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Publication

Parallel Particle Advection Bake-Off for Scientific Visualization Workloads...

by David R Pugmire, Hank Childs
Publication Type
Conference Paper
Journal Name
IEEE CLUSTER
Book Title
2020 IEEE International Conference on Cluster Computing (CLUSTER)
Publication Date
Page Numbers
1 to 11
Issue
1
Publisher Location
New York, United States of America
Conference Name
IEEE Cluster
Conference Location
Kobe, Japan
Conference Sponsor
IEEE
Conference Date
-

There are multiple algorithms for parallelizing particle advection for scientific visualization workloads. While many previous studies have contributed to the understanding of individual algorithms, our study aims to provide a holistic understanding of how algorithms perform relative to each other on various workloads. To accomplish this, we consider four popular parallelization algorithms and run a “bake-off” study (i.e., an empirical study) to identify the best matches for each. The study includes 216 tests, going to a concurrency of up to 8192 cores and considering data sets as large as 34 billion cells with 300 million particles. Overall, our study informs three important research questions: (1) which parallelization algorithms perform best for a given workload?, (2) why?, and (3) what are the unsolved problems in parallel particle advection? In terms of findings, we find that the seeding box is the most important factor in choosing the best algorithm, and also that there is a significant opportunity for improvement in execution time, scalability, and efficiency.