A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing
Abstract
Radiative heat transfer is an important mechanism in a class of challenging engineering and research problems. A direct all-to-all treatment of these problems is prohibitively expensive on large core counts due to pervasive all- to-all MPI communication. The massive heat transfer problem arising from the next generation of clean coal boilers being modeled by the Uintah framework has radiation as a dominant heat transfer mode. Reverse Monte Carlo ray tracing (RMCRT) can be used to solve for the radiative-flux divergence while accounting for the effects of participating media. The ray tracing approach used here replicates the geometry of the boiler on a multi-core node and then uses an all-to-all communication phase to distribute the results globally. The cost of this all-to-all is reduced by using an adaptive mesh approach in which a fine mesh is only used locally, and a coarse mesh is used elsewhere. Here, a model for communication and computation complexity is used to predict performance of this new method. We show this model is consistent with observed results and demonstrate excellent strong scaling to 262K cores on the DOE Titan system on problem sizes that were previously computationally intractable.
- Authors:
-
- Univ. of Utah, Salt Lake City, UT (United States)
- Publication Date:
- Research Org.:
- Univ. of Utah, Salt Lake City, UT (United States)
- Sponsoring Org.:
- USDOE National Nuclear Security Administration (NNSA)
- OSTI Identifier:
- 1756107
- Grant/Contract Number:
- NA0002375; AC05-00OR22725
- Resource Type:
- Journal Article: Accepted Manuscript
- Journal Name:
- Lecture Notes in Computer Science
- Additional Journal Information:
- Journal Volume: 9137; Conference: ISC'15: High Performance Computing; Journal ID: ISSN 0302-9743
- Publisher:
- Springer
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 97 MATHEMATICS AND COMPUTING; Uintah; Radiation modeling; Parallel; Scalability; Adaptive mesh refinement; Simulation science; Titan
Citation Formats
Humphrey, Alan, Harman, Todd, Berzins, Martin, and Smith, Phillip. A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing. United States: N. p., 2015.
Web. doi:10.1007/978-3-319-20119-1_16.
Humphrey, Alan, Harman, Todd, Berzins, Martin, & Smith, Phillip. A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing. United States. https://doi.org/10.1007/978-3-319-20119-1_16
Humphrey, Alan, Harman, Todd, Berzins, Martin, and Smith, Phillip. Sat .
"A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing". United States. https://doi.org/10.1007/978-3-319-20119-1_16. https://www.osti.gov/servlets/purl/1756107.
@article{osti_1756107,
title = {A Scalable Algorithm for Radiative Heat Transfer Using Reverse Monte Carlo Ray Tracing},
author = {Humphrey, Alan and Harman, Todd and Berzins, Martin and Smith, Phillip},
abstractNote = {Radiative heat transfer is an important mechanism in a class of challenging engineering and research problems. A direct all-to-all treatment of these problems is prohibitively expensive on large core counts due to pervasive all- to-all MPI communication. The massive heat transfer problem arising from the next generation of clean coal boilers being modeled by the Uintah framework has radiation as a dominant heat transfer mode. Reverse Monte Carlo ray tracing (RMCRT) can be used to solve for the radiative-flux divergence while accounting for the effects of participating media. The ray tracing approach used here replicates the geometry of the boiler on a multi-core node and then uses an all-to-all communication phase to distribute the results globally. The cost of this all-to-all is reduced by using an adaptive mesh approach in which a fine mesh is only used locally, and a coarse mesh is used elsewhere. Here, a model for communication and computation complexity is used to predict performance of this new method. We show this model is consistent with observed results and demonstrate excellent strong scaling to 262K cores on the DOE Titan system on problem sizes that were previously computationally intractable.},
doi = {10.1007/978-3-319-20119-1_16},
url = {https://www.osti.gov/biblio/1756107},
journal = {Lecture Notes in Computer Science},
issn = {0302-9743},
number = ,
volume = 9137,
place = {United States},
year = {2015},
month = {6}
}
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