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Title: Portable Data-Parallel Surface Reconstruction on a Uniform Rectilinear Grid

With the increasing heterogeneity and on-node parallelism of high-performance computing hardware, a major challenge is to develop portable and efficient algorithms and software. In this work, we present our implementation of a portable code to perform surface reconstruction using NVIDIA's Thrust library. Surface reconstruction is a technique commonly used in volume tracking methods for simulations of multimaterial flow with interfaces. Here, we have designed a 3D mesh data structure that is easily mapped to the 1D vectors used by Thrust and at the same time is simple to use and uses familiar data structure terminology (such as cells, faces, vertices, and edges). With this new data structure in place, we have implemented a piecewise linear interface reconstruction algorithm in 3 dimensions that effectively exploits the symmetry present in a uniform rectilinear computational cell. Finally, we report performance results, which show that a single implementation of these algorithms can be compiled to multiple backends (specifically, multi-core CPUs, NVIDIA GPUs, and Intel Xeon Phi processors), making efficient use of the available parallelism on each. We also compare performance of our implementation to a legacy FORTRAN implementation in Message Passing Interface (MPI) and show performance parity on single and multi-core CPU and achievedmore » good parallel speed-ups on GPU. In conclusion, our research demonstrates the advantage of performance portability of the underlying data-parallel programming model.« less
Authors:
ORCiD logo [1] ; ORCiD logo [1] ;  [1] ;  [1]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Publication Date:
Report Number(s):
LA-UR-16-21452
Journal ID: ISSN 0271-2091
Grant/Contract Number:
AC52-06NA25396
Type:
Accepted Manuscript
Journal Name:
International Journal for Numerical Methods in Fluids
Additional Journal Information:
Journal Volume: 86; Journal Issue: 2; Journal ID: ISSN 0271-2091
Publisher:
Wiley
Research Org:
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
Sponsoring Org:
USDOE National Nuclear Security Administration (NNSA)
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Volume-of-Fluid method; interface reconstruction; data parallel models for multi-core; mesh data structures
OSTI Identifier:
1458933

Francois, Marianne M., Lo, Li-Ta, Sewell, Christopher Meyer, and Velechovsky, Jan. Portable Data-Parallel Surface Reconstruction on a Uniform Rectilinear Grid. United States: N. p., Web. doi:10.1002/fld.4410.
Francois, Marianne M., Lo, Li-Ta, Sewell, Christopher Meyer, & Velechovsky, Jan. Portable Data-Parallel Surface Reconstruction on a Uniform Rectilinear Grid. United States. doi:10.1002/fld.4410.
Francois, Marianne M., Lo, Li-Ta, Sewell, Christopher Meyer, and Velechovsky, Jan. 2018. "Portable Data-Parallel Surface Reconstruction on a Uniform Rectilinear Grid". United States. doi:10.1002/fld.4410.
@article{osti_1458933,
title = {Portable Data-Parallel Surface Reconstruction on a Uniform Rectilinear Grid},
author = {Francois, Marianne M. and Lo, Li-Ta and Sewell, Christopher Meyer and Velechovsky, Jan},
abstractNote = {With the increasing heterogeneity and on-node parallelism of high-performance computing hardware, a major challenge is to develop portable and efficient algorithms and software. In this work, we present our implementation of a portable code to perform surface reconstruction using NVIDIA's Thrust library. Surface reconstruction is a technique commonly used in volume tracking methods for simulations of multimaterial flow with interfaces. Here, we have designed a 3D mesh data structure that is easily mapped to the 1D vectors used by Thrust and at the same time is simple to use and uses familiar data structure terminology (such as cells, faces, vertices, and edges). With this new data structure in place, we have implemented a piecewise linear interface reconstruction algorithm in 3 dimensions that effectively exploits the symmetry present in a uniform rectilinear computational cell. Finally, we report performance results, which show that a single implementation of these algorithms can be compiled to multiple backends (specifically, multi-core CPUs, NVIDIA GPUs, and Intel Xeon Phi processors), making efficient use of the available parallelism on each. We also compare performance of our implementation to a legacy FORTRAN implementation in Message Passing Interface (MPI) and show performance parity on single and multi-core CPU and achieved good parallel speed-ups on GPU. In conclusion, our research demonstrates the advantage of performance portability of the underlying data-parallel programming model.},
doi = {10.1002/fld.4410},
journal = {International Journal for Numerical Methods in Fluids},
number = 2,
volume = 86,
place = {United States},
year = {2018},
month = {1}
}