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DTSTART:19700308T020000
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DTSTAMP:20181221T160731Z
LOCATION:D161
DTSTART;TZID=America/Chicago:20181116T092000
DTEND;TZID=America/Chicago:20181116T094000
UID:submissions.supercomputing.org_SC18_sess153_ws_espt102@linklings.com
SUMMARY:Understanding the Scalability of Molecular Simulation Using Empiri
 cal Performance Modeling
DESCRIPTION:Workshop\nPerformance, Productivity, Workshop Reg Pass\n\nUnde
 rstanding the Scalability of Molecular Simulation Using Empirical Performa
 nce Modeling\n\nShudler, Vrabec, Wolf\n\nMolecular dynamics (MD) simulatio
 n allows for the study of static and dynamic properties of molecular ensem
 bles at various molecular scales, from monatomics to macromolecules such a
 s proteins and nucleic acids. It has applications in biology, materials sc
 ience, biochemistry, and biophysics. Recent developments in simulation tec
 hniques 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 calculat
 e 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 stu
 dy 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 mode
 ls can help users better understand how to scale the simulation with minim
 al increase in execution time.
URL:https://sc18.supercomputing.org/presentation/?id=ws_espt102&sess=sess1
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