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Title: Versioned distributed arrays for resilience in scientific applications: Global view resilience

Journal Article · · Procedia Computer Science
 [1];  [2];  [2];  [1];  [3];  [1];  [2];  [3];  [4];  [4];  [5];  [5];  [6];  [6];  [7];  [7];  [8];  [8];  [8];  [2]
  1. Univ. of Chicago. Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States)
  2. Argonne National Lab. (ANL), Argonne, IL (United States)
  3. Univ. of Chicago. Chicago, IL (United States)
  4. Intel Corp. Santa Clara, CA (United States)
  5. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  6. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  7. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  8. Univ. of Chicago. Chicago, IL (United States); Argonne National Lab. (ANL), Argonne, IL (United States); Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Intel Corp. Santa Clara, CA (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
+ Show Author Affiliations

Exascale studies project reliability challenges for future high-performance computing (HPC) systems. We propose the Global View Resilience (GVR) system, a library that enables applications to add resilience in a portable, application-controlled fashion using versioned distributed arrays. We describe GVR’s interfaces to distributed arrays, versioning, and cross-layer error recovery. Using several large applications (OpenMC, the preconditioned conjugate gradient solver PCG, ddcMD, and Chombo), we evaluate the programmer effort to add resilience. The required changes are small (<2% LOC), localized, and machine-independent, requiring no software architecture changes. We also measure the overhead of adding GVR versioning and show that generally overheads <2% are achieved. We conclude that GVR’s interfaces and implementation are flexible and portable and create a gentle-slope path to tolerate growing error rates in future systems.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States); Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States); Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States); Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR)
Grant/Contract Number:
SC0008603; AC02-06CH11357; AC02-05CH11231; AC04-94AL85000
OSTI ID:
1196863
Alternate ID(s):
OSTI ID: 1214658
Journal Information:
Procedia Computer Science, Vol. 51, Issue C; Conference: International Conference On Computational Science (ICCS 2015). Computational Science at the Gates of Nature, Reykjavik (Iceland), 1-3 Jun 2015; ISSN 1877-0509
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 12 works
Citation information provided by
Web of Science

References (13)

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Cited By (3)

Failure Recovery in Resilient X10 journal July 2019
Language Support for Reliable Memory Regions preprint January 2016
Resilience and fault tolerance in high-performance computing for numerical weather and climate prediction journal February 2021

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Related Subjects

97 MATHEMATICS AND COMPUTING
resilience
fault tolerance
exascale
scalable computing
application-based fault tolerance