Parallel stream surface computation for large data sets

David Camp; Hank Childs; Christoph Garth; David Pugmire; Kenneth I. Joy

doi:10.1109/LDAV.2012.6378974

Parallel stream surface computation for large data sets

David Camp, Hank Childs, Christoph Garth, David Pugmire, Kenneth I. Joy

Visualization

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

Parallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.

Original language	English
Title of host publication	IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings
Pages	39-47
Number of pages	9
DOIs	https://doi.org/10.1109/LDAV.2012.6378974
State	Published - 2012
Event	2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 - Seattle, WA, United States Duration: Oct 14 2012 → Oct 19 2012

Publication series

Name	IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings

Conference

Conference	2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012
Country/Territory	United States
City	Seattle, WA
Period	10/14/12 → 10/19/12

Keywords

Computer Graphics [I.3.3]: Picture/Image Generation - Display algorithms
D.1.3 [Programming Techniques]: Concurrent Programming - Parallel programming

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10.1109/LDAV.2012.6378974

Cite this

Camp, D., Childs, H., Garth, C., Pugmire, D., & Joy, K. I. (2012). Parallel stream surface computation for large data sets. In IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings (pp. 39-47). Article 6378974 (IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings). https://doi.org/10.1109/LDAV.2012.6378974

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title = "Parallel stream surface computation for large data sets",

abstract = "Parallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.",

keywords = "Computer Graphics [I.3.3]: Picture/Image Generation - Display algorithms, D.1.3 [Programming Techniques]: Concurrent Programming - Parallel programming",

author = "David Camp and Hank Childs and Christoph Garth and David Pugmire and Joy, {Kenneth I.}",

year = "2012",

doi = "10.1109/LDAV.2012.6378974",

language = "English",

isbn = "9781467347334",

series = "IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings",

pages = "39--47",

booktitle = "IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings",

note = "2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012 ; Conference date: 14-10-2012 Through 19-10-2012",

}

Camp, D, Childs, H, Garth, C, Pugmire, D & Joy, KI 2012, Parallel stream surface computation for large data sets. in IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings., 6378974, IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings, pp. 39-47, 2nd Symposium on Large-Scale Data Analysis and Visualization, LDAV 2012, Seattle, WA, United States, 10/14/12. https://doi.org/10.1109/LDAV.2012.6378974

Parallel stream surface computation for large data sets. / Camp, David; Childs, Hank; Garth, Christoph et al.
IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings. 2012. p. 39-47 6378974 (IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Parallel stream surface computation for large data sets

AU - Camp, David

AU - Childs, Hank

AU - Garth, Christoph

AU - Pugmire, David

AU - Joy, Kenneth I.

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N2 - Parallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.

AB - Parallel stream surface calculation, while highly related to other particle advection-based techniques such as streamlines, has its own unique characteristics that merit independent study. Specifically, stream surfaces require new integral curves to be added continuously during execution to ensure surface quality and accuracy; performance can be improved by specifically accounting for these additional particles. We present an algorithm for generating stream surfaces in a distributed-memory parallel setting. The algorithm incorporates multiple schemes for parallelizing particle advection and we study which schemes work best. Further, we explore speculative calculation and how it can improve overall performance. In total, this study informs the efficient calculation of stream surfaces in parallel for large data sets, based on existing integral curve functionality.

KW - Computer Graphics [I.3.3]: Picture/Image Generation - Display algorithms

KW - D.1.3 [Programming Techniques]: Concurrent Programming - Parallel programming

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M3 - Conference contribution

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T3 - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings

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BT - IEEE Symposium on Large Data Analysis and Visualization 2012, LDAV 2012 - Proceedings

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Y2 - 14 October 2012 through 19 October 2012

ER -

Parallel stream surface computation for large data sets

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