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| Titel |
Par@Graph – a parallel toolbox for the construction and analysis of large complex climate networks |
| VerfasserIn |
H. Ihshaish, A. Tantet, J. C. M. Dijkzeul, H. A. Dijkstra |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1991-959X
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| Digitales Dokument |
URL |
| Erschienen |
In: Geoscientific Model Development ; 8, no. 10 ; Nr. 8, no. 10 (2015-10-22), S.3321-3331 |
| Datensatznummer |
250116609
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| Publikation (Nr.) |
 copernicus.org/gmd-8-3321-2015.pdf |
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| Zusammenfassung |
| In this paper, we present Par@Graph, a software toolbox to reconstruct and
analyze complex climate networks having a large number of nodes (up to at
least 106) and edges (up to at least 1012). The key innovation is
an efficient set of parallel software tools designed to leverage the
inherited hybrid parallelism in distributed-memory clusters of multi-core
machines. The performance of the toolbox is illustrated through networks
derived from sea surface height (SSH) data of a global high-resolution ocean
model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to
reconstruct a climate network
including the preprocessing and the correlation of 3 × 105 SSH time
series, resulting in a weighted graph with the same number of vertices and
about 3.2 × 108 edges. In less than 14 min on 30 processors, the
resulted graph's degree centrality, strength, connected components,
eigenvector centrality, entropy and clustering coefficient metrics were
obtained. These results indicate that a complete cycle to construct and
analyze a large-scale climate network is available under 22 min Par@Graph
therefore facilitates the application of climate network analysis on
high-resolution observations and model results, by enabling fast network
reconstruct from the calculation
of statistical similarities between climate time series. It also enables
network analysis at unprecedented scales on a variety of different sizes of
input data sets. |
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