Supercomputing and Computation

SHARE

Ultrascale Predictive Analysis Toolbox


Problem Statement:

  • Supercomputing’s major successes to date have come from discovery through simulation rather than discovery through analysis
  • Traditional multivariate analysis is not scalable to extremes; high-dimensional data are broken into subsets that ignore system-level context
  • Can we tractably analyze ultrascale complex-systems data, learning from the system-wide interactions?

Technical Approach:

  • Use system-level interaction information to address the sample complexity problem.
  • Treat variables as tasks in a multi-task machine learning approach.
  • Provide the capability for scientists to explore ultrascale datasets in new ways.

Advantage over the State-of-the-Art:

  • Rather than being stymied by complexity, our approach takes advantage of complex interdependencies in a system to learn in ways that aren’t possible in less complex systems
  • Allows data from different time periods, different locations, and even different variables to be used together without typical fusion problems, such as data imputation for missing variables.

 

ASK ORNL

We're always happy to get feedback from our users. Please use the Comments form to send us your comments, questions, and observations.