Hybrid Codes for Atomistic Simulations on the Desmos Supercomputer: GPU-acceleration, Scalability and Parallel I/O

Publication typeBook Chapter
Publication date2018-12-31
scimago Q4
SJR0.182
CiteScore1.1
Impact factor
ISSN18650929, 18650937
Abstract
In this paper, we compare different GPU accelerators and algorithms for classical molecular dynamics using LAMMPS and GROMACS codes. BigDFT is considered as an example of the modern ab initio code that implements the density functional theory algorithms in the wavelet basis and uses effectively GPU acceleration. Efficiency of distributed storage managed by the BeeGFS parallel file system is analysed with respect to saving of large molecular-dynamics trajectories. Results have been obtained using the Desmos supercomputer in JIHT RAS.
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Journal of Chemical Physics
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Kondratyuk N. et al. Hybrid Codes for Atomistic Simulations on the Desmos Supercomputer: GPU-acceleration, Scalability and Parallel I/O // Communications in Computer and Information Science. 2018. pp. 218-229.
GOST all authors (up to 50) Copy
Kondratyuk N., Smirnov G., Stegailov V. Hybrid Codes for Atomistic Simulations on the Desmos Supercomputer: GPU-acceleration, Scalability and Parallel I/O // Communications in Computer and Information Science. 2018. pp. 218-229.
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RIS Copy
TY - GENERIC
DO - 10.1007/978-3-030-05807-4_19
UR - http://link.springer.com/10.1007/978-3-030-05807-4_19
TI - Hybrid Codes for Atomistic Simulations on the Desmos Supercomputer: GPU-acceleration, Scalability and Parallel I/O
T2 - Communications in Computer and Information Science
AU - Kondratyuk, Nikolay
AU - Smirnov, Grigory
AU - Stegailov, Vladimir
PY - 2018
DA - 2018/12/31
PB - Springer Nature
SP - 218-229
SN - 1865-0929
SN - 1865-0937
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@incollection{2018_Kondratyuk,
author = {Nikolay Kondratyuk and Grigory Smirnov and Vladimir Stegailov},
title = {Hybrid Codes for Atomistic Simulations on the Desmos Supercomputer: GPU-acceleration, Scalability and Parallel I/O},
publisher = {Springer Nature},
year = {2018},
pages = {218--229},
month = {dec}
}