From mesh generation to scientific visualization: an end-to-end approach to parallel supercomputing

  • Authors:

  • Tiankai Tu;Hongfeng Yu;Leonardo Ramirez-Guzman;Jacobo Bielak;Omar Ghattas;Kwan-Liu Ma;David R. O'Hallaron

  • Affiliations:

  • Carnegie Mellon University, Pittsburgh, PA;University of California, Davis, CA;Carnegie Mellon University, Pittsburgh, PA;Carnegie Mellon University, Pittsburgh, PA;The University of Texas at Austin, Austin, TX;University of California, Davis, CA;Carnegie Mellon University, PA

  • Venue:
  • Proceedings of the 2006 ACM/IEEE conference on Supercomputing
  • Year:
  • 2006

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Abstract

Parallel supercomputing has traditionally focused on the inner kernel of scientific simulations: the solver. The front and back ends of the simulation pipeline---problem description and interpretation of the output---have taken a back seat to the solver when it comes to attention paid to scalability and performance, and are often relegated to offline, sequential computation. As the largest simulations move beyond the realm of the terascale and into the petascale, this decomposition in tasks and platforms becomes increasingly untenable. We propose an end-to-end approach in which all simulation components---meshing, partitioning, solver, and visualization---are tightly coupled and execute