Ultrafast scalable parallel algorithm for the radial distribution function histogramming using MPI maps

In the present paper, an ultrafast and scalable parallel program for the Radial Pair Function (RPF) is presented via pure Message Passing Interface (MPI) paradigm. The parallel code computes the radial distribution function for the single-component as well as multi-component systems. The single-comp...

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Vydáno v:The Journal of supercomputing Ročník 73; číslo 4; s. 1629 - 1653
Hlavní autoři: Kouetcha, Daniella Nguemalieu, Ramézani, Hamidréza, Cohaut, Nathalie
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.04.2017
Springer Nature B.V
Springer Verlag
Témata:
Algorithms
C (programming language)
Compilers
Computer Science
Computer simulation
Computing time
Condensed Matter
Distribution functions
Interpreters
Message passing
Optimization
Parallel programming
Physics
Processor Architectures
Programming Languages
Radial distribution
Single wall carbon nanotubes
Stochastic approach
Parallel computing
Statistical mechanics
Radial distribution function
Monte Carlo simulation
MPI
ISSN:0920-8542, 1573-0484
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Shrnutí:In the present paper, an ultrafast and scalable parallel program for the Radial Pair Function (RPF) is presented via pure Message Passing Interface (MPI) paradigm. The parallel code computes the radial distribution function for the single-component as well as multi-component systems. The single-component and multi-component systems have been extracted for benchmarking purposes by means of user-written codes in C++ for the Graphite structure and MPI C++ for hydrogen adsorption via Grand Canonical Monte Carlo (GCMC) in the Single-Walled Carbon Nanotube (SWNT), respectively. The speedup and efficiency curves substantiate an excellent performance in terms of computing time and computation size as well. Additionally, the mentioned MPI paradigms are nearly five times (single-component systems) and two times (multi-component systems) faster than the relevant parallel codes using a machine with 48 CPUs and NVIDIA Quadro K5200/PCIe/SSE2. Some conclusions and outlooks pertaining to the numerical implementations, algorithm optimization involving the space decomposition idea have been discussed and provided.
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ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-016-1854-0