Accelerating multi-dimensional population balance model simulations via a highly scalable framework using GPUs. (2nd September 2020)

• Record Type: Journal Article
• Authors: Sampat, Chaitanya; Baranwal, Yukteshwar; Ramachandran, Rohit
• Is Part Of: Computers & chemical engineering. Volume 140(2020)
• Publication Date: 2020-09-02
• Subjects: Population balance model -- GPU -- Parallel computing -- Granulation -- MPI -- CUDA; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2020.106935
• Languages: English
• ISSNs: 0098-1354

Abstract

Abstract: The solution of high-dimensional PBMs using CPUs are often computationally intractable. This study focuses on the development of a scalable algorithm to parallelize the nested loops inside the PBM via a GPU framework. The developed PBM is unique since it adapts to the size of the problem and uses the GPU cores accordingly. This algorithm was parallelized for NVIDIA® GPUs as it was written in CUDA® and C/C++. The major bottleneck of such algorithms is the communication time between the CPU and the GPU. In our studies, communication time contributed to less than 1% of the total run time and a maximum speedup of about 12 over the serial CPU code was achieved. The GPU PBM achieved a speedup of about two times compared to the PBM's multi-core configuration on a desktop computer. The speed improvements are also reported for various CPU and GPU architectures and configurations.