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Titolo:
Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins, and crystals
Autore:
Lee, JY; Pillardy, J; Czaplewski, C; Arnautova, Y; Ripoll, DR; Liwo, A; Gibson, KD; Wawak, RJ; Scheraga, HA;
Indirizzi:
Cornell Univ, Baker Lab Chem & Chem Biol, Ithaca, NY 14853 USA Cornell Univ Ithaca NY USA 14853 b Chem & Chem Biol, Ithaca, NY 14853 USA Cornell Theory Ctr, Ithaca, NY 14853 USA Cornell Theory Ctr Ithaca NY USA14853 l Theory Ctr, Ithaca, NY 14853 USA Univ Gdansk, Fac Chem, PL-80952 Gdansk, Poland Univ Gdansk Gdansk PolandPL-80952 sk, Fac Chem, PL-80952 Gdansk, Poland
Titolo Testata:
COMPUTER PHYSICS COMMUNICATIONS
fascicolo: 1-2, volume: 128, anno: 2000,
pagine: 399 - 411
SICI:
0010-4655(200006)128:1-2<399:EPAIGO>2.0.ZU;2-B
Fonte:
ISI
Lingua:
ENG
Soggetto:
RESIDUE FORCE-FIELD; MONTE-CARLO METHOD; MULTIPLE-MINIMA PROBLEM; MEMBRANE-BOUND PORTION; HYDROGEN-BOND INTERACTIONS; OCCURRING AMINO-ACIDS; CONFORMATIONAL-ANALYSIS; NONBONDED INTERACTIONS; STRUCTURE SIMULATIONS; STRUCTURE PREDICTION;
Keywords:
crystal structure prediction; genetic algorithms; global optimization; Monte Carlo methods; parallel algorithms; protein structure prediction;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Physical, Chemical & Earth Sciences
Citazioni:
38
Recensione:
Indirizzi per estratti:
Indirizzo: Scheraga, HA Cornell Univ, Baker Lab Chem & Chem Biol, Ithaca, NY 14853 USA Cornell Univ Ithaca NY USA 14853 Biol, Ithaca, NY 14853 USA
Citazione:
J.Y. Lee et al., "Efficient parallel algorithms in global optimization of potential energy functions for peptides, proteins, and crystals", COMP PHYS C, 128(1-2), 2000, pp. 399-411

Abstract

Global optimization is playing an increasing role in physics, chemistry, and biophysical chemistry. One of the most important applications of global optimization is to find the global minima of the potential energy of molecules or molecular assemblies, such as crystals. The solution of this problemtypically requires huge computational effort. Even the fastest processor available is not fast enough to carry out this kind of computation in real time for the problems of real interest, e.g., protein and crystal structure prediction. One way to circumvent this problem is to take advantage of massively parallel computing. In this paper, we provide several examples of parallel implementations of global optimization algorithms developed in our laboratory. All of these examples follow the master/worker approach. Most of the methods are parallelized on the algorithmic (coarse-grain) level and one example of fine-grain parallelism is given, in which the function evaluation itself is computationally expensive. All parallel algorithms were initially implemented on an IBM/SP2 (distributed-memory) machine. In all cases, however, message passing is handled through the standard Message Passing Interface (MPI); consequently the algorithms can also be implemented on any distributed- or shared-memory system that runs MPI. The efficiency of these implementations is discussed. (C) 2000 Elsevier Science B.V. All rights reserved.

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Documento generato il 07/07/20 alle ore 10:50:38