformations and outline their applicability to specific problems. Last, we
discuss the roadmap for the development of NAMD and our current efforts toward achieving
optimal performance on GPU-based architectures, for pushing back the limitations that have
prevented biologically realistic billion-atom objects to be fruitfully simulated, and for
making large-scale simulations less expensive and easier to set up, run, and analyze. NAMD
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fulltext | fulltext |
identifier | ISSN: 0021-9606 |
ispartof | Journal of Chemical Physics, 2020-07, Vol.153 (4), p.044130-044130 |
issn | 0021-9606 1089-7690 |
language | eng |
recordid | cdi_scitation_primary_10_1063_5_0014475 |
source | AIP Journals Complete; Alma/SFX Local Collection |
subjects | Biological Physics Chemical Sciences high-performance computing Molecular dynamics simulation or physical chemistry Physics statistical mechanics Theoretical and |
title | Scalable molecular dynamics on CPU and GPU architectures with NAMD |
url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-02T09%3A34%3A52IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_scita&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Scalable%20molecular%20dynamics%20on%20CPU%20and%20GPU%20architectures%20with%20NAMD&rft.jtitle=Journal%20of%20Chemical%20Physics&rft.au=Phillips,%20James%20C.&rft.aucorp=Argonne%20National%20Lab.%20(ANL),%20Argonne,%20IL%20(United%20States)&rft.date=2020-07-28&rft.volume=153&rft.issue=4&rft.spage=044130&rft.epage=044130&rft.pages=044130-044130&rft.issn=0021-9606&rft.eissn=1089-7690&rft.coden=JCPSA6&rft_id=info:doi/10.1063/5.0014475&rft_dat=%3Cproquest_scita%3E2430647263%3C/proquest_scita%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2430647263&rft_id=info:pmid/32752662&rfr_iscdi=true |