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Title: Hybrid CPU-GPU community detection in weighted networks
Authors: Souravlas, Stavros
Sifaleras, Angelo
Katsavounis, Stefanos
Type: Article
Subjects: FRASCATI::Natural sciences::Computer and information sciences
FRASCATI::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering
Keywords: Community Detection
Parallel Algorithms
Binary Trees
Social Circles
GPU-CPU Scheduling
Issue Date: 2020
Publisher: IEEE
Source: IEEE Access
Volume: 8
First Page: 57527
Last Page: 57551
Abstract: Recently, a new trend has emerged in the field of parallel and high performance computing, the hybrid implementation using CPU-GPU modules. In such implementations, the computational load is shared between the CPU and GPU, in order to improve the computational efficiency. However, the task of sharing the computational load between the two modules is a rather difficult one, with a number of limitations being imposed. This paper extends our recent work [1] on community detection, which is based on transforming a network of nodes into a set of threaded binary trees. In this work, we share the computational load between the two units: the CPU takes specific samples of the network communities and organizes them in the form of threaded binary trees. The GPU takes over the heavy load of reading this data and transforming it into a path-matrix. Finally, this matrix is sent back to the CPU for analysis, community detection and overlaps, as well as network information upgrades. Our simulation results show significant improvement over our previous strategy and other known community detection strategies found in the literature.
ISSN: 2169-3536
Other Identifiers: 10.1109/ACCESS.2020.2982227
Appears in Collections:Department of Applied Informatics

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