Lessons learned at 208K: Towards Debugging Millions of Cores

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Petascale systems will present several new challenges to performance and correctness tools. Such machines may contain millions of cores, requiring that tools use scalable data structures and analysis algorithms to collect and to process application data. In addition, at such scales, each tool itself will become a large parallel application--already, debugging the full Blue-Gene/L (BG/L) installation at the Lawrence Livermore National Laboratory requires employing 1664 tool daemons. To reach such sizes and beyond, tools must use a scalable communication infrastructure and manage their own tool processes efficiently. Some system resources, such as the file system, may also become tool bottlenecks. ... continued below

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PDF-file: 11 pages; size: 0.6 Mbytes

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Lee, G L; Ahn, D H; Arnold, D C; de Supinski, B R; Legendre, M; Miller, B P et al. April 14, 2008.

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Petascale systems will present several new challenges to performance and correctness tools. Such machines may contain millions of cores, requiring that tools use scalable data structures and analysis algorithms to collect and to process application data. In addition, at such scales, each tool itself will become a large parallel application--already, debugging the full Blue-Gene/L (BG/L) installation at the Lawrence Livermore National Laboratory requires employing 1664 tool daemons. To reach such sizes and beyond, tools must use a scalable communication infrastructure and manage their own tool processes efficiently. Some system resources, such as the file system, may also become tool bottlenecks. In this paper, we present challenges to petascale tool development, using the Stack Trace Analysis Tool (STAT) as a case study. STAT is a lightweight tool that gathers and merges stack traces from a parallel application to identify process equivalence classes. We use results gathered at thousands of tasks on an Infiniband cluster and results up to 208K processes on BG/L to identify current scalability issues as well as challenges that will be faced at the petascale. We then present implemented solutions to these challenges and show the resulting performance improvements. We also discuss future plans to meet the debugging demands of petascale machines.

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PDF-file: 11 pages; size: 0.6 Mbytes

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  • Presented at: SuperComputing 2008, Austin, TX, United States, Nov 15 - Nov 21, 2008

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  • Report No.: LLNL-CONF-402967
  • Grant Number: W-7405-ENG-48
  • Office of Scientific & Technical Information Report Number: 945138
  • Archival Resource Key: ark:/67531/metadc902253

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Office of Scientific & Technical Information Technical Reports

Reports, articles and other documents harvested from the Office of Scientific and Technical Information.

Office of Scientific and Technical Information (OSTI) is the Department of Energy (DOE) office that collects, preserves, and disseminates DOE-sponsored research and development (R&D) results that are the outcomes of R&D projects or other funded activities at DOE labs and facilities nationwide and grantees at universities and other institutions.

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  • April 14, 2008

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  • Sept. 27, 2016, 1:39 a.m.

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  • Nov. 29, 2016, 6:29 p.m.

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Lee, G L; Ahn, D H; Arnold, D C; de Supinski, B R; Legendre, M; Miller, B P et al. Lessons learned at 208K: Towards Debugging Millions of Cores, article, April 14, 2008; Livermore, California. (digital.library.unt.edu/ark:/67531/metadc902253/: accessed November 20, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; crediting UNT Libraries Government Documents Department.