High performance multivariate visual data exploration for extremely large data

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High performance multivariate visual data exploration for extremely large data

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Conference on High Performance Networking and Computing archive
Proceedings of the 2008 ACM/IEEE conference on Supercomputing - Volume 00 table of contents

Austin, Texas

SECTION: Papers table of contents

Article No. 51

Year of Publication: 2008

ISBN:978-1-4244-2835-9

Authors

Oliver Rübel	Lawrence Berkeley National Laboratory, Berkeley, CA and University of California, Davis, CA and Technische Universität Kaiserslautern, Kaiserslautern, Germany
Prabhat	Lawrence Berkeley National Laboratory, Berkeley, CA
Kesheng Wu	Lawrence Berkeley National Laboratory, Berkeley, CA
Hank Childs	Lawrence Livermore National Laboratory, Livermore, CA
Jeremy Meredith	Oak Ridge National Laboratory, Oak Ridge, TN
Cameron G. R. Geddes	LOASIS program of Lawrence Berkeley National Laboratory, Berkeley, CA
Estelle Cormier-Michel	LOASIS program of Lawrence Berkeley National Laboratory, Berkeley, CA
Sean Ahern	Oak Ridge National Laboratory, Oak Ridge, TN
Gunther H. Weber	Lawrence Berkeley National Laboratory, Berkeley, CA
Peter Messmer	Tech-X Corporation, Boulder, CO
Hans Hagen	Technische Universität Kaiserslautern, Kaiserslautern, Germany
Bernd Hamann	Lawrence Berkeley National Laboratory, Berkeley, CA and University of California, Davis, CA and Technische Universität Kaiserslautern, Kaiserslautern, Germany
E. Wes Bethel	Lawrence Berkeley National Laboratory, Berkeley, CA and University of California, Davis, CA

Publisher

IEEE Press Piscataway, NJ, USA

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ABSTRACT

One of the central challenges in modern science is the need to quickly derive knowledge and understanding from large, complex collections of data. We present a new approach that deals with this challenge by combining and extending techniques from high performance visual data analysis and scientific data management. This approach is demonstrated within the context of gaining insight from complex, time-varying datasets produced by a laser wakefield accelerator simulation. Our approach leverages histogram-based parallel coordinates for both visual information display as well as a vehicle for guiding a data mining operation. Data extraction and subsetting are implemented with state-of-the-art index/query technology. This approach, while applied here to accelerator science, is generally applicable to a broad set of science applications, and is implemented in a production-quality visual data analysis infrastructure. We conduct a detailed performance analysis and demonstrate good scalability on a distributed memory Cray XT4 system.

REFERENCES

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