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Fast Dynamic Graph Algorithms

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Languages and Compilers for Parallel Computing (LCPC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11403))

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Abstract

We show that dynamic graph algorithms are amenable to parallelism on graphics processing units (GPUs). Evolving graphs such as social networks undergo structural updates, and analyzing such graphs with the existing static graph algorithms is inefficient. To deal with such dynamic graphs, we present techniques to (i) represent evolving graphs, (ii) amortize the processing cost over multiple updates, and (iii) optimize graph analytic algorithms for GPUs. We illustrate the effectiveness of our proposed mechanisms with three dynamic graph algorithms: dynamic breadth-first search, dynamic shortest paths computation and dynamic minimum spanning tree maintenance. In particular, we show that the dynamic processing is beneficial up to a certain percentage of updates beyond which a static algorithm is more efficient.

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Notes

  1. 1.

    http://www.cse.iitm.ac.in/~rupesh/?mode=Research.

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Acknowledgments

We thank the reviewers and our shepherd Nancy Amato for their comments which considerably improved our work. This work is partially supported by IIT Madras Exploratory Research Grant CSE/16-17/837/RFER/RUPS.

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Authors and Affiliations

  1. IIT Madras, Chennai, India

    Gaurav Malhotra, Hitish Chappidi & Rupesh Nasre

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  1. Gaurav Malhotra
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  2. Hitish Chappidi
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  3. Rupesh Nasre
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Corresponding author

Correspondence to Rupesh Nasre .

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Editors and Affiliations

  1. Texas A&M University, College Station, TX, USA

    Lawrence Rauchwerger

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Malhotra, G., Chappidi, H., Nasre, R. (2019). Fast Dynamic Graph Algorithms. In: Rauchwerger, L. (eds) Languages and Compilers for Parallel Computing. LCPC 2017. Lecture Notes in Computer Science(), vol 11403. Springer, Cham. https://doi.org/10.1007/978-3-030-35225-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-35225-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-35224-0

  • Online ISBN: 978-3-030-35225-7

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