GPU parallelization of particulate matter concentration modeling in indoor environment with cellular automata framework

Abstract

Nowadays, the CFD approaches for modeling turbulent airflow and particulate matter (PM) concentration distribution are matured despite they all suffer from heavy computational demands. Particularly, in indoor PM concentration modeling, a novel cellular automata (CA) approach in “Modeling particulate matter concentration in indoor environment with cellular automata framework” is developed to achieve almost the same accuracy with improved efficiency as the Eulerian approach. To further enhance its efficiency, this study proposes two parallelization procedures. With the mechanism parallelization, the four PM transport mechanisms (flow advection, turbulent diffusion, gravitational settling, and boundary deposition) are simulated simultaneously instead of sequentially. Besides, using the GPU-based cell parallelization by adopting OpenCL 2.1 under Nvidia CUDA, the execution of all the PM transport mechanisms is performed parallelly on GPUs instead of sequentially on the CPU. Three parallelized CA scenarios, i.e., the parallelized CA approach with only the mechanism parallelization, with only the GPU-based cell parallelization, and with both the two parallelization procedures, are evaluated through two indoor PM concentration experiments. The three parallelized CA scenarios are found to maintain the accuracy but enhance the efficiency by 174%–210%, 1780%–5730%, and 2427%–7695%, respectively. Thus, the GPU-based cell parallelization obtains more efficiency enhancement than the mechanism parallelization. Furthermore, despite the simulations are performed on an i9 PC with an Intel UHD Graphics 630 graphic card, the parallelized CA approach with both the two parallelization procedures can enhance its efficiency up to 24–77 times, proving its considerable potentials as a useful tool for real-time 3D indoor PM distribution modeling.