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ppscan's Introduction

ppSCAN

Parallel pSCAN algorithm with vectorized set intersection, compared with SCAN, pSCAN, SCAN++, anySCAN, SCAN-XP (an order of magnitude faster than the-state-of-the-arts), 250x faster than pSCAN on KNL (1.8 billion-undirected-edge-graph friendster).

See our slides for the general idea. Please check out our ICPP'18 paper Parallelizing Pruning-based Graph Structural Clustering for further details.

by Authors:

Yulin Che, Shixuan Sun and Prof. Luo.

ppSCAN Codes RoadMap

ppSCAN is tested on both Multi-core Intel Xeon CPU and Intel Xeon Phi Processor Knights Landing.

content directory
c++ parallel codes (Release) see ppSCAN-release, removing experimental codes, tested on KNL (support dst_edge (neighbors) array allocated on MCDRAM, using memkind)
c++ parallel codes see ppSCAN
vectorization codes (AVX2 / AVX512) see Graph.cpp
experimental scripts and case studies see python_experiments
python scripts for generating figures in paper see python paper-figures
SCAN variants codes see SCANVariants: SCAN, pSCAN, SCAN++, anySCAN, SCAN-XP
python scripts during study see python_playground: union-find, galloping-search, pSCAN

Paper Presented

  • Yulin Che, Shixuan Sun, Qiong Luo. 2018. Parallelizing Pruning-based Graph Structural Clustering. In ICPP 2018: 47th International Conference on Parallel Processing, August 13โ€“16, 2018, Eugene, OR, USA. ACM, New York, NY, USA, 10 pages.

If you use our codes in your research, please kindly cite our paper. The BibTeX is as follows.

@inproceedings{DBLP:conf/icpp/CheSL18,
  author    = {Yulin Che and
               Shixuan Sun and
               Qiong Luo},
  title     = {Parallelizing Pruning-based Graph Structural Clustering},
  booktitle = {Proceedings of the 47th International Conference on Parallel Processing,
               {ICPP} 2018, Eugene, OR, USA, August 13-16, 2018},
  pages     = {77:1--77:10},
  year      = {2018},
  crossref  = {DBLP:conf/icpp/2018},
  url       = {http://doi.acm.org/10.1145/3225058.3225063},
  doi       = {10.1145/3225058.3225063},
  timestamp = {Thu, 09 Aug 2018 08:53:38 +0200},
  biburl    = {https://dblp.org/rec/bib/conf/icpp/CheSL18},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Other Information

SCAN Preliminaries, our ppSCAN overview including design and optimizations.

Refrences

  • SCAN

Xu X, Yuruk N, Feng Z, et al. Scan: a structural clustering algorithm for networks[C]//Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2007: 824-833.

  • SCAN++

Shiokawa H, Fujiwara Y, Onizuka M. SCAN++: efficient algorithm for finding clusters, hubs and outliers on large-scale graphs[J]. Proceedings of the VLDB Endowment, 2015, 8(11): 1178-1189.

  • pSCAN

Chang L, Li W, Qin L, et al. $\mathsf {pSCAN} $: Fast and Exact Structural Graph Clustering[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(2): 387-401.

  • anySCAN

Mai S T, Dieu M S, Assent I, et al. Scalable and Interactive Graph Clustering Algorithm on Multicore CPUs[C]//Data Engineering (ICDE), 2017 IEEE 33rd International Conference on. IEEE, 2017: 349-360.

  • SCAN-XP

Takahashi T, Shiokawa H, Kitagawa H. SCAN-XP: Parallel Structural Graph Clustering Algorithm on Intel Xeon Phi Coprocessors[C]//Proceedings of the 2nd International Workshop on Network Data Analytics. ACM, 2017: 6.

  • GPU-SCAN (TPDS, journal)

Stovall T R, Kockara S, Avci R. GPUSCAN: GPU-Based Parallel Structural Clustering Algorithm for Networks[J]. IEEE Transactions on Parallel and Distributed Systems, 2015, 26(12): 3381-3393.

ppscan's People

Contributors

cheyulin avatar

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