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G-ML-octree: An update-efficient index structure for simulating 3D moving objects across GPUs

Lookup NU author(s): Professor Raj Ranjan

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This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE Computer Society, 2018.

For re-use rights please refer to the publisher's terms and conditions.


Abstract

In real simulation applications, simulations often involve large volumes of three-dimensinal (3D) moving objects. With the rapid growth of the scale of simulation-problem domains, it has become a key requirement to efficiently manage massive 3D moving objects. Conventional indexing approaches for managing 3D moving objects during simulations generally sufferfrom excessive update costs. Aiming to this problem, this paper first proposes an update-efficient indexing structure by fusing a loose Octree and one update-memo structure, namely ML-Octree. ML-Octree significantly reduces the update costs of one simulation involving massive 3D moving objects. Towards providing a more efficient indexing approach, this paper has explored the feasibility of paralleling ML-Octree by employing Graphic Processing Unit (GPU). A load-balancing scheme is used to further improve the update performance of the GPU-aided ML-Octree. Finally, a distributed GPU-aided ML-Octree is proposed for large-scale simulations. The experimental results indicate that (1) ML-Octree can acquire the update-performance gain of an order of magnitude similar to that of Octree, (2) the GPU-aided ML-Octree can accelerate 5.07χ fasterthan a parallel ML-Octree with 8 CPU threads on average, (3) the load-balance scheme can improve GPU-aided ML-Octree by 2.3χ on average, and (4) the distributed GPU-aided ML-Octree can efficiently support large-scale simulations.


Publication metadata

Author(s): Deng Z, Wang L, Han W, Ranjan R, Zomaya A

Publication type: Article

Publication status: Published

Journal: IEEE Transactions on Parallel and Distributed Systems

Year: 2018

Volume: 29

Issue: 5

Pages: 1075-1088

Print publication date: 01/05/2018

Online publication date: 28/12/2017

Acceptance date: 25/12/2017

Date deposited: 07/10/2018

ISSN (print): 1045-9219

ISSN (electronic): 1558-2183

Publisher: IEEE Computer Society

URL: https://doi.org/10.1109/TPDS.2017.2787747

DOI: 10.1109/TPDS.2017.2787747

Notes: G-ML-octree


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