Hybrid CPU-GPU Community Detection in Weighted Networks

Souravlas, Stavros; Sifaleras, Angelo; Katsavounis, Stefanos

doi:10.1109/access.2020.2982227

Cited by 15 publications

(13 citation statements)

References 34 publications

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“…One idea we currently work on in order to reduce the total execution time is to pipeline the computations, but careful design is required to avoid delays between the pipeline [37] stages. Also, the introduction of a CPU/GPU combination would be of high interest, especially for large scale networks [38]. In this case, the model, and thus the simulator, has to be equiped with cores which are able to model the pipeline operations.…”

Section: Discussionmentioning

confidence: 99%

On Modeling and Simulation of Resource Allocation Policies in Cloud Computing Using Colored Petri Nets

2020

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The Petri net (PN) formalism is a suitable tool for modeling parallel systems due to its basic characteristics, such as synchronization. The extension of PN, the Colored Petri Nets (CPN) allows the incorporation of more details of the real system into the model (for example, contention for shared resources). The CPNs have been widely used in a variety of fields to produce suitable models. One of their biggest strengths is that their overall philosophy is quite similar to the philosophy of the object-oriented paradigm. In this regard, the CPN models can be used to implement simulators in a rather straightforward way. In this paper, the CPN framework is employed to implement a new resource allocation simulator, which is used to verify the performance of our previous work, where we proposed a fair resource allocation scheme with flow control and maximum utilization of the system’s resources.

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Section: Discussionmentioning

confidence: 99%

On Modeling and Simulation of Resource Allocation Policies in Cloud Computing Using Colored Petri Nets

2020

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“…The data structures have been extensively studied in the literature and very robust algorithms have been developed for handling them. For example, parallel algorithms, which reduce the total execution times or implementation to more powerful hardware types, like GPU (Souravlas, Sifaleras, and Katsavounis 2020). In this regard, we believe that it is important to present such schemes.…”

Section: Articlementioning

confidence: 99%

“…The algorithm's complexity was detected by the cost of the most expensive phase, the path analysis, and it was (n + 1)M − 2 M+1 , where M = log 2 (n + 1) was the number of each node's neighbors and n was the number of the threaded tree's nodes. An extension of this work was developed later, in which the authors have designed parts of the algorithm in such a way that can be suitable for execution in a GPU (Souravlas, Sifaleras, and Katsavounis 2020). This resulted in a much more efficient scheme in terms of the total execution time.…”

Section: Data Structure-based Approachesmentioning

confidence: 99%

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A classification of community detection methods in social networks: a survey

Souravlas

Sifaleras

Tsintogianni

et al. 2021

International Journal of General Systems

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The detection of community structures is a crucial research area. The problem of community detection has received considerable attention from a large portion of the scientific community and a very large number of papers has already been published in the literature. Even more important is the fact that, this large number of articles is in fact spread across a large number of different disciplines, from computer science, to statistics, and social sciences. These facts necessitate some type of classification and organization of these works. In this work, our basic classification approach divides the community detection schemes into three basic approaches: (a) the bottom-up approaches, (b) the top-down approaches, and (c) the data structurebased approaches. The first category includes the majority of algorithms, so further classification is possible. Such a classification is included in this work. For the other two categories, we make no further categorizations but we simply focus our discussion on the metrics or the data structures being used. Finally, a few possible directions for future research are also suggested.

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“…They also proposed parallel implementations with hybrid CPU-GPU. In [60], Souravlas et al proposed a parallel CD algorithm with hybrid CPU-GPU devices. The algorithm transforms the network nodes into a set of threaded binary trees.…”

Section: ) Gpu Based Parallel Computationmentioning

confidence: 99%

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

et al. 2021

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Complex networks (CNs) have gained much attention in recent years due to their importance and popularity. The rapid growth in the size of CNs leads to more difficulties in the analysis of CNs tasks. Community Detection (CD) is an important multidisciplinary research area where many machine/deep learning-based methods have been applied to map CNs into a low-dimensional representation for extracting information similarity among members of CNs. Currently, Deep Learning (DL) is one of the promising methods to extract knowledge and learn information from high dimensional space and represent it in low dimensional space. However, designing an accurate and efficient DL-based CD method especially when dealing with large CNs is always an on-going research endeavor to pursue. Meta-Heuristic (MH) algorithms have shown their potentials in improving DL models in terms of solution quality and computational cost. In addition, Parallel computing is a feasible solution for building efficient DL models. The algorithmic principle of MH is parallel in nature; however, its computation framework in DL training that is reported in the literature is not really implemented in a parallel computing setup. In this paper, we present a systematic review of CD in CNs from conventional machine learning to DL methods and point out the gap of applying DL-based CD methods in large CNs. In addition, the relevant studies on DL with parallel and MH approaches are reviewed and their implications on DL models are highlighted to prospect effective solutions to overcome the challenges of DL-based CD methods. We also point out research challenges in the field of CD and suggest possible future research directions. INDEX TERMSCommunity detection; Deep learning; Complex networks; Meta-heuristic algorithms; Parallel computing.

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Hybrid CPU-GPU Community Detection in Weighted Networks

Cited by 15 publications

References 34 publications

On Modeling and Simulation of Resource Allocation Policies in Cloud Computing Using Colored Petri Nets

On Modeling and Simulation of Resource Allocation Policies in Cloud Computing Using Colored Petri Nets

A classification of community detection methods in social networks: a survey

A Review on Community Detection in Large Complex Networks from Conventional to Deep Learning Methods: A Call for the Use of Parallel Meta-Heuristic Algorithms

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