skip to main content
10.1145/3343211.3343215acmotherconferencesArticle/Chapter ViewAbstractPublication PageseurompiConference Proceedingsconference-collections
research-article

QMPI: a next generation MPI profiling interface for modern HPC platforms

Published:11 September 2019Publication History

ABSTRACT

As we approach exascale and start planning for beyond, the rising complexity of systems and applications demands new monitoring, analysis, and optimization approaches. This requires close coordination with the parallel programming system used, which for HPC in most cases includes MPI, the Message Passing Interface. While MPI provides comprehensive tool support in the form of the MPI Profiling interface, PMPI, which has inspired a generation of tools, it is not sufficient for the new arising challenges. In particular, it does not support modern software design principles nor the composition of multiple monitoring solutions from multiple agents or sources. We approach these gaps and present QMPI, as a possible successor to PMPI. In this paper, we present the use cases and requirements that drive its development, offer a prototype design and implementation, and demonstrate its effectiveness and low overhead.

References

  1. A. Netti, M. Müller, A. Auweter, C. Guillen, M. Ott, D. Tafani, and M. Schulz. 2019. From Facility to Application Sensor Data: Modular, Continuous and Holistic Monitoring with DCDB. (Nov 2019), 1--12.Google ScholarGoogle Scholar
  2. Jack Dongarra, Michael A Heroux, and Piotr Luszczek. 2016. High-performance conjugate-gradient benchmark: A new metric for ranking high-performance computing systems. The International Journal of High Performance Computing Applications 30, 1 (2016), 3--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. Jack Dongarra, Piotr Luszczek, and Michael A Heroux. 2013. HPCG Technical Specification. Sandia National Laboratories. https://prod.sandia.gov/techlib-noauth/access-control.cgi/2013/138752.pdfGoogle ScholarGoogle Scholar
  4. Jonathan Eastep, Steve Sylvester, Christopher Cantalupo, Brad Geltz, Federico Ardanaz, Asma Al-Rawi, Kelly Livingston, Fuat Keceli, Matthias Maiterth, and Siddhartha Jana. 2017. Global Extensible Open Power Manager: A Vehicle for HPC Community Collaboration on Co-Designed Energy Management Solutions. In High Performance Computing, Julian M. Kunkel, Rio Yokota, Pavan Balaji, and David Keyes (Eds.). Springer International Publishing, Cham, 394--412.Google ScholarGoogle Scholar
  5. Edgar Gabriel, Graham E. Fagg, George Bosilca, Thara Angskun, Jack J. Dongarra, Jeffrey M. Squyres, Vishal Sahay, Prabhanjan Kambadur, Brian Barrett, Andrew Lumsdaine, Ralph H. Castain, David J. Daniel, Richard L. Graham, and Timothy S. Woodall. 2004. Open MPI: Goals, Concept, and Design of a Next Generation MPI Implementation. In Proceedings, 11th European PVM/MPI Users' Group Meeting. Budapest, Hungary, 97--104.Google ScholarGoogle Scholar
  6. Todd Gamblin. 2016. Measuring and Analyzing Entire HPC Centers: The Sonar Project at LLNL. Invited talk at the Tokyo Institute of Technology, Tokyo, Japan.Google ScholarGoogle Scholar
  7. Marc-André Hermanns, Nathan T Hjlem, Michael Knobloch, Kathryn Mohror, and Martin Schulz. 2018. Enabling callback-driven runtime introspection via MPI_T. In Proceedings of the 25th European MPI Users' Group Meeting. ACM, 8. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. HPC Advisory Council. 2014. HPCG Performance Benchmark and Profiling. http://www.hpcadvisorycouncil.com/pdf/HPCG_Analysis_and_Profiling.pdfGoogle ScholarGoogle Scholar
  9. IBM Corporation. 2017. IBM Spectrum MPI V10.1 documentation. https://www.ibm.com/support/knowledgecenter/en/SSZTET_10.1/smpi_welcome/smpi_welcome.html. Accessed on 07.05.2019.Google ScholarGoogle Scholar
  10. Tanzima Islam, Kathryn Mohror, and Martin Schulz. 2016. Exploring the MPI tool information interface: features and capabilities. The International Journal of High Performance Computing Applications 30, 2 (2016), 212--222. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. Robert Mijaković, Antonio Pimenta Soto, Isaías A Comprés Ureña, Michael Gerndt, Anna Sikora, and Eduardo César. 2014. Specification of periscope tuning framework plugins. (2014), 123--132.Google ScholarGoogle Scholar
  12. M. Schulz and B. R. De Supinski. 2006. A Flexible and Dynamic Infrastructure for MPI Tool Interoperability. (Aug 2006), 193--202. Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. M. Schulz and B. R. de Supinski. 2007. PNMPI tools: a whole lot greater than the sum of their parts. (Nov 2007), 1--10. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. M. Schulz S.Rasmussen and K. Mohror. 2016. Allowing MPI tools builders to forget about Fortran. (2016), 208--211. Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. The Ohio State University. 2018. MVAPICH: MPI over InfiniBand, Omni-Path, Ethernet/iWARP, and RoCE. http://mvapich.cse.ohio-state.edu/benchmarks/. Accessed on 07.05.2019.Google ScholarGoogle Scholar
  16. Ulrike Yang, Robert Falgout, and Jongsoo Park. 2017. Algebraic Multigrid Benchmark, Version 00. https://www.osti.gov//servlets/purl/1389816. Accessed on 07.05.2019.Google ScholarGoogle Scholar

Index Terms

  1. QMPI: a next generation MPI profiling interface for modern HPC platforms

    Recommendations

    Comments

    Login options

    Check if you have access through your login credentials or your institution to get full access on this article.

    Sign in
    • Published in

      cover image ACM Other conferences
      EuroMPI '19: Proceedings of the 26th European MPI Users' Group Meeting
      September 2019
      134 pages
      ISBN:9781450371759
      DOI:10.1145/3343211

      Copyright © 2019 ACM

      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 September 2019

      Permissions

      Request permissions about this article.

      Request Permissions

      Check for updates

      Qualifiers

      • research-article

      Acceptance Rates

      EuroMPI '19 Paper Acceptance Rate13of26submissions,50%Overall Acceptance Rate66of139submissions,47%

    PDF Format

    View or Download as a PDF file.

    PDF

    eReader

    View online with eReader.

    eReader