sponsored byIEEEACMThe International Conference for High Performance 
Computing, Networking, Storage and Analysis
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SCHEDULE: NOV 16-21, 2014

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Scalable Computation of Stream Surfaces on Large Scale Vector Fields

SESSION: Large-Scale Visualization


TIME: 4:00PM - 4:30PM


AUTHOR(S):Kewei Lu, Han-Wei Shen, Tom Peterka



Stream surfaces and streamlines are two popular methods for visualizing three-dimensional flow fields. While several parallel streamline computation algorithms exist, relatively little research has been done to parallelize stream surface generation. This is because load-balanced parallel stream surface computation is
nontrivial, due to the strong dependency in computing the positions of the particles forming the stream surface front. In this paper, we present a new algorithm that computes stream surfaces efficiently. In our algorithm, seeding curves are divided into segments, which are then assigned to the processes. Each process is responsible for integrating the segments assigned to it. To ensure a balanced computational workload, work stealing and dynamic refinement of seeding curve segments are employed to improve the overall performance. We demonstrate the effectiveness of our parallel stream surface algorithm using several large scale flow field data sets, and show the performance and scalability on HPC systems.

Chair/Author Details:

Brent Welch (Chair) - Google

Kewei Lu - Ohio State University

Han-Wei Shen - Ohio State University

Tom Peterka - Argonne National Laboratory

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Paper provided by the ACM Digital Library

Paper also available from IEEE Computer Society