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Extreme Event Analysis in Next Generation Simulation Architectures

  • Stephen Hamilton
  • Randal Burns
  • Charles Meneveau
  • Perry Johnson
  • Peter Lindstrom
  • John Patchett
  • Alexander S. Szalay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10266)

Abstract

Numerical simulations present challenges because they generate petabyte-scale data that must be extracted and reduced during the simulation. We demonstrate a seamless integration of feature extraction for a simulation of turbulent fluid dynamics. The simulation produces on the order of 6 TB per timestep. In order to analyze and store this data, we extract velocity data from a dilated volume of the strong vortical regions and also store a lossy compressed representation of the data. Both reduce data by one or more orders of magnitude. We extract data from user checkpoints in transit while they reside on temporary burst buffer SSD stores. In this way, analysis and compression algorithms are designed to meet specific time constraints so they do not interfere with simulation computations. Our results demonstrate that we can perform feature extraction on a world-class direct numerical simulation of turbulence while it is running and gather meaningful scientific data for archival and post analysis.

Keywords

Velocity Data Compression Algorithm Lossy Compression Vorticity Magnitude High Vorticity 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

The authors would like to thank Los Alamos National Laboratory for providing compute resources. Specifically we would like to thank Ryan Braithwaite who configured the Darwin cluster and setup our reservation times to run our experiments. This work is supported in part by the National Science Foundation under Grants CMMI-0941530, OCI-108849, ACI-1261715, No. OCI-1244820, and AST-0939767, Johns Hopkins University’s Institute for Data Intensive Engineering & Science, Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, and was partially supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration, and under the auspices of the U.S. Department of Energy.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of Computer ScienceJohns Hopkins UniversityBaltimoreUSA
  2. 2.Department of Mechanical EngineeringJohns Hopkins UniversityBaltimoreUSA
  3. 3.Lawrence Livermore National LaboratoryLivermoreUSA
  4. 4.Los Alamos National LaboratoryLos AlamosUSA
  5. 5.Department of Physics and AstronomyJohns Hopkins UniversityBaltimoreUSA

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