2010OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information)Open access

Data-intensive computing on numerically-insensitive supercomputers

James Ahrens, Patricia Fasel, Salman Habib, Katrin Heitmann, Li Ta Lo, John Patchett, Sean Williams, Jonathan Woodring, Joshua H. Wu, Chung Hsing Hsu

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Abstract

With the advent of the era of petascale supercomputing, via the delivery of the Roadrunner supercomputing platform at Los Alamos National Laboratory, there is a pressing need to address the problem of visualizing massive petascale-sized results. In this presentation, I discuss progress on a number of approaches including in-situ analysis, multi-resolution out-of-core streaming and interactive rendering on the supercomputing platform. These approaches are placed in context by the emerging area of data-intensive supercomputing.

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What this paper is about

With the advent of the era of petascale supercomputing, via the delivery of the Roadrunner supercomputing platform at Los Alamos National Laboratory, there is a pressing need to address the problem of visualizing massive petascale-sized results. In this presentation, I discuss progress on a number of approaches including in-situ analysis, multi-resolution out-of-core streaming and interactive rendering on the supercomputing platform. These approaches are placed in context by the emerging area of data-intensive supercomputing.

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Available abstract

With the advent of the era of petascale supercomputing, via the delivery of the Roadrunner supercomputing platform at Los Alamos National Laboratory, there is a pressing need to address the problem of visualizing massive petascale-sized results. In this presentation, I discuss progress on a number of approaches including in-situ analysis, multi-resolution out-of-core streaming and interactive rendering on the supercomputing platform. These approaches are placed in context by the emerging area of data-intensive supercomputing.

Key concepts: Petascale computing, Supercomputer, Computer science, Context (archaeology), Many core, High resolution, Parallel computing, Computational science

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