학술논문

Parallel $SP_N$ on multi-core CPUs and many-core GPUs.
Document Type
Journal
Author
Kirschenmann, W. (F-EDF-RD) AMS Author Profile; Plagne, L. (F-EDF-RD) AMS Author Profile; Ponçot, A. (F-EDF-RD) AMS Author Profile; Vialle, S. AMS Author Profile
Source
Transport Theory and Statistical Physics (Transport Theory Statist. Phys.) (20100101), 39, no.~2-4, 255-281. ISSN: 0041-1450 (print).eISSN: 1532-2424.
Subject
82 Statistical mechanics, structure of matter -- 82C Time-dependent statistical mechanics
  82C70 Transport processes
Language
English
Abstract
Summary: ``This paper presents two parallel Simplified $P_N$ $(SP_N)$ solver implementations for both multi-core Central Processing Units (CPU) and Graphics Processing Units (GPU). For a nuclear operator such as Électricité de France (EDF), the time required to carry out nuclear reactor core simulations is rather critical when dealing with production constraints. The $SP_N$ method provides a convenient tradeoff between accuracy and numerical complexity and is used in several industrial simulations. The parallelization of the $SP_N$ algorithm reduces its computation time. To solve the problem on distributed memory machines such as PC clusters, Domain Decomposition Methods have been investigated. Complementary to this approach, this work aims to use emerging massively parallel processors such as the GPUs as well as current multi-core CPUs. Based on a fine grained parallelism, this solution achieves good performances on desktop machines. Our multi-core CPU and many-core GPU implementations allow us to solve 3D $SP_N$ problems, respectively, 10 and 36 times faster than our sequential CPU reference.''