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Title: LAMMPS on CMake

Authors:
ORCiD logo [1]
  1. Los Alamos National Laboratory
Publication Date:
Research Org.:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org.:
USDOE Office of Science (SC). Advanced Scientific Computing Research (ASCR) (SC-21)
OSTI Identifier:
1418777
Report Number(s):
LA-UR-18-20583
DOE Contract Number:
AC52-06NA25396
Resource Type:
Conference
Resource Relation:
Conference: LAMMPS Workshop ; 2017-08-02 - 2017-08-02 ; Albuquerque, Minnesota, United States
Country of Publication:
United States
Language:
English
Subject:
Computer Science

Citation Formats

Junghans, Christoph. LAMMPS on CMake. United States: N. p., 2018. Web.
Junghans, Christoph. LAMMPS on CMake. United States.
Junghans, Christoph. 2018. "LAMMPS on CMake". United States. doi:. https://www.osti.gov/servlets/purl/1418777.
@article{osti_1418777,
title = {LAMMPS on CMake},
author = {Junghans, Christoph},
abstractNote = {},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2018,
month = 1
}

Conference:
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  • 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.
  • Abstract not provided.