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231223s2004 xx ||||| 00| ||eng c |
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|a (DE-627)NLM14711988X
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|a (NLM)15011259
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|a DE-627
|b ger
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|e rakwb
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|a eng
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|a Fedorov, Dmitri G
|e verfasserin
|4 aut
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|a A new hierarchical parallelization scheme
|b generalized distributed data interface (GDDI), and an application to the fragment molecular orbital method (FMO)
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|c 2004
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|a Text
|b txt
|2 rdacontent
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|a ohne Hilfsmittel zu benutzen
|b n
|2 rdamedia
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|a Band
|b nc
|2 rdacarrier
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|a Date Completed 27.05.2004
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|a Date Revised 10.03.2004
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|a published: Print
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|a Citation Status PubMed-not-MEDLINE
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|a Copyright 2004 Wiley Periodicals, Inc. J Comput Chem 25: 872-880, 2004
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|a A two-level hierarchical scheme, generalized distributed data interface (GDDI), implemented into GAMESS is presented. Parallelization is accomplished first at the upper level by assigning computational tasks to groups. Then each group does parallelization at the lower level, by dividing its task into smaller work loads. The types of computations that can be used with this scheme are limited to those for which nearly independent tasks and subtasks can be assigned. Typical examples implemented, tested, and analyzed in this work are numeric derivatives and the fragment molecular orbital method (FMO) that is used to compute large molecules quantum mechanically by dividing them into fragments. Numeric derivatives can be used for algorithms based on them, such as geometry optimizations, saddle-point searches, frequency analyses, etc. This new hierarchical scheme is found to be a flexible tool easily utilizing network topology and delivering excellent performance even on slow networks. In one of the typical tests, on 16 nodes the scalability of GDDI is 1.7 times better than that of the standard parallelization scheme DDI and on 128 nodes GDDI is 93 times faster than DDI (on a multihub Fast Ethernet network). FMO delivered scalability of 80-90% on 128 nodes, depending on the molecular system (water clusters and a protein). A numerical gradient calculation for a water cluster achieved a scalability of 70% on 128 nodes. It is expected that GDDI will become a preferred tool on massively parallel computers for appropriate computational tasks
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|a Journal Article
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|a Olson, Ryan M
|e verfasserin
|4 aut
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|a Kitaura, Kazuo
|e verfasserin
|4 aut
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|a Gordon, Mark S
|e verfasserin
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|a Koseki, Shiro
|e verfasserin
|4 aut
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|i Enthalten in
|t Journal of computational chemistry
|d 1984
|g 25(2004), 6 vom: 30. Apr., Seite 872-80
|w (DE-627)NLM098138448
|x 1096-987X
|7 nnns
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| 773 |
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|g volume:25
|g year:2004
|g number:6
|g day:30
|g month:04
|g pages:872-80
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|a GBV_USEFLAG_A
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|a SYSFLAG_A
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|a GBV_ILN_350
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|a AR
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|d 25
|j 2004
|e 6
|b 30
|c 04
|h 872-80
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