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New Generation Computing

, Volume 35, Issue 2, pp 157–180 | Cite as

Parallel Interaction Detection Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System Accelerated by GPGPU

  • Gregory Gutmann
  • Daisuke Inoue
  • Akira Kakugo
  • Akihiko Konagaya
Research Paper
  • 211 Downloads

Abstract

Real-time simulations have been getting more attention in the field of self-organizing molecular pattern formation such as a microtubule gliding assay. When appropriate microtubule interactions are set up on gliding assay experiments, microtubules often organize and create higher-level dynamics such as ring and bundle structures. In order to reproduce such higher-level dynamics in silico, we have been focusing on making a real-time 3D microtubule simulation. This real-time 3D microtubule simulation enables us to gain more knowledge on microtubule dynamics and their swarm movements by means of adjusting simulation parameters in a real-time fashion. For the recreation of microtubule dynamics our model proposes the use of the Lennard-Jones potential for our particle-based simulation, as well as a flocking algorithm for self-organization. One of the technical challenges when creating a real-time 3D simulation is computational scalability performance, as well as balancing the 3D rendering and computing work flows. GPU programming plays an essential role in executing the millions of tasks necessary for microtubule interaction detection and makes this real-time 3D simulation possible. However, an excess number of tasks sometimes causes a memory bottleneck which prevents performance scalability when using GPGPU processing. In order to alleviate the memory bottleneck, we propose a new parallel interaction detection algorithm that uses warp level optimizations for the two memory bound interactions discussed in this paper.

Keywords

CUDA DirectX GPGPU Microtubule gliding assay Real-time 3D simulation Live control Lennard-Jones potential Threading 

Notes

Acknowledgements

This work was supported by a Grant-in-Aid for Scientific Research on Innovation Areas Molecular Robotics (No. 24104004) of The Ministry of Education, Culture, Sports, Science, and Technology, Japan.

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Copyright information

© Ohmsha, Ltd. and Springer Japan 2017

Authors and Affiliations

  1. 1.Department of Computational Intelligence and Systems ScienceTokyo Institute of TechnologyYokohamaJapan
  2. 2.Faculty of ScienceHokkaido UniversitySapporoJapan
  3. 3.Graduate School of Chemical Sciences and EngineeringHokkaido UniversitySapporoJapan

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