Skip to main content
Springer Nature Link
Log in

Scaling SQL to the Supercomputer for Interactive Analysis of Simulation Data

  • Conference paper
  • First Online:
Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation (SMC 2021)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1512))

Included in the following conference series:

  • 1154 Accesses

Abstract

AI and simulation workloads consume and generate large amounts of data that need to be searched, transformed and merged with other data. With the goal of treating data as a first-class citizen inside a traditionally compute-centric HPC environment, we explore how the use of accelerators and high-speed interconnects can speed up tasks which otherwise constitute bottlenecks in computational discovery workflows. BlazingSQL is SQL engine that runs natively on NVIDIA GPUs and supports internode communication for fast analytics on terabyte-scale tabular data sets. We show how a fast interconnect improves query performance if leveraged through the Unified Communication X (UCX) middleware. We envision that future computing platforms will integrate accelerated database query capabilities for immediate and interactive analysis of large simulation data.

This manuscript has been authored by UT-Battelle, LLC, under contract DE-AC05-00OR22725 with the US Department of Energy (DOE). The US government retains and the publisher, by accepting the article for publication, acknowledges that the US government retains a nonexclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for US government purposes. DOE will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    https://openucx.github.io/ucx/api/v1.10/html/group___u_c_p___w_o_r_k_e_r.html.

  2. 2.

    https://openucx.github.io/ucx/api/v1.10/html/group___u_c_p___c_o_m_m.html.

  3. 3.

    https://github.com/rapidsai/gpu-bdb.

  4. 4.

    http://tpc.org/tpcx-bb/default5.asp.

References

  1. dask-sql. https://github.com/dask-contrib/dask-sql (2021). Accessed 5 Nov 2021

  2. Bakkum, P., Skadron, K.: Accelerating SQL database operations on a GPU with CUDA. In: Proceedings of the 3rd Workshop on General-Purpose Computation on Graphics Processing Units, GPGPU-3, pp. 94–103. Association for Computing Machinery, New York, NY, USA (2010). https://doi.org/10.1145/1735688.1735706

  3. BlazingSQL: high performance SQL engine on RAPIDS AI. https://blazingsql.com/ (2021). Accessed 08 Oct 2021

  4. Breß, S., Saake, G.: Why it is time for a HyPE: a hybrid query processing engine for efficient GPU coprocessing in DBMS. Proc. VLDB Endow. 6(12), 1398–1403 (2013). https://doi.org/10.14778/2536274.2536325

    Article  Google Scholar 

  5. Bre, S., Beier, F., Rauhe, H., Sattler, K.U., Schallehn, E., Saake, G.: Efficient co-processor utilization in database query processing. Inf. Syst. 38(8), 1084–1096 (2013). https://www.sciencedirect.com/science/article/pii/S0306437913000732

  6. Chapman, B., et al.: Introducing OpenSHMEM: SHMEM for the PGAS community. In: Proceedings of the Fourth Conference on Partitioned Global Address Space Programming Model, pp. 1–3 (2010)

    Google Scholar 

  7. Chrysogelos, P., Sioulas, P., Ailamaki, A.: Hardware-conscious query processing in GPU-accelerated analytical engines. In: Proceedings of the 9th Biennial Conference on Innovative Data Systems Research. No. CONF (2019)

    Google Scholar 

  8. Dean, J., Ghemawat, S.: MapReduce: simplified data processing on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  9. DeWitt, D., Gray, J.: Parallel database systems: the future of high performance database systems. Commun. ACM 35(6), 85–98 (1992). https://doi.org/10.1145/129888.129894

  10. Fang, R., et al.: GPUQP: query co-processing using graphics processors. In: Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data. SIGMOD 2007, pp. 1061–1063. Association for Computing Machinery, New York, NY, USA (2007). https://doi.org/10.1145/1247480.1247606

  11. Fang, W., He, B., Luo, Q.: Database compression on graphics processors. Proc. VLDB Endow. 3(1–2), 670–680 (2010). https://doi.org/10.14778/1920841.1920927

  12. Glaser, J., et al.: High-throughput virtual laboratory for drug discovery using massive datasets. Int. J. High Perform. Comput. Appl. 35, 452–468 (2021). https://doi.org/10.1177/10943420211001565

  13. Govindaraju, N.K., Lloyd, B., Wang, W., Lin, M., Manocha, D.: Fast computation of database operations using graphics processors. In: Proceedings of the 2004 ACM SIGMOD International Conference on Management of Data. SIGMOD 2004, pp. 215–226. Association for Computing Machinery, New York, NY, USA (2004). https://doi.org/10.1145/1007568.1007594

  14. He, B., Relational query coprocessing on graphics processors. ACM Trans. Database Syst. 34(4) (2009). https://doi.org/10.1145/1620585.1620588

  15. Hernández, B., et al.: Performance evaluation of Python based data analytics frameworks in summit: early experiences. In: Nichols, J., Verastegui, B., Maccabe, A.B., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds.) SMC 2020. CCIS, vol. 1315, pp. 366–380. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-63393-6_24

    Chapter  Google Scholar 

  16. Huebl, A.: OpenPMD release 1.4.0 with support for data processing through dask. https://github.com/openPMD/openPMD-api/releases/tag/0.14.0 (2021)

  17. Lee, S., Park, S.: Performance analysis of big data ETL process over CPU-GPU heterogeneous architectures. In: 2021 IEEE 37th International Conference on Data Engineering Workshops (ICDEW), pp. 42–47 (2021)

    Google Scholar 

  18. Lu, X., et al.: High-performance design of hadoop RPC with RDMA over InfiniBand. In: 2013 42nd International Conference on Parallel Processing, pp. 641–650 (2013)

    Google Scholar 

  19. NVIDIA: Open GPU data science-RAPIDS. https://rapids.ai (2021). Accessed 26 May 2021

  20. Olsen, S., Romoser, B., Zong, Z.: SQLPhi: a SQL-based database engine for intel Xeon Phi coprocessors. In: Proceedings of the 2014 International Conference on Big Data Science and Computing. BigDataScience 2014. Association for Computing Machinery, New York, NY, USA (2014). https://doi.org/10.1145/2640087.2644172

  21. OmniSciDB: OmniSciDB: open source SQL-based, relational, columnar database engine. https://github.com/omnisci/omniscidb (2021). Accessed 26 May 2021

  22. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011)

    MathSciNet  MATH  Google Scholar 

  23. PGStrom: PG-Strom: a GPU extension module of PostgreSQL. https://github.com/heterodb/pg-strom (2021). Accessed 26 May 2021

  24. Poeschel, F., et al.: Transitioning from file-based HPC workflows to streaming data pipelines with openPMD and ADIOS2. arXiv preprint arXiv:2107.06108 (2021)

  25. Shamis, P., et al.: UCX: an open source framework for HPC network APIs and beyond. In: 2015 IEEE 23rd Annual Symposium on High-Performance Interconnects, pp. 40–43. IEEE (2015)

    Google Scholar 

  26. Shehab, E., Algergawy, A., Sarhan, A.: Accelerating relational database operations using both CPU and GPU co-processor. Comput. Electr. Eng. 57, 69–80 (2017). https://www.sciencedirect.com/science/article/pii/S0045790616310631

  27. The pandas development team: pandas-dev/pandas: Pandas (2020). https://doi.org/10.5281/zenodo.3509134

  28. UCX: UCX Client-Server. https://openucx.github.io/ucx/api/v1.10/html/ucp_client_server_8c-example.html (2021). Accessed 26 May 2021

  29. Weininger, D.: SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules. J. Chem. Inf. Comput. Sci. 28(1), 31–36 (1988)

    Google Scholar 

  30. Woods, L., István, Z., Alonso, G.: Ibex: an intelligent storage engine with support for advanced SQL offloading. Proc. VLDB Endow. 7(11), 963–974 (2014). https://doi.org/10.14778/2732967.2732972

Download references

Acknowledgments

We are grateful to Oscar Hernandez (NVIDIA) for initial conceptualization of this research. We thank Arjun Shankar (ORNL) for support. This research used resources of the Oak Ridge Leadership Computing Facility (OLCF) at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Author information

Authors and Affiliations

  1. Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN, 37831, USA

    Jens Glaser, Benjamín Hernández & Matthew Baker

  2. Voltron Data, Inc., Mountain View, USA

    Felipe Aramburú, William Malpica & Rodrigo Aramburú

Authors
  1. Jens Glaser
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Felipe Aramburú
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. William Malpica
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Benjamín Hernández
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Matthew Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Rodrigo Aramburú
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jens Glaser .

Editor information

Editors and Affiliations

  1. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Jeffrey Nichols

  2. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Arthur ‘Barney’ Maccabe

  3. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    James Nutaro

  4. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Swaroop Pophale

  5. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Pravallika Devineni

  6. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Theresa Ahearn

  7. Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Becky Verastegui

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Glaser, J., Aramburú, F., Malpica, W., Hernández, B., Baker, M., Aramburú, R. (2022). Scaling SQL to the Supercomputer for Interactive Analysis of Simulation Data. In: Nichols, J., et al. Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation. SMC 2021. Communications in Computer and Information Science, vol 1512. Springer, Cham. https://doi.org/10.1007/978-3-030-96498-6_19

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-96498-6_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-96497-9

  • Online ISBN: 978-3-030-96498-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics