A fast Monte Carlo GPU based algorithm for particle breakage

An algorithm for simulating the particle population balance in case of breakage is designed to function on a Graphic Processing Unit (GPU) in a Compute Unified Device Architecture (CUDA). The GPU lowers the computational cost of the particle breakage simulation, which is generally complex and demand...

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Bibliographic Details
Published in2017 4th International Conference on Control, Decision and Information Technologies (CoDIT) pp. 0784 - 0789
Main Authors Devi, Jherna, Kruis, F. Einar
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.04.2017
Subjects
Algorithm design and analysis
Computational modeling
CUDA
GPU
Graphics processing units
Mathematical model
Parallel Processing
Particle Breakage
Population Balances
Sociology
Statistics
Tuning
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Summary:An algorithm for simulating the particle population balance in case of breakage is designed to function on a Graphic Processing Unit (GPU) in a Compute Unified Device Architecture (CUDA). The GPU lowers the computational cost of the particle breakage simulation, which is generally complex and demanding. We simulate particle breakage by a Population Balance-Monte Carlo (PB-MC) simulation method. Data analysis has confirmed that use of the GPU accelerates the execution of the MC program. The computational time of the algorithm linearly increases with the number of simulation entries (SEs). Here, an all-inclusive framework that accelerates the PB-MC simulation of particle breakage dynamics is introduced. The computational efficiency of the simulation method was significantly improved by the parallel computing approach enabled by the GPU. A fast acceptance-rejection (AR) and inverse scheme based algorithm that speeds up the performance of the PB-MC method has been implemented and validated. The complexity of the proposed algorithm is O(N).
DOI:10.1109/CoDIT.2017.8102690