A CAM-Free Exascalable HPC Router for Low-Energy Communications

  • Caroline ConcattoEmail author
  • Jose A. Pascual
  • Javier Navaridas
  • Joshua Lant
  • Andrew Attwood
  • Mikel Lujan
  • John Goodacre
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10793)


Power consumption is the main hurdle in the race for designing Exascale-capable computing systems which would require deploying millions of computing elements. While this problem is being addressed by designing increasingly more power-efficient processing subsystems, little effort has been put on reducing the power consumption of the interconnection network. This is precisely the objective of this work, in which we study the benefits, in terms of both area and power, of avoiding costly and power-hungry CAM-based routing tables deep-rooted in all current networking technologies. We present our custom-made, FPGA-based router based on a simple, arithmetic routing engine which is shown to be much more power- and area-efficient than even a relatively small 2K-entry routing table which requires as much area and one order of magnitude more power than our router.


  1. 1.
    Abts, D., et al.: Energy proportional datacenter networks. In: International Symposium on Computer Architecture, ISCA 2010, pp. 338–347. ACM, New York (2010)Google Scholar
  2. 2.
    Ajima, Y., et al.: The Tofu interconnect. IEEE Micro 32(1), 21–31 (2012)CrossRefGoogle Scholar
  3. 3.
    Al-Fares, et al.: A scalable, commodity data center network architecture. In: ACM SIGCOMM 2008 Conference on Data Communication, SIGCOMM 2008, pp. 63–74. ACM, New York (2008)Google Scholar
  4. 4.
    Aroca, R.V., Gonçalves, L.M.G.: Towards green data centers: a comparison of \(\times \)86 and ARM architectures power efficiency. J. Parallel Distrib. Comput. 72(12), 1770–1780 (2012)CrossRefGoogle Scholar
  5. 5.
    Bhuyan, L.N., Agrawal, D.P.: Generalized hypercube and hyperbus structures for a computer network. IEEE Trans. Comput. 33(4), 323–333 (1984)CrossRefzbMATHGoogle Scholar
  6. 6.
    Chen, D., et al.: Looking under the hood of the IBM Blue Gene/Q network. In: Conference for High Performance Computing, Networking, Storage and Analysis (SC), pp. 1–12, November 2012Google Scholar
  7. 7.
    Cuzzocrea, et al.: Big graph analytics: the state of the art and future research agenda. In: Proceedings of the 17th International Workshop on Data Warehousing and OLAP, DOLAP 2014, pp. 99–101, ACM, New York (2014)Google Scholar
  8. 8.
    Dally, W., Towles, B.: Principles and Practices of Interconnection Networks. Morgan Kaufmann Publishers Inc., San Francisco (2003)Google Scholar
  9. 9.
    Dean, J., et al.: Mapreduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)CrossRefGoogle Scholar
  10. 10.
    Derradji, S., et al.: The BXI interconnect architecture. In: IEEE Annual Symposium on High-Performance Interconnects, HOTI 2015, pp. 18–25. IEEE Computer Society, Washington (2015)Google Scholar
  11. 11.
    Duato, J., et al.: Interconnection Networks: An Engineering Approach. Morgan Kaufmann Publishers Inc., San Francisco (2002)Google Scholar
  12. 12.
    Gómez, C., et al.: Deterministic versus adaptive routing in fat-trees. In: Workshop on Communication Architecture on Clusters (CAC 2007) (2007)Google Scholar
  13. 13.
    Heller, B., et al.: ElasticTree: saving energy in data center networksGoogle Scholar
  14. 14.
    Katevenis, M., et al.: The exanest project: interconnects, storage, and packaging for exascale systems. In: 2016 Euromicro Conference on Digital System Design (DSD), pp. 60–67, August 2016Google Scholar
  15. 15.
    Kieu, T.C., et al.: An interconnection network exploiting trade-off between routing table size and path length. In: International Symposium on Computing and Networking (CANDAR), pp. 666–670, November 2016Google Scholar
  16. 16.
    Kim, J., et al.: Technology-driven, highly-scalable dragonfly topology. In: 2008 International Symposium on Computer Architecture, pp. 77–88, June 2008Google Scholar
  17. 17.
    Navaridas, J., Miguel-Alonso, J., Pascual, J.A., Ridruejo, F.J.: Simulating and evaluating interconnection networks with insee. Simul. Model. Pract. Theory 19(1), 494–515 (2011). CrossRefGoogle Scholar
  18. 18.
    Petrini, F., Vanneschi, M.: k-ary n-trees: high performance networks for massively parallel architectures. In: International Parallel Processing Symposium, pp. 87–93 (1997)Google Scholar
  19. 19.
    Sancho, J.C., et al.: Effective methodology for deadlock-free minimal routing in infiniband networks. In: Proceedings International Conference on Parallel Processing, pp. 409–418 (2002)Google Scholar
  20. 20.
    Singh, A., et al.: Jupiter rising: a decade of Clos topologies and centralized control in Google’s datacenter network. In: ACM Conference on Special Interest Group on Data Communication, SIGCOMM 2015, pp. 183–197. ACM, New York (2015)Google Scholar
  21. 21.
    Vermeij, M., et al.: MonetDB, a novel spatial columnstore DBMS. In: Free and Open Source for Geospatial (FOSS4G) Conference, OSGeo (2008)Google Scholar
  22. 22.
    Vignéras, P., Quintin, J.N.: The BXI routing architecture for exascale supercomputer. J. Supercomput. 72(12), 4418–4437 (2016)CrossRefGoogle Scholar
  23. 23.
    Zahavi, E.: Fat-tree routing and node ordering providing contention free traffic for MPI global collectives. J. Parallel Distrib. Comput. 72(11), 1423–1432 (2012)CrossRefzbMATHGoogle Scholar
  24. 24.
    Zahid, F., et al.: A weighted fat-tree routing algorithm for efficient load-balancing in infini band enterprise clusters. In: 2015 23rd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing, pp. 35–42, March 2015Google Scholar
  25. 25.
    Zilberman, N., et al.: NetFPGA SUME: toward 100 Gbps as research commodity. IEEE Micro 34(5), 32–41 (2014)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Caroline Concatto
    • 1
    Email author
  • Jose A. Pascual
    • 1
  • Javier Navaridas
    • 1
  • Joshua Lant
    • 1
  • Andrew Attwood
    • 1
  • Mikel Lujan
    • 1
  • John Goodacre
    • 1
  1. 1.School of Computer ScienceUniversity of ManchesterManchesterUK

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