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Title: LAMMPS Tutorial Thursday AM.


Abstract not provided.

Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
Report Number(s):
DOE Contract Number:
Resource Type:
Resource Relation:
Conference: Proposed for presentation at the Molecular Dynamics for Modern Materials with LAMMPS/Molecular dynamics of materials from assembly to fracture held August 15-19, 2016 in Philadelphia, PA.
Country of Publication:
United States

Citation Formats

Moore, Stan Gerald. LAMMPS Tutorial Thursday AM.. United States: N. p., 2016. Web.
Moore, Stan Gerald. LAMMPS Tutorial Thursday AM.. United States.
Moore, Stan Gerald. 2016. "LAMMPS Tutorial Thursday AM.". United States. doi:.
title = {LAMMPS Tutorial Thursday AM.},
author = {Moore, Stan Gerald},
abstractNote = {Abstract not provided.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2016,
month = 8

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  • Abstract not provided.
  • No abstract prepared.
  • LAMMPS is a classical molecular dynamics code, and an acronym for Large-scale Atomic/Molecular Massively Parallel Simulator. LAMMPS has potentials for soft materials (biomolecules, polymers) and solid-state materials (metals, semiconductors) and coarse-grained or mesoscopic systems. It can be used to model atoms or, more generically, as a parallel particle simulator at the atomic, meso, or continuum scale. LAMMPS runs on single processors or in parallel using message-passing techniques and a spatial-decomposition of the simulation domain. The code is designed to be easy to modify or extend with new functionality.
  • The MGPT potential has been implemented as a drop in package to the general molecular dynamics code LAMMPS. We implement an improved communication scheme that shrinks the communication layer thickness, and increases the load balancing. This results in unprecedented strong scaling, and speedup continuing beyond 1/8 atom/core. In addition, we have optimized the small matrix linear algebra with generic blocking (for all processors) and specific SIMD intrinsics for vectorization on Intel, AMD, and BlueGene CPUs.