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Abstractions for supercomputing platforms to facilitate consolidation of services: poster

Published:11 December 2017Publication History

ABSTRACT

Supercomputing platforms have developed over the years to maximize performance and scalability and to minimize any abstractions that could result in slowdowns and performance variability. Largescale supercomputing systems traditionally tightly integrate hardware and software stacks, for instance, light weight operating systems, vertically integrated communication layers and tightly coupled parallel file systems. Consequently, these environments do not lend themselves for portability expected for web and ISV applications as well as emerging data science workflows. This poster will present abstractions that are introduced in Piz Daint, a hybrid and heterogeneous Cray XC50 and XC40 platform with Nvidia P100 GPU devices, in order to enable Large Hadron Collider (LHC) computing grid workflows. This transition is completely transparent to the end users and existing workflows for experiments including ATLAS, CMS and LHCb. The technologies and solutions that have been exploited in creating abstractions include containerization, file system virtualization and other configuration changes. Incidentally, these abstractions have proven to be highly effective in leveraging execution of other complex workflows, for instance, Apache Spark, often with performance and productivity gains. Piz Daint hybrid and heterogeneous supercomputing platform has therefore enabled the Swiss National Supercomputing Centre (CSCS) in consolidating a range of services including computing, data analysis and visualization for its diverse and growing user communities.

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    • Published in

      cover image ACM Conferences
      Middleware '17: Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference: Posters and Demos
      December 2017
      30 pages
      ISBN:9781450352017
      DOI:10.1145/3155016

      Copyright © 2017 Owner/Author

      Permission to make digital or hard copies of part or all 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 third-party components of this work must be honored. For all other uses, contact the Owner/Author.

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 11 December 2017

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      • poster

      Acceptance Rates

      Middleware '17 Paper Acceptance Rate12of17submissions,71%Overall Acceptance Rate203of948submissions,21%

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