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Title: A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment

Abstract

Monte Carlo (MC) method is able to accurately calculate eigenvalues in reactor analysis. Its lengthy computation time can be reduced by general-purpose computing on Graphics Processing Units (GPU), one of the latest parallel computing techniques under development. The method of porting a regular transport code to GPU is usually very straightforward due to the 'embarrassingly parallel' nature of MC code. However, the situation becomes different for eigenvalue calculation in that it will be performed on a generation-by-generation basis and the thread coordination should be explicitly taken care of. This paper presents our effort to develop such a GPU-based MC code in Compute Unified Device Architecture (CUDA) environment. The code is able to perform eigenvalue calculation under simple geometries on a multi-GPU system. The specifics of algorithm design, including thread organization and memory management were described in detail. The original CPU version of the code was tested on an Intel Xeon X5660 2.8 GHz CPU, and the adapted GPU version was tested on NVIDIA Tesla M2090 GPUs. Double-precision floating point format was used throughout the calculation. The result showed that a speedup of 7.0 and 33.3 were obtained for a bare spherical core and a binary slab system respectively. The speedupmore » factor was further increased by a factor of {approx}2 on a dual GPU system. The upper limit of device-level parallelism was analyzed, and a possible method to enhance the thread-level parallelism was proposed. (authors)« less

Authors:
; ; ;  [1];  [2];  [3]
  1. Nuclear Engineering and Engineering Physics, Rensselaer Polytechnic Inst., Troy, NY 12180 (United States)
  2. Dept. of Computer Science, Rensselaer Polytechnic Inst. RPI (United States)
  3. Los Alamos National Laboratory (LANL) (United States)
Publication Date:
Research Org.:
American Nuclear Society, Inc., 555 N. Kensington Avenue, La Grange Park, Illinois 60526 (United States)
OSTI Identifier:
22105653
Resource Type:
Conference
Resource Relation:
Conference: PHYSOR 2012: Conference on Advances in Reactor Physics - Linking Research, Industry, and Education, Knoxville, TN (United States), 15-20 Apr 2012; Other Information: Country of input: France; 23 refs.
Country of Publication:
United States
Language:
English
Subject:
22 GENERAL STUDIES OF NUCLEAR REACTORS; ALGORITHMS; COMPUTER ARCHITECTURE; COMPUTER GRAPHICS; DESIGN; EIGENFUNCTIONS; EIGENVALUES; GHZ RANGE 01-100; MEMORY MANAGEMENT; MONTE CARLO METHOD; NEUTRON FLUX; NEUTRON TRANSPORT; PARALLEL PROCESSING; SPHERICAL CONFIGURATION

Citation Formats

Liu, T., Ding, A., Ji, W., Xu, X. G., Carothers, C. D., and Brown, F. B. A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment. United States: N. p., 2012. Web.
Liu, T., Ding, A., Ji, W., Xu, X. G., Carothers, C. D., & Brown, F. B. A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment. United States.
Liu, T., Ding, A., Ji, W., Xu, X. G., Carothers, C. D., and Brown, F. B. 2012. "A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment". United States.
@article{osti_22105653,
title = {A Monte Carlo neutron transport code for eigenvalue calculations on a dual-GPU system and CUDA environment},
author = {Liu, T. and Ding, A. and Ji, W. and Xu, X. G. and Carothers, C. D. and Brown, F. B.},
abstractNote = {Monte Carlo (MC) method is able to accurately calculate eigenvalues in reactor analysis. Its lengthy computation time can be reduced by general-purpose computing on Graphics Processing Units (GPU), one of the latest parallel computing techniques under development. The method of porting a regular transport code to GPU is usually very straightforward due to the 'embarrassingly parallel' nature of MC code. However, the situation becomes different for eigenvalue calculation in that it will be performed on a generation-by-generation basis and the thread coordination should be explicitly taken care of. This paper presents our effort to develop such a GPU-based MC code in Compute Unified Device Architecture (CUDA) environment. The code is able to perform eigenvalue calculation under simple geometries on a multi-GPU system. The specifics of algorithm design, including thread organization and memory management were described in detail. The original CPU version of the code was tested on an Intel Xeon X5660 2.8 GHz CPU, and the adapted GPU version was tested on NVIDIA Tesla M2090 GPUs. Double-precision floating point format was used throughout the calculation. The result showed that a speedup of 7.0 and 33.3 were obtained for a bare spherical core and a binary slab system respectively. The speedup factor was further increased by a factor of {approx}2 on a dual GPU system. The upper limit of device-level parallelism was analyzed, and a possible method to enhance the thread-level parallelism was proposed. (authors)},
doi = {},
url = {https://www.osti.gov/biblio/22105653}, journal = {},
number = ,
volume = ,
place = {United States},
year = {2012},
month = {7}
}

Conference:
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