GPU Acceleration of 3D Agent-Based Biological Simulations

Ahmad Hesam, Lukas Breitwieser, Fons Rademakers, Zaid Al-Ars

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

Abstract

Researchers in biology are faced with the tough challenge of developing high-performance computer simulations of their increasingly complex agent-based models. BioDynaMo is an open-source agent-based simulation platform that aims to alleviate researchers from the intricacies that go into the development of high-performance computing. Through a high-level interface, researchers can implement their models on top of BioDynaMo's multi-threaded core execution engine to rapidly develop simulations that effectively utilize parallel computing hardware. In biological agent-based modeling, the type of operations that are typically the most compute-intensive are those that involve agents interacting with their local neighborhood. In this work, we investigate the currently implemented method of handling neighborhood interactions of cellular agents in BioDynaMo, and ways to improve the performance to enable large-scale and complex simulations. We propose to replace the kd-tree implementation to find and iterate over the neighborhood of each agent with a uniform grid method that allows us to take advantage of the massively parallel architecture of graphics processing units (GPUs). We implement the uniform grid method in both CUDA and OpenCL to address GPUs from all major vendors and evaluate several techniques to further improve the performance. Furthermore, we analyze the performance of our implementations for models with a varying density of neighboring agents. As a result, the performance of the mechanical interactions method improved by up to two orders of magnitude in comparison to the multithreaded baseline version. The implementations are open-source and publicly available on Github.
Original languageEnglish
Title of host publication2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021
EditorsL. O'Conner
Place of PublicationPiscataway
PublisherIEEE
Pages210-217
Number of pages8
ISBN (Electronic)978-1-6654-3577-2
ISBN (Print)978-1-6654-1192-9
DOIs
Publication statusPublished - 2021
Event2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW) - Virtually at Portland, United States
Duration: 17 Jun 202121 Jun 2021

Publication series

Name2021 IEEE International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2021 - In conjunction with IEEE IPDPS 2021

Workshop

Workshop2021 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW)
Country/TerritoryUnited States
CityVirtually at Portland
Period17/06/2121/06/21

Keywords

  • GPU
  • acceleration
  • agent-based modeling
  • biological models
  • coprocessing
  • simulation

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