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Graph algorithms: parallelization and scalability

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Abstract

For computations on large-scale graphs, one often resorts to parallel algorithms. However, parallel algorithms are difficult to write, debug and analyze. Worse still, it is difficult to make algorithms parallelly scalable, such that the more machines are used, the faster the algorithms run. Indeed, it is not yet known whether any PTIME computational problems admit parallelly scalable algorithms on shared-nothing systems. Is it possible to parallelize sequential graph algorithms and guarantee convergence at the correct results as long as the sequential algorithms are correct? Moreover, does a PTIME parallelly scalable problem exist on shared-nothing systems? This position paper answers both questions in the affirmative.

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Acknowledgements

This work was supported in part by Shenzhen Institute of Computing Sciences, Beijing Advanced Innovation Center for Big Data and Brain Computing (Beihang University), Royal Society Wolfson Research Merit Award (Grant No. WRM/R1/180014), European Research Council (Grant No. 652976), and Engineering and Physical Sciences Research Council (Grant No. EP/M025268/1).

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Authors and Affiliations

  1. School of Informatics, University of Edinburgh, Edinburgh, EH8 9AB, UK

    Wenfei Fan

  2. Shenzhen Institute of Computing Sciences, Shenzhen University, Shenzhen, 518060, China

    Wenfei Fan, Kun He, Qian Li & Yue Wang

  3. Beijing Advanced Innovation Center for Big Data and Brain Computing, Beihang University, Beijing, 100191, China

    Wenfei Fan

  4. Guangdong Province Key Laboratory of Popular High Performance Computers, Shenzhen University, Shenzhen, 518060, China

    Kun He & Qian Li

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  1. Wenfei Fan
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  2. Kun He
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  4. Yue Wang
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Correspondence to Wenfei Fan.

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Fan, W., He, K., Li, Q. et al. Graph algorithms: parallelization and scalability. Sci. China Inf. Sci. 63, 203101 (2020). https://doi.org/10.1007/s11432-020-2952-7

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  • DOI: https://doi.org/10.1007/s11432-020-2952-7

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