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Title: Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis

Abstract

We present an efficient and scalable solution to estimate uncertain transport behaviors-stochastic flow maps (SFMs)-for visualizing and analyzing uncertain unsteady flows. Computing flow maps from uncertain flow fields is extremely expensive because it requires many Monte Carlo runs to trace densely seeded particles in the flow. We reduce the computational cost by decoupling the time dependencies in SFMs so that we can process shorter sub time intervals independently and then compose them together for longer time periods. Adaptive refinement is also used to reduce the number of runs for each location. We parallelize over tasks-packets of particles in our design-to achieve high efficiency in MPI/thread hybrid programming. Such a task model also enables CPU/GPU coprocessing. We show the scalability on two supercomputers, Mira (up to 256K Blue Gene/Q cores) and Titan (up to 128K Opteron cores and 8K GPUs), that can trace billions of particles in seconds.

Authors:
; ; ; ; ; ;
Publication Date:
Research Org.:
Argonne National Lab. (ANL), Argonne, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1366303
DOE Contract Number:  
AC02-06CH11357
Resource Type:
Conference
Resource Relation:
Conference: 2016 International Conference for High Performance Computing, Networking, Storage and Analysis , 11/13/16 - 11/18/16, Salt Lake City, UT, US
Country of Publication:
United States
Language:
English
Subject:
CPU-GPU hybrid parallelism; Parallel particle tracing; Uncertain flow visualization

Citation Formats

Guo, Hanqi, He, Wenbin, Seo, Sangmin, Shen, Han-Wei, Constantinescu, Emil Mihai, liu, Chunhui, and Peterka, Tom. Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis. United States: N. p., 2019. Web. doi:10.1109/TVCG.2018.2856772.
Guo, Hanqi, He, Wenbin, Seo, Sangmin, Shen, Han-Wei, Constantinescu, Emil Mihai, liu, Chunhui, & Peterka, Tom. Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis. United States. doi:10.1109/TVCG.2018.2856772.
Guo, Hanqi, He, Wenbin, Seo, Sangmin, Shen, Han-Wei, Constantinescu, Emil Mihai, liu, Chunhui, and Peterka, Tom. Sun . "Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis". United States. doi:10.1109/TVCG.2018.2856772. https://www.osti.gov/servlets/purl/1366303.
@article{osti_1366303,
title = {Extreme-Scale Stochastic Particle Tracing for Uncertain Unsteady Flow Visualization and Analysis},
author = {Guo, Hanqi and He, Wenbin and Seo, Sangmin and Shen, Han-Wei and Constantinescu, Emil Mihai and liu, Chunhui and Peterka, Tom},
abstractNote = {We present an efficient and scalable solution to estimate uncertain transport behaviors-stochastic flow maps (SFMs)-for visualizing and analyzing uncertain unsteady flows. Computing flow maps from uncertain flow fields is extremely expensive because it requires many Monte Carlo runs to trace densely seeded particles in the flow. We reduce the computational cost by decoupling the time dependencies in SFMs so that we can process shorter sub time intervals independently and then compose them together for longer time periods. Adaptive refinement is also used to reduce the number of runs for each location. We parallelize over tasks-packets of particles in our design-to achieve high efficiency in MPI/thread hybrid programming. Such a task model also enables CPU/GPU coprocessing. We show the scalability on two supercomputers, Mira (up to 256K Blue Gene/Q cores) and Titan (up to 128K Opteron cores and 8K GPUs), that can trace billions of particles in seconds.},
doi = {10.1109/TVCG.2018.2856772},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

Conference:
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