Understanding the Scalability of Molecular Simulation Using Empirical Performance Modeling

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dc.contributor.authorShudler, Sergei
dc.contributor.authorVrabec, Jadran
dc.contributor.authorWolf, Felix
dc.date.accessioned2019-12-05T13:09:27Z
dc.date.available2019-12-05T13:09:27Z
dc.date.issued2019-04-24
dc.descriptionThe final authenticated publication is available online at https://doi.org/10.1007/978-3-030-17872-7_8.en
dc.description.abstractMolecular dynamics (MD) simulation allows for the study of static and dynamic properties of molecular ensembles at various molecular scales, from monatomics to macromolecules such as proteins and nucleic acids. It has applications in biology, materials science, biochemistry, and biophysics. Recent developments in simulation techniques spurred the emergence of the computational molecular engineering (CME) field, which focuses specifically on the needs of industrial users in engineering. Within CME, the simulation code ms2 allows users to calculate thermodynamic properties of bulk fluids. It is a parallel code that aims to scale the temporal range of the simulation while keeping the execution time minimal. In this paper, we use empirical performance modeling to study the impact of simulation parameters on the execution time. Our approach is a systematic workflow that can be used as a blue-print in other fields that aim to scale their simulation codes. We show that the generated models can help users better understand how to scale the simulation with minimal increase in execution time.en
dc.description.sponsorshipBMBF, 01IH16008D, Verbundprojekt: TaLPas - Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulationen
dc.description.sponsorshipDFG, 323299120, ExtraPeak - Automatische Leistungsmodellierung von HPC-Anwendungen mit multiplen Modellparameternen
dc.identifier.eissn1611-3349
dc.identifier.isbn978-3-030-17872-7
dc.identifier.isbn978-3-030-17871-0
dc.identifier.issn0302-9743
dc.identifier.urihttps://depositonce.tu-berlin.de/handle/11303/10443
dc.identifier.urihttp://dx.doi.org/10.14279/depositonce-9395
dc.language.isoenen
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subject.ddc000 Informatik, Informationswissenschaft, allgemeine Werkede
dc.subject.ddc621 Angewandte Physikde
dc.subject.othermolecular dynamicsen
dc.subject.otherperformance modelingen
dc.subject.otherparallel programmingen
dc.titleUnderstanding the Scalability of Molecular Simulation Using Empirical Performance Modelingen
dc.typeConference Objecten
dc.type.versionacceptedVersionen
dcterms.bibliographicCitation.doi10.1007/978-3-030-17872-7_8en
dcterms.bibliographicCitation.editorBhatele, Abhinav
dcterms.bibliographicCitation.editorBoehme, David
dcterms.bibliographicCitation.editorLevine, Joshua A.
dcterms.bibliographicCitation.editorMalony, Allen D.
dcterms.bibliographicCitation.editorSchulz, Martin
dcterms.bibliographicCitation.originalpublishernameSpringeren
dcterms.bibliographicCitation.originalpublisherplaceChamen
dcterms.bibliographicCitation.pageend143en
dcterms.bibliographicCitation.pagestart125en
dcterms.bibliographicCitation.proceedingstitleProgramming and Performance Visualization Tools : ESPT 2017, ESPT 2018, VPA 2017, VPA 2018en
tub.accessrights.dnbfreeen
tub.affiliationFak. 3 Prozesswissenschaften::Inst. Prozess- und Verfahrenstechnik::FG Thermodynamik und Thermische Verfahrenstechnikde
tub.affiliation.facultyFak. 3 Prozesswissenschaftende
tub.affiliation.groupFG Thermodynamik und Thermische Verfahrenstechnikde
tub.affiliation.instituteInst. Prozess- und Verfahrenstechnikde
tub.publisher.universityorinstitutionTechnische Universität Berlinen
tub.series.issuenumber11027en
tub.series.nameLecture Notes in Computer Scienceen

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