Work efficient parallel algorithms for large graph exploration on emerging heterogeneous architectures

Banerjee, Dip Sankar; Kumar, Anujith; Chaitanya, Meher; Sharma, Shashank; Kothapalli, Kishore

doi:10.1016/j.jpdc.2014.11.006

Cited by 9 publications

(2 citation statements)

References 36 publications

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“…27 In recent years, one particular line of work that is used in this context is to consider the structural properties of the input graph and study how these properties impact the algorithm design and implementation process. Examples of this technique can be seen in the work of Hong et al, 28 Djidjev et al, 29 Banerjee et al, 30 Chaitanya and Kothapalli, 31 Garg and Kothapalli 32 among others. Garg et al 32 propose a framework which prune the graph by dead nodes and chain nodes helps in optimization of pagerank computation.…”

Section: Related Workmentioning

confidence: 99%

Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Regunta

Tondomker

Shukla

et al. 2021

Concurrency and Computation

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Finding the centrality measures of nodes in a graph is a problem of fundamental importance due to various applications from social networks, biological networks, and transportation networks. Given the large size of such graphs, it is natural to use parallelism as a recourse. Several studies show how to compute the various centrality measures of nodes in a graph on parallel architectures, including multi‐core systems and GPUs. However, as these graphs evolve and change, it is pertinent to study how to update the centrality measures on changes to the underlying graph. In this article, we show novel parallel algorithms for updating the betweenness‐ and closeness‐centrality values of nodes in a dynamic graph. Our algorithms process a batch of updates in parallel by extending the approach of handling a single update for betweenness‐ and closeness‐centrality. For the latter, we also introduce techniques based on traversals of the block‐cut tree of a graph. Besides, our algorithms incorporate mechanisms to exploit the structural properties of graphs for enhanced performance. We implement our algorithms on two parallel architectures: an Intel 24‐core CPU and an Nvidia Tesla V100 GPU. To the best of our knowledge, we are the first to show GPU algorithms for the above two problems. In addition, we conduct detailed experiments to study the impact of various parameters associated with our algorithms and their implementation. Our results on a collection of real‐world graphs indicate that our algorithms achieve a significant speedup over corresponding state‐of‐the‐art algorithms.

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Section: Related Workmentioning

confidence: 99%

Efficient parallel algorithms for dynamic closeness‐ and betweenness centrality

Regunta

Tondomker

Shukla

et al. 2021

Concurrency and Computation

Self Cite

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“…However, the increase in the relative size of datasets to account for real-world problems, has forced researchers and engineers to move to distributed and parallel proposals [21] in order to explore and process large graphs, and to calculate different measures over them. Machine learning research has made great advancements in developing scalable and distributed algorithms, but they often lack support for interactivity.…”

Section: Exploratory Data Analysis In Graphsmentioning

confidence: 99%

Improving data exploration in graphs with fuzzy logic and large-scale visualisation

Molina-Solana

Birch

Guo

2017

Applied Soft Computing

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This work presents three case-studies of how fuzzy logic can be combined with large-scale immersive visualisation to enhance the process of graph sensemaking, enabling interactive fuzzy filtering of large global views of graphs. The aim is to provide users a mechanism to quickly identify interesting nodes for further analysis. Fuzzy logic allows a flexible framework to ask human-like curiosity-driven questions over the data, and visualisation allows its communication and understanding. Together, these two technologies successfully empower novices and experts to a faster and deeper understanding of the underlying patterns in big datasets compared to traditional means in a desktop screen with crisp queries. Among other examples, we provide evidence of how these two technologies successfully enable the identification of relevant transaction patterns in the Bitcoin network.

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