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An Efficient Parallel Load-Balancing Framework for Orthogonal Decomposition of Geometrical Data

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High Performance Computing (ISC High Performance 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9697))

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Abstract

The accurate subdivision of spatially organized datasets is a complex problem in computer science but specifically important for load balancing in parallel environments. The problem is to (a) find a partitioning where each partition has the same number of elements and (b) the communication between partitions (duplicate members) is minimized. We present a novel parallel load-balancing framework — Sort Balance Split (SBS) — the first to our knowledge to perform accurate parallel partitioning of multidimensional data, while requiring a fixed number of communication steps independent of network size or input data distribution. When compared to the state of the art sampling and parallel partitioning methods adopted by HPC problems, it delivers better load balancing on a shorter time to solution. We analyse four partitioning schemes that SBS can be applied to, and evaluated our method on 4096 nodes of an IBM BlueGene/Q supercomputer partitioning up to 1 trillion elements, and exhibiting almost-linear scaling properties.

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References

  1. Aluru, S., Sevilgen, F.E.: Parallel domain decomposition and load balancing using space-filling curves. In: Proceedings of Fourth International Conference on High-Performance Computing, pp. 230–235. IEEE (1997)

    Google Scholar 

  2. Blackston, D., Suel, T.: Highly portable and efficient implementations of parallel adaptive n-body methods. In: Proceedings of the 1997 ACM/IEEE Conference on Supercomputing, SC 1997, pp. 1–20. ACM, New York (1997). http://doi.acm.org/10.1145/509593.509597

  3. Boley, D., Gini, M., Gross, R., Han, E.H.S., Hastings, K., Karypis, G., Kumar, V., Mobasher, B., Moore, J.: Partitioning-based clustering for web document categorization. Decis. Support Syst. 27(3), 329–341 (1999)

    Article  Google Scholar 

  4. Boman, E.G., Catalyurek, U.V., Chevalier, C., Devine, K.D.: The Zoltan and Isorropia parallel toolkits for combinatorial scientific computing: partitioning, ordering, and coloring. Sci. Prog. 20(2), 129–150 (2012)

    Google Scholar 

  5. Catalyurek, U.V., Aykanat, C.: Hypergraph-partitioning-based decomposition for parallel sparse-matrix vector multiplication. IEEE Trans. Parallel Distrib. Syst. 10(7), 673–693 (1999)

    Article  Google Scholar 

  6. Deveci, M., Rajamanickam, S., Devine, K., Catalyurek, U.: Multi-jagged: a scalable parallel spatial partitioning algorithm. IEEE Transactions on Parallel and Distributed Systems, PP(99), 1–1 (2015)

    Google Scholar 

  7. Grama, A.: Introduction to Parallel Computing. Pearson Education, Upper Saddle River (2003)

    Google Scholar 

  8. Hamada, T., Narumi, T., Yokota, R., Yasuoka, K., Nitadori, K., Taiji, M.: 42 tflops hierarchical n-body simulations on gpus with applications in both astrophysics and turbulence. In: Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis, SC 2009, pp. 62:1–62:12. ACM, New York (2009). http://doi.acm.org/10.1145/1654059.1654123

  9. Hamada, T., Nitadori, K.: 190 tflops astrophysical n-body simulation on a cluster of gpus. In: Proceedings of the 2010 ACM/IEEE International Conference for High Performance Computing, Networking, Storage and Analysis, SC 2010, pp. 1–9. IEEE Computer Society, Washington (2010). http://dx.doi.org/10.1109/SC.2010.1

  10. Haring, R., Ohmacht, M., Fox, T., Gschwind, M., Satterfield, D., Sugavanam, K., Coteus, P., Heidelberger, P., Blumrich, M., Wisniewski, R., Gara, A., Chiu, G., Boyle, P., Chist, N., Kim, C.: The IBM blue Gene/Q compute chip. IEEE Micro 32(2), 48–60 (2012)

    Article  Google Scholar 

  11. Hill, S.L., Wang, Y., Riachi, I., Schürmann, F., Markram, H.: Statistical connectivity provides a sufficient foundation for specific functional connectivity in neocortical neural microcircuits. Proc. National Acad. Sci. 109(42), E2885–E2894 (2012). http://www.pnas.org/content/109/42/E2885.abstract

    Article  Google Scholar 

  12. Ishiyama, T., Nitadori, K., Makino, J.: 4.45 pflops astrophysical n-body simulation on k computer: The gravitational trillion-body problem. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2012, pp. 5:1–5:10. IEEE Computer Society Press, Los Alamitos (2012). http://dl.acm.org/citation.cfm?id=2388996.2389003

  13. Karypis, G.: METIS and ParMETIS. In: Padua, D. (ed.) Encyclopedia of Parallel Computing, pp. 1117–1124. Springer, Heidelberg (2011)

    Google Scholar 

  14. Karypis, G., Aggarwal, R., Kumar, V., Shekhar, S.: Multilevel hypergraph partitioning: applications in vlsi domain. IEEE Trans. Very Large Scale Integr. VLSI Syst. 7(1), 69–79 (1999)

    Article  Google Scholar 

  15. Kozloski, J., Sfyrakis, K., Hill, S., Schurmann, F., Peck, C., Markram, H.: Identifying, tabulating, and analyzing contacts between branched neuron morphologies. IBM J. Res. Dev. 52(12), 43–55 (2008)

    Article  Google Scholar 

  16. Kutluca, H., Aykanat, C., et al.: Image-space decomposition algorithms for sort-first parallel volume rendering of unstructured grids. J. Supercomput. 15(1), 51–93 (2000)

    Article  MATH  Google Scholar 

  17. Papa, D.A., Markov, I.L.: Hypergraph partitioning and clustering. In: Approximation Algorithms and Metaheuristics, pp. 61–1 (2007)

    Google Scholar 

  18. Pellegrini, F., Roman, J.: Scotch: a software package for static mapping by dual recursive bipartitioning of process and architecture graphs. In: Liddell, H., Colbrook, A., Hertzberger, B., Sloot, P.M.A. (eds.) HPCN-Europe 1996. LNCS, vol. 1067, pp. 493–498. Springer, Heidelberg (1996). http://dl.acm.org/citation.cfm?id=645560.658570

    Chapter  Google Scholar 

  19. Randles, A., Draeger, E.W., Oppelstrup, T., Krauss, L., Gunnels, J.A.: Massively parallel models of the human circulatory system. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, p. 1. ACM (2015)

    Google Scholar 

  20. Reumann, M., Fitch, B.G., Rayshubskiy, A., Keller, D.U., Seemann, G., Dossel, O., Pitman, M.C., Rice, J.J.: Orthogonal recursive bisection data decomposition for high performance computing in cardiac model simulations: dependence on anatomical geometry. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, pp. 2799–2802. IEEE (2009)

    Google Scholar 

  21. Rodrigues, E.R., Navaux, P.O.A., Panetta, J., Fazenda, A., Mendes, C.L., Kale, L.V.: A comparative analysis of load balancing algorithms applied to a weather forecast model. In: 2010 22nd International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD), pp. 71–78. IEEE (2010)

    Google Scholar 

  22. Rossinelli, D., Hejazialhosseini, B., Hadjidoukas, P., Bekas, C., Curioni, A., Bertsch, A., Futral, S., Schmidt, S.J., Adams, N.A., Koumoutsakos, P.: 11 PFLOP/s simulations of cloud cavitation collapse. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC 2013, pp. 3:1–3:13. ACM, New York (2013). http://doi.acm.org/10.1145/2503210.2504565

  23. Saule, E., Bas, E.Ö., Çatalyürek, Ü.V.: Load-balancing spatially located computations using rectangular partitions. CoRR abs/1104.2566 (2011). http://arxiv.org/abs/1104.2566

  24. Shimokawabe, T., Aoki, T., Takaki, T., Endo, T., Yamanaka, A., Maruyama, N., Nukada, A., Matsuoka, S.: Peta-scale phase-field simulation for dendritic solidification on the tsubame 2.0 supercomputer. In: Proceedings of 2011 International Conference for High Performance Computing, Networking, Storage and Analysis. SC 2011, pp. 3:1–3:11. ACM, New York (2011). http://doi.acm.org/10.1145/2063384.2063388

  25. Siebert, C., Wolf, F.: A scalable parallel sorting algorithm using exact splitting. Technical report, German Research School for Simulation Sciences GmbH (2010)

    Google Scholar 

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Acknowledgements

The work was supported by funding from the ETH Domain for the Blue Brain Project (BBP). The BlueBrain IV BlueGene/Q system is financed by ETH Board Funding to the Blue Brain Project and hosted at the Swiss National Supercomputing Center (CSCS). We thank James King, Stuart Yates and Fabien Delalondre for technical discussions.

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Authors and Affiliations

  1. Blue Brain Project, École Polytechnique Fédérale de Lausanne (EPFL), Biotech Campus, 1202, Geneva, Switzerland

    Bruno R. C. Magalhães & Felix Schürmann

  2. Data-Intensive Applications and Systems Laboratory, École Polytechnique Fédérale de Lausanne (EPFL), 1015, Lausanne, Switzerland

    Farhan Tauheed, Thomas Heinis & Anastasia Ailamaki

Authors
  1. Bruno R. C. Magalhães
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  2. Farhan Tauheed
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  3. Thomas Heinis
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  4. Anastasia Ailamaki
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  5. Felix Schürmann
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Correspondence to Bruno R. C. Magalhães .

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Editors and Affiliations

  1. Deutsches Klimarechenzentrum, Hamburg, Germany

    Julian M. Kunkel

  2. Argonne National Laboratory, Lemont, Illinois, USA

    Pavan Balaji

  3. University of Tennessee, Knoxville, Tennessee, USA

    Jack Dongarra

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Magalhães, B.R.C., Tauheed, F., Heinis, T., Ailamaki, A., Schürmann, F. (2016). An Efficient Parallel Load-Balancing Framework for Orthogonal Decomposition of Geometrical Data. In: Kunkel, J., Balaji, P., Dongarra, J. (eds) High Performance Computing. ISC High Performance 2016. Lecture Notes in Computer Science(), vol 9697. Springer, Cham. https://doi.org/10.1007/978-3-319-41321-1_5

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  • DOI: https://doi.org/10.1007/978-3-319-41321-1_5

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-41321-1

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