ISSN:
0271-2091
Keywords:
Computational fluid dynamics
;
Parallel computing
;
Parallel processing
;
Functional decomposition
;
SIMPLE algorithm
;
Pressure correction schemes
;
Engineering
;
Engineering General
Source:
Wiley InterScience Backfile Collection 1832-2000
Topics:
Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
Notes:
The primary aim of this work was to determine the simplest and most effective parallelization strategy for control-volume-based codes solving industrial problems. It has been found that for certain classes of problems, the coarse-grain functional decomposition strategy, largely ignored due to its limited scaling capability, offers the potential for significant execution speed-ups while maintaining the inherent structure of traditional serial algorithms. Functional decomposition requires only minor modification of the existing serial code to implement and, hence, code portability across both concurrent and serial computers is maintained. Fine-grain parallelization strategies at the ‘DO loop’ level are also easy to implement and largely preserve code portability. Both coarse-grain functional decomposition and fine-grain loop-level parallelization strategies for the SIMPLE pressure correction algorithm are demonstrated on a Silicon Graphics 4D280S eight CPU shared memory computer system for a highly coupled, transient two-dimensional simulation involving melting of a metal in the presence of thermal-buoyancy-driven laminar convection. Problems requiring the solution of a larger number of transport equations were simulated by including further scalar variables in the calculation. While resulting in slight degradation of the convergence rate, the functional decomposition strategy exhibited higher parallel efficiencies and yielded greater speed-ups relative to the original serial code. Initially, this strategy showed a significant degradation in convergence rate due to an inconsistency in the parallel solution of the pressure correction equation. After correcting for this inconsistency, the maximum speed-up for 16 dependent variables was a factor of 5·28 with eight processors, representing a parallel efficiency of 67%. Peak efficiency of 76% was achieved using five processors to solve for 10 dependent variables.
Additional Material:
16 Ill.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1002/fld.1650161004
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