Skip to main content

Exploiting 3D Memory for Accelerated In-Network Processing of Hash Joins in Distributed Databases

  • Conference paper
  • First Online:
Applied Reconfigurable Computing. Architectures, Tools, and Applications (ARC 2021)

Abstract

The computing potential of programmable switches with multi-Tbit/s throughput is of increasing interest to the research community and industry alike. Such systems have already been employed in a wide spectrum of applications, including statistics gathering, in-network consensus protocols, or application data caching. Despite their high throughput, most architectures for programmable switches have practical limitations, e.g., with regard to stateful operations.

FPGAs, on the other hand, can be used to flexibly realize switch architectures for far more complex processing operations. Recently, FPGAs have become available that feature 3D-memory, such as HBM stacks, that is tightly integrated with their logic element fabrics. In this paper, we examine the impact of exploiting such HBM to accelerate an inter-server join operation at the switch-level between the servers of a distributed database system. As the hash-join algorithm used for high performance needs to maintain a large state, it would overtax the capabilities of conventional software-programmable switches.

The paper shows that across eight 10G Ethernet ports, the single HBM-FPGA in our prototype can not only keep up with the demands of over 60 Gbit/s of network throughput, but it also beats distributed-join implementations that do not exploit in-network processing.

This work was partially funded by the DFG Collaborative Research Center 1053 (MAKI) and by the German Federal Ministry for Education and Research (BMBF) with the funding ID 16ES0999. The authors would like to thank Xilinx Inc. for supporting their work by donations of hard- and software.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Blanas, S., Li, Y., Patel, J.M.: Design and evaluation of main memory hash join algorithms for multi-core CPUs. In: Proceedings of the ACM SIGMOD International Conference on Management of Data, SIGMOD 2011, Athens, Greece, 12–16 June 2011, pp. 37–48. ACM (2011)

    Google Scholar 

  2. Blöcher, M., Ziegler, T., Binnig, C., Eugster, P.: Boosting scalable data analytics with modern programmable networks. In: Proceedings of the 14th International Workshop on Data Management on New Hardware. DAMON 2018. Association for Computing Machinery, New York (2018)

    Google Scholar 

  3. DeWitt, D.J., Katz, R.H., et al.: Implementation techniques for main memory database systems. SIGMOD Rec. 14(2), 1–8 (1984)

    Article  Google Scholar 

  4. Dreseler, M., Boissier, M., Rabl, T., Uflacker, M.: Quantifying TPC-H choke points and their optimizations. Proc. VLDB Endow. 13(8), 1206–1220 (2020)

    Article  Google Scholar 

  5. Firestone, D., Putnam, A., et al.: Azure accelerated networking: Smartnics in the public cloud. In: Proceedings of the 15th USENIX Conference on Networked Systems Design and Implementation, NSDI 2018, pp. 51–64. USENIX Association, USA (2018)

    Google Scholar 

  6. Gustavo, A., Binnig, C., et al.: DPI: the data processing interface for modern networks. In: Proceedings of CIDR 2019 (2019)

    Google Scholar 

  7. Heinz, C., Hofmann, J., Korinth, J., Sommer, L., Weber, L., Koch, A.: The TaPaSCo open-source toolflow. J. Signal Process. Syst. 93, 1–19 (2021). https://doi.org/10.1007/s11265-021-01640-8

    Article  Google Scholar 

  8. Hofmann, J., Thostrup, L., Ziegler, T., Binnig, C., Koch, A.: High-performance in-network data processing. In: International Workshop on Accelerating Analytics and Data Management Systems Using Modern Processor and Storage Architectures, ADMS@VLDB 2019, Los Angeles, United States (2019)

    Google Scholar 

  9. Kimball, R., Ross, M.: The Data Warehouse Toolkit: The Complete Guide to Dimensional Modeling, 2nd edn. Wiley, Hoboken (2002)

    Google Scholar 

  10. Preshing, J.: Hash collision probabilities (2011). https://preshing.com/20110504/hash-collision-probabilities/

  11. Rödiger, W., Mühlbauer, T., Kemper, A., Neumann, T.: High-speed query processing over high-speed networks. Proc. VLDB Endow. 9(4), 228–239 (2015)

    Article  Google Scholar 

  12. Sapio, A., Abdelaziz, I., et al.: In-network computation is a dumb idea whose time has come. In: Proceedings of the 16th ACM Workshop on Hot Topics in Networks, pp. 150–156. HotNets-XVI, Association for Computing Machinery, New York (2017)

    Google Scholar 

  13. Wellons, C.: Hash function prospector (2020). https://github.com/skeeto/hash-prospector

  14. Zilberman, N., Audzevich, Y., Covington, G.A., Moore, A.W.: NetFPGA SUME: toward 100 Gbps as research commodity. IEEE Micro 34(5), 32–41 (2014)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Johannes Wirth .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wirth, J., Hofmann, J.A., Thostrup, L., Koch, A., Binnig, C. (2021). Exploiting 3D Memory for Accelerated In-Network Processing of Hash Joins in Distributed Databases. In: Derrien, S., Hannig, F., Diniz, P.C., Chillet, D. (eds) Applied Reconfigurable Computing. Architectures, Tools, and Applications. ARC 2021. Lecture Notes in Computer Science(), vol 12700. Springer, Cham. https://doi.org/10.1007/978-3-030-79025-7_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-79025-7_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-79024-0

  • Online ISBN: 978-3-030-79025-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics