Efficient GPU parallel computing of population balance—Monte Carlo method for coagulation

Abstract

The population balance-Monte Carlo (PBMC) method has become increasingly popular because the discrete and stochastic nature of the MCmethod is especially suited for particle dynamics. However, for the two-particle events (typically, particle coagulation), the double looping over all simulation particles is required in normal PBMCmethods, and simulating particle coagulation is in general a challenging computational task due to its numerical complexity and the computing cost. The compute unified device architecture (CUDA) is a programming approach for performing scientific calculations on a graphics processing unit (GPU) as a data-parallel computing device. In this article we present an implementation of accelerating PBMCmethod based on the Inverse scheme and the Acceptance-rejection (AR) scheme for simulating particle coagulation on the GPU. The main idea is to implement the highly threaded data-parallel processing tasks by using GPU and serial computing of complex logic and transaction processing by CPU. Furthermore, the computation accuracy of the PBMC on GPU was validated with a benchmark, a CPU-based discrete-sectional method. To evaluate the accelerating performance, the computing time on the GPU against its sequential counterpart on the CPU was compared. The speedups show that the GPU can accelerate the PBMC by a factor from decades to more than one hundred, depending on the number of simulation particle.