The SDAV Software Frameworks for Visualization and Analysis on Next-Generation Multi-Core and Many-Core Architectures

  • Authors:

  • Christopher Sewell;Jeremy Meredith;Kenneth Moreland;Tom Peterka;Dave DeMarle;Li-ta Lo;James Ahrens;Robert Maynard;Berk Geveci

  • Affiliations:

  • -;-;-;-;-;-;-;-;-

  • Venue:
  • SCC '12 Proceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis
  • Year:
  • 2012

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper surveys the four software frameworks being developed as part of the visualization pillar of the SDAV (Scalable Data Management, Analysis, and Visualization) Institute, one of the SciDAC (Scientific Discovery through Advanced Computing) Institutes established by the ASCR (Advanced Scientific Computing Research) Program of the U.S. Department of Energy. These frameworks include EAVL (Extreme-scale Analysis and Visualization Library), DAX (Data Analysis at Extreme), DIY (Do It Yourself), and PISTON. The objective of these frameworks is to facilitate the adaptation of visualization and analysis algorithms to take advantage of the available parallelism in emerging multi-core and many-core hardware architectures, in anticipation of the need for such algorithms to be run in-situ with LCF (leadership-class facilities) simulation codes on supercomputers.