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Vectorized and Multitasked Solution of the Few-Group Neutron Diffusion Equations

 

作者: KyunSung,   TurinskyPaul J.,   ShayerZeev,  

 

期刊: Nuclear Science and Engineering  (Taylor Available online 1989)
卷期: Volume 101, issue 3  

页码: 205-216

 

ISSN:0029-5639

 

年代: 1989

 

DOI:10.13182/NSE89-A23609

 

出版商: Taylor&Francis

 

数据来源: Taylor

 

摘要:

AbstractA numerical algorithm with parallelism was used to solve the two-group, multidimensional neutron diffusion equations on computers characterized by shared memory, vector pipeline, and multi-CPU architecture features. Specifically, solutions were obtained on the Cray X/MP-48, the IBM-3090 with vector facilities, and the FPS-164. The material-centered mesh finite difference method approximation and outer-inner iteration method were employed. Parallelism was introduced in the inner iterations using the cyclic line successive overrelaxation iterative method and solving in parallel across lines. The outer iterations were completed using the Chebyshev semi-iterative method that allows parallelism to be introduced in both space and energy groups. For the three-dimensional model, power, soluble boron, and transient fission product feedbacks were included. Concentrating on the pressurized water reactor (PWR), the thermal-hydraulic calculation of moderator density assumed single-phase flow and a closed flow channel, allowing parallelism to be introduced in the solution across the radial plane.Using a pinwise detail, quarter-core model of a typical PWR in cycle 1, for the two-dimensional model without feedback the measured million floating point operations per second (MFLOPS)/vector speedups were 83/11.7, 18/2.2, and 2.4/5.6 on the Cray, IBM, and FPS without multitasking, respectively. Lower performance was observed with a coarser mesh, i.e., shorter vector length, due to vector pipeline start-up. For an 18×18×30 (x-y-z) three-dimensional model with feedback of the same core, MFLOPS/vector speedups of∼61/6.7 and an execution time of 0.8 CPU seconds on the Cray without multitasking were measured. Finally, using two CPUs and the vector pipelines of the Cray, a multitasking efficiency of 81% was noted for the three-dimensional model.

 

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