GPU-accelerated Monte Carlo simulation of particle coagulation based on the inverse method
Simulating particle coagulation using Monte Carlo methods is in general a challenging computational task due to its numerical complexity and the computing cost. Currently, the lowest computing costs are obtained when applying a graphic processing unit (GPU) originally developed for speeding up graphic processing in the consumer market. In this article we present an implementation of accelerating a Monte Carlo method based on the Inverse scheme for simulating particle coagulation on the GPU. The abundant data parallelism embedded within the Monte Carlo method is explained as it will allow an efficient parallelization of the MC code on the GPU. Furthermore, the computation accuracy of the MC on GPU was validated with a benchmark, a CPU-based discrete-sectional method. To evaluate the performance gains by using the GPU, the computing time on the GPU against its sequential counterpart on the CPU were compared. The measured speedups show that the GPU can accelerate the execution of the MC code by a factor 10100, depending on the chosen particle number of simulation particles. The algorithm shows a linear dependence of computing time with the number of simulation particles, which is a remarkable result in view of the n2 dependence of the coagulation.
gpu monte carlo particle coagulation inverse method cuda