Skip to main content

A Tunable Implementation of Quality-of-Service Classes for HPC Networks

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 12728)


High-performance computer (HPC) networks are often shared by communication traffic from multiple applications with varying communication characteristics and resource requirements. These applications contend for shared network buffers and channels, potentially resulting in significant performance variations and slowdown of critical communication operations such as low-latency MPI collectives. In order to ensure predictable communication performance, network resources must be allocated relative to the communication requirements of applications.

Quality of Service (QoS) solutions can regulate the allocation of resources by defining traffic classes with specified resource allocations and assigning applications to these classes, thus improving application performance predictability. However, it is difficult to accomplish facility-level goals of ensuring efficient application communication when constrained to a limited number of classes.

We propose a practical QoS implementation for large-scale, low-diameter networks, such as the dragonfly topology, using flexible bandwidth shaping along with traffic prioritization to reduce the impact of interference on communication performance. Our design gives facilities more control over tuning QoS class to meet application- and site-specific performance guarantees. The results show that our solution effectively eliminates the slowdown of high-priority traffic due to interference with lower-priority traffic, significantly reducing run-to-run variability. We also demonstrate how port counters can be used to detect when a job-to-class assignment is inappropriate for a given system and when a workload is exceeding the bandwidth limits of its class.


  • Interconnect network
  • 1D dragonfly topology
  • QoS
  • Traffic class

This is a preview of subscription content, access via your institution.

Buying options

USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-030-78713-4_8
  • Chapter length: 20 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-3-030-78713-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.


  1. 1.

    Switch hardware can only support a limited number of actives classes due to resource limitations.

  2. 2.

    The number of traffic classes that can be configured on a given switch will be limited by how many class buffers and rate limiting counters are supported by that switch hardware.


  1. Brown, K.A., Jain, N., Matsuoka, S., Schulz, M., Bhatele, A.: Interference between I/O and MPI traffic on fat-tree networks. In: Proceedings of the 47th International Conference on Parallel Processing, ICPP 2018, pp. 1–10. Association for Computing Machinery, New York, August 2018

    Google Scholar 

  2. Carothers, C.D., Bauer, D., Pearce, S.: ROSS: a high-performance, low memory, modular time warp system. In: Proceedings Fourteenth Workshop on Parallel and Distributed Simulation, pp. 53–60 (2000)

    Google Scholar 

  3. Chunduri, S., et al.: GPCNeT: designing a benchmark suite for inducing and measuring contention in HPC networks. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis. SC 2019. Association for Computing Machinery, New York (2019)

    Google Scholar 

  4. Chunduri, S., Parker, S., Balaji, P., Harms, K., Kumaran, K.: Characterization of MPI usage on a production supercomputer. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis. SC 2018. IEEE Press (2018)

    Google Scholar 

  5. Cope, J., Liu, N., Lang, S., Carns, P., Carothers, C., Ross, R.: CODES: enabling co-design of multilayer exascale storage architectures (2011)

    Google Scholar 

  6. Dordal, P.L.: An Introduction to Computer Networks, August 2020

    Google Scholar 

  7. Grant, R.E., Pedretti, K.T., Gentile, A.: Overtime: a tool for analyzing performance variation due to network interference. In: Proceedings of the 3rd Workshop on Exascale MPI, ExaMPI 2015, pp. 1–10. Association for Computing Machinery, New York, November 2015

    Google Scholar 

  8. Groves, T., Gu, Y., Wright, N.J.: Understanding performance variability on the aries dragonfly network. In: 2017 IEEE International Conference on Cluster Computing (CLUSTER), pp. 809–813, September 2017. iSSN 2168-9253

    Google Scholar 

  9. Hewlett Packard Enterprise: Shasta Software Workshop (2019). Accessed 19 Oct 2020

  10. Hewlett Packard Enterprise: Measuring Network Performance to Better Manage IT. Technical White Paper a50002193ENW, August 2020

    Google Scholar 

  11. Jha, S., Brandt, J., Gentile, A., Kalbarczyk, Z., Iyer, R.: Characterizing supercomputer traffic networks through link-level analysis. In: 2018 IEEE International Conference on Cluster Computing (CLUSTER), pp. 562–570, September 2018., iSSN: 2168-9253

  12. John Thompson: Scalable Workload Models for System Simulations (2014). Accessed 19 Oct 2020

  13. Jokanovic, A., Sancho, J.C., Labarta, J., Rodriguez, G., Minkenberg, C.: Effective quality-of-service policy for capacity high-performance computing systems. In: 2012 IEEE 14th International Conference on High Performance Computing and Communication 2012 IEEE 9th International Conference on Embedded Software and Systems, pp. 598–607, June 2012.

  14. Kim, J., Dally, W.J., Scott, S., Abts, D.: Technology-driven, highly-scalable dragonfly topology. In: Proceedings - International Symposium on Computer Architecture, pp. 77–88 (2008)

    Google Scholar 

  15. Li, F., Niaki, A.A., Choffnes, D., Gill, P., Mislove, A.: A large-scale analysis of deployed traffic differentiation practices. In: Proceedings of the ACM Special Interest Group on Data Communication, Beijing China, pp. 130–144. ACM, August 2019

    Google Scholar 

  16. Mubarak, M., et al.: Evaluating quality of service traffic classes on the Megafly network. In: Weiland, M., Juckeland, G., Trinitis, C., Sadayappan, P. (eds.) ISC High Performance 2019. LNCS, vol. 11501, pp. 3–20. Springer, Cham (2019).

    CrossRef  Google Scholar 

  17. OFI Working Group: Libfabric Programmer’s manual (2020). Accessed 19 Oct 2020

  18. Savoie, L., Lowenthal, D.K., de Supinski, B.R., Mohror, K., Jain, N.: Mitigating inter-job interference via process-level quality-of-service. In: 2019 IEEE International Conference on Cluster Computing (CLUSTER), pp. 1–5 (2019)

    Google Scholar 

  19. Sensi, D.D., Girolamo, S.D., McMahon, K.H., Roweth, D., Hoefler, T.: An in-depth analysis of the slingshot interconnect. In: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC20), November 2020

    Google Scholar 

  20. Smith, S.A., et al.: Mitigating inter-job interference using adaptive flow-aware routing. In: SC18: International Conference for High Performance Computing, Networking, Storage and Analysis, pp. 346–360, November 2018

    Google Scholar 

  21. Society, T.I.: A Two Rate Three Color Marker (1999). Accessed 01 June 2020

  22. Wilke, J., Kenny, J.: Opportunities and limitations of quality-of-service in message passing applications on adaptively routed dragonfly and fat tree networks. In: 2020 IEEE International Conference on Cluster Computing (CLUSTER) (2020)

    Google Scholar 

  23. Zhang, Y., Tuncer, O., Kaplan, F., Olcoz, K., Leung, V.J., Coskun, A.K.: Level-spread: a new job allocation policy for dragonfly networks. In: 2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 1123–1132 (2018)

    Google Scholar 

Download references


This work was supported by the Argonne Leadership Computing Facility, which is a DOE Office of Science User Facility supported under Contract DE-AC02-06CH11357, and by the Exascale Computing Project – learn more at We also gratefully acknowledge the computing resources provided and operated by the Joint Laboratory for System Evaluation (JLSE) at Argonne National Laboratory.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Kevin A. Brown .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 UChicago Argonne, LLC, Operator of Argonne National Laboratory

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Brown, K.A. et al. (2021). A Tunable Implementation of Quality-of-Service Classes for HPC Networks. In: Chamberlain, B.L., Varbanescu, AL., Ltaief, H., Luszczek, P. (eds) High Performance Computing. ISC High Performance 2021. Lecture Notes in Computer Science(), vol 12728. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-78712-7

  • Online ISBN: 978-3-030-78713-4

  • eBook Packages: Computer ScienceComputer Science (R0)