Parallel Interaction Detection Algorithms for a Particle-based Live Controlled Real-time Microtubule Gliding Simulation System Accelerated by GPGPU
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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.
KeywordsCUDA DirectX GPGPU Microtubule gliding assay Real-time 3D simulation Live control Lennard-Jones potential Threading
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.
- 2.Green, S.: Particle simulation using cuda—nvidia documentation (2012). http://docs.nvidia.com/cuda/samples/5_simulations/particles/doc/particles.pdf
- 3.Gutmann, G., Inoue, D., Kakugo, A., Konagaya, A.: Real-time 3d microtubule gliding simulation. Commun. Comput. Inf. Sci. Life Syst. Model. Simul. 13–22 (2014). doi: 10.1007/978-3-662-45283-7
- 6.Hess, H., Clemmens, J., Brunner, C., Doot, R., Luna, S., Ernst, K.H., Vogel, V.: Molecular self-assembly of nanowires and nanospools using active transport. Nano Lett. 5(4), 629–633 (2005). doi: 10.1021/nl0478427
- 7.Horio, T., Murata, T.: The role of dynamic instability in microtubule organization. Front. Plant Sci. 5 (2014). doi: 10.3389/fpls.2014.00511
- 10.Kabir, A.M.R., Wada, S., Inoue, D., Tamura, Y., Kajihara, T., Mayama, H., Sada, K., Kakugo, A., Gong, J.P.: Formation of ring-shaped assembly of microtubules with a narrow size distribution at an airbuffer interface. Soft Matter 8(42):10,863 (2012). doi: 10.1039/c2sm26441b
- 11.Kong, K.Y., Marcus, A.I., Giannakakou, P., Alberti, C., Wang, M.D.: A two dimensional simulation of microtubule dynamics. In: 2008 International conference on technology and applications in biomedicine (2008). doi: 10.1109/itab.2008.4570630
- 12.Kraikivski, P., Lipowsky, R., Kierfeld, J.: Enhanced ordering of interacting filaments by molecular motors. Phys. Rev. Lett. 96(25) (2006). doi: 10.1103/physrevlett.96.258103
- 13.Kudrolli, A., Lumay, G., Volfson, D., Tsimring, L.S.: Swarming and swirling in self-propelled polar granular rods. Phys. Rev. Lett. 100(5) (2008). doi: 10.1103/physrevlett.100.058001
- 15.Pharr, M., Fernando, R.: GPU Gems 2: Programming Techniques for High-Performance Graphics and General-Purpose Computation. Addison-Wesley, Boston (2005)Google Scholar
- 19.Wolfe, M.: Understanding the cuda data parallel threading model (2010). http://www.pgroup.com/lit/articles/insider/v2n1a5.htm