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Best Practices for Administering a Medium Sized Cluster with Intel® Xeon Phi™ Coprocessors

Published: 13 July 2014 Publication History

Abstract

This work describes the best practices for configuring and managing an Intel® Xeon Phi™ cluster. The Xeon Phi presents a unique environment to the user and preparing this environment requires unique procedures. This work will outline these procedures and provide examples for HPC Administrators to utilize and then customize for their system. Considerable effort has been put forth to help researchers determine how to maximize their performance on the Xeon Phi, but little has been done for the administrators of these systems. Now that the Xeon Phis are being deployed on larger systems, there is a need for information on how to manage and deploy these systems. The information provided here will serve as a supplement to the documentation Intel provides in order to bridge the gap between workstation and cluster deployments. This work is based on the authors experiences deploying and maintaining the Beacon cluster at the University of Tennessee's Application Acceleration Center of Excellence (AACE).

References

[1]
Intel Manycore Platform Software stack (MPSS). http://software.intel.com/en-us/articles/intel-manycore-platform-software-stack-mpss, 2014. {Online; accessed 9-March-2014}.
[2]
TORQUE Resource Manager - Adaptive Computing. http://www.adaptivecomputing.com/products/open-source/torque/, 2014. {Online; accessed 9-March-2014}.
[3]
Moab HPC Suite Basic Edition - Adaptive Computing. http://www.adaptivecomputing.com/products/hpc-products/moab-hpc-basic-edition/, 2014. {Online; accessed 9-March-2014}.
[4]
Modules -- Software Environment Management. http://modules.sourceforge.net/, 2014. {Online; accessed 9-March-2014}.
[5]
pdsh - Parallel Distributed Shell. https://code.google.com/p/pdsh/, 2014. {Online; accessed 9-March-2014}.
[6]
MPSS Users Guide. http://registrationcenter.intel.com/irc_nas/3988/MPSS_Users_Guide.pdf, 2014. {Online; accessed 9-March-2014}.
[7]
Example Files. http://http://www.nics.tennessee.edu/~peltz/examples.tar.bz2, 2014. {Online; accessed 9-March-2014}.
[8]
Ganglia Monitoring System. http://ganglia.sourceforge.net/, 2014. {Online; accessed 9-March-2014}.
[9]
J.S. Vetter, R. Glassbrook, J. Dongarra, K. Schwan, B. Loftis, S. McNally, J. Meredith, J. Rogers, P. Roth, K. Spafford, and S. Yalamanchili. Keeneland: Bringing Heterogeneous GPU Computing to the Computational Science Community. IEEE Computing in Science and Engineering, 13:90--5, August 2011. {Online; accessed 9-March-2014}.

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Information & Contributors

Information

Published In

cover image ACM Other conferences
XSEDE '14: Proceedings of the 2014 Annual Conference on Extreme Science and Engineering Discovery Environment
July 2014
445 pages
ISBN:9781450328937
DOI:10.1145/2616498
  • General Chair:
  • Scott Lathrop,
  • Program Chair:
  • Jay Alameda
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]

In-Cooperation

  • NSF: National Science Foundation
  • Drexel University
  • Indiana University: Indiana University

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 13 July 2014

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Author Tags

  1. HPC
  2. Intel MIC
  3. Intel Xeon Phi
  4. JICS
  5. NICS
  6. accelerators
  7. administration
  8. best practice
  9. cluster
  10. coprocessors

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  • Research-article
  • Research
  • Refereed limited

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XSEDE '14

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XSEDE '14 Paper Acceptance Rate 80 of 120 submissions, 67%;
Overall Acceptance Rate 129 of 190 submissions, 68%

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