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Vectorization Methods Development for a New Version of the KENO-V.a Criticality Safety Code

D. F. Hollenbach, L. M. Petri, H. L. Dodds

Nuclear Science and Engineering / Volume 116 / Number 3 / Pages 147-164

March 1994

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The object of this research project is to develop a vectorized version of the KENO-V.a criticality safety code, benchmark it against the original version of the code, and determine its speedup factor for various classes of problems. The current generation of supercomputers is equipped with vector processors that allow the same operation to be simultaneously performed on a string of data. Unfortunately, the Monte Carlo algorithm used in KENO-V.a, which tracks particles individually, cannot utilize these vector processors. A new Monte Carlo algorithm that would efficiently utilize the vector processors currently used in computers is needed. The algorithm developed for the vectorized version of KENO-V.a is an event-based, stack-driven, all-zone, implicit-stack Monte Carlo algorithm. This algorithm divides the particles into one of four main stacks: free flight, inward crossing, outward crossing, or collision. A fifth stack, kill, contains all particles that have either leaked from the system or have been terminated by Russian roulette. The main stack, containing the largest number of particles, is the next stack processed. All the particles in the longest stack are processed simultaneously. The generation is complete when the four main stacks are empty. Only the particle number is transferred between stacks; the particle data remain in permanent vector locations and are updated as the particles traverse through the system. This approach minimizes data transfer between stacks and optimizes the vector length, thus maximizing the speedup. For the 25 benchmark problems, speedup factors ranging from 1.8 to 5.7 relative to the optimized scalar version of KENO-V.a were obtained. Problem geometry, material composition, and the number of histories per generation—all have significant effects on the speedup factor of a problem.

 
 
 
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