The Need for HPC Computing in Network Science

The Need for HPC Computing in Network Science

DOI: 10.4018/978-1-5225-3799-1.ch001

Abstract

The size of complex networks introduces large amounts of traversal times that can be tackled by exploiting pervasive multi-core and many-core parallel hardware architectures. However, there is a list of factors that make the design of efficient parallel traversal algorithms for graphs difficult: unstructured problems, data-driven computation, irregular memory access, poor locality, and low computing load. In this chapter, the authors introduce the synergy between Network Science and High Performance Computing and motivate the combined use of multi/many-core heterogeneous computing and Network Science techniques to tackle the above-mentioned challenges and to efficiently traverse the structure of massive real-world graphs.
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Complex Networks And Network Science

Network Science has been defined by the Committee on Network Science for Future Army Applications (2005) as “the study of network representations of physical, biological, and social phenomena leading to predictive models of these phenomena.” The main study objects in Network Science are complex networks.

A complex network is a graph , i.e. a discrete mathematical object integrated of a non-empty set of vertices or nodes and a set of edges or links connecting pairs of vertices.

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