Distributed Memory Algorithms for Weight Cancellation in Monte Carlo Particle Transport Simulations

Recent literature has demonstrated use cases for Monte Carlo transport simulations where particles can have statistical weights that are positive or negative. There are even examples which require particles to have complex statistical weights, and the real and imaginary components can be positive or...

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Bibliographic Details
Published in:EPJ Web of conferences Vol. 302; p. 9007
Main Authors: Grablevsky, Nicholas, Belanger, Hunter
Format: Journal Article Conference Proceeding
Language:English
Published: Les Ulis EDP Sciences 01.01.2024
Subjects:
Algorithms
Cancellation
Computer simulation
Distributed memory
Fission
Simulation
ISSN:2100-014X, 2101-6275, 2100-014X
Online Access:Get full text
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Summary:Recent literature has demonstrated use cases for Monte Carlo transport simulations where particles can have statistical weights that are positive or negative. There are even examples which require particles to have complex statistical weights, and the real and imaginary components can be positive or negative. In such cases, weight cancellation algorithms can be very efficient at reducing the variance, or might even be required for a simulation to converge. Previous works that have employed weight cancellation in distributed memory simulations required that all fission particles be sent to a single node for the cancellation operation. This work examines possible implementations of distributed memory weight cancellation algorithms that do not require the transfer of the fission source to a single node.
Bibliography:ObjectType-Conference Proceeding-1
SourceType-Conference Papers & Proceedings-1
content type line 21
ISSN:2100-014X
2101-6275
2100-014X
DOI:10.1051/epjconf/202430209007