Accelerating multi-dimensional population balance model simulations via a highly scalable framework using GPUs

Chaitanya Sampat, Yukteshwar Baranwal, Rohit Ramachandran

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Article number106935
JournalComputers and Chemical Engineering
Volume140
DOIs
StatePublished - Sep 2 2020

All Science Journal Classification (ASJC) codes

  • Chemical Engineering(all)
  • Computer Science Applications

Keywords

  • CUDA
  • GPU
  • Granulation
  • MPI
  • Parallel computing
  • Population balance model

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