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Exploration of Exascale In Situ Visualization and Analysis Approaches. IMD. Exascale and “Big Data” running simulations, running experiments, static repositories, etc. Novel Ideas.

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Exploration of Exascale In Situ Visualization

and Analysis Approaches


Exascale and “Big Data”

running simulations, running experiments, static repositories, etc.

Novel Ideas

Perception, cognitive capacity, and computer bandwidth are limited, but the scale of data continues to increase. In order to save both compute cycles and analyst time, we explicitly select and present data to the scientist:

  • Time, space, and variable selection algorithms
  • Store and index selected data products
  • Interactive presentation and query methods
  • Illustratively and artistically highlight to perceptually drive scientists to key data

Data Selection Algorithms (time, space, variable, product, etc.)




Analysis with Perceptually-Driven Highlighting

Expected Impact

Milestones and Status

  • In our first year, we have developed several data selection algorithms, designed a prototype visualization and analysis system that utilizes selected data, and quantified the effects on selecting scientific data.
  • Milestone Expected Actual
  • Data Selection Algorithms 03/13 03/13
  • Data Product Explorer 09/13 09/13
  • Selection Quality Measurements 09/13 09/13
  • Advanced Selection Algorithms 03/14
  • Perceptually-Driven Presentation 03/14
  • Selection in Data Product Explorer 09/14
  • Domain-Driven Selection 03/15
  • Bandwidth Utilization Adjustment 09/15
  • Advanced Presentation Methods 09/15
  • Save compute time as only selected data are stored with limited I/O bandwidth and capacity
    • Not all data can be saved from an exascale simulation, therefore we must be prescriptive on what data are saved
    • We will provide data selection algorithms
  • Save analysis time as selected data are presented to the scientist
    • The resulting data will still be massive, more than any one scientist can look at
    • Interaction, query, and highlighting methods drive scientists to view key data

Principal Investigator: James Ahrens et al., LANL

Sept. 25, 2013