Abstract
Abstract. 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.
Highlights
Over the last decade, the techniques of complex network analysis have found application in climate research
We introduce a complete toolbox Par@Graph designed for parallel computing platforms, which is capable of the preprocessing of large number of climate time series and the calculation of pairwise statistical measures, leading to the reconstruction of large-node climate networks
In this paper we presented the new parallel software toolbox Par@Graph to construct and analyze large-scale complex networks
Summary
The techniques of complex network analysis have found application in climate research. Given time series of climate data, represented by an N × M matrix, where N is the number of locations and M is the length of data attributes (daily or monthly values), one needs to calculate at least N 2/2 correlation values Such computations become challenging for large N; for example, with a network of 106 nodes, this would result in 5×1011 calculations. An exception is the software package Pyunicorn (Donges et al, 2013), developed at the Potsdam Institute for Climate Impact Research, that couples Python modules for numerical analysis with igraph It can carry out both tasks; the construction of climate networks and the analysis of the resulted graphs. We introduce a complete toolbox Par@Graph designed for parallel computing platforms, which is capable of the preprocessing of large number of climate time series and the calculation of pairwise statistical measures, leading to the reconstruction of large-node climate networks.
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