“Massive particles: particle-based simulations on multiple GPUs” by Harada, Masaie, Koshizuka and Kawaguchi

  • ©Takahiro Harada, Issei Masaie, Seiichi Koshizuka, and Yoichiro Kawaguchi




    Massive particles: particle-based simulations on multiple GPUs



    There is no study using multiple GPUs for particle-based simulation to the best of our knowledge although several researchers have been using a GPU. In this study, a particle-based simulation is parallelized on multiple GPUs. There are several challenges to accomplish it. For example, the simulation should not have serial computations that can be a bottleneck of the simulation. Particle methods cannot assign a fixed computation data to each GPU but the data has to be reassigned each iteration because particles not having any fixed connectivity can move freely. It is difficult for a particle-based simulation to scale the performance to the number of GPUs because the overhead of the parallelization can be high. We overcame these hurdles by employing not a server-client computation model but a peer-to-peer model among GPUs. Each GPU dynamically manages their own computation data without a server. A sliced-grid was used to lower the traffic among GPUs. As a result, the simulation speed scales well to the number of GPUs and the method brings it possible to simulate millions of particles in real-time. Of course, the proposed method is effective not only for a simulation on GPUs but also one on CPUs. The contribution of this study also includes a sorting technique utilizing the coherency between the time steps which was also introduced to increase the performance on a GPU.


    1. Harada, T., Koshizuka, S., and Kawaguchi, Y. 2007. Sliced-data structure for particle based simulations on gpus. In Proc. of GRAPHITE, 55–62.
    2. NVIDIA. Compute unified device architecture. http://www.nvidia.com/object/cuda_home.html.

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