Title: Parallel computing of texture-optimisation-based flow visualisation on CUDA

Authors: Ying Tang; Zhan Zhou; Xiaoying Shi; Jing Fan

Addresses: College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang Hangzhou 310023, China ' College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang Hangzhou 310023, China ' College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang Hangzhou 310023, China ' College of Computer Science and Technology, Zhejiang University of Technology, Zhejiang Hangzhou 310023, China

Abstract: Flow visualisation effectively visualises flow fields with moving textures by vividly capturing the flow field properties through varying texture appearances. Texture-optimisation-based (TOB) flow visualisation can produce excellent visualisation results of flow fields. However, TOB flow visualisation is a slow process with a huge amount of time-consuming computation of nearest neighbour searching and thus is difficult to be applied to dynamic flow field visualisation. In this paper, we propose an optimal acceleration scheme for speed and quality for searching the approximate nearest neighbour by comparing and analysing three techniques to accelerate the computation of the nearest neighbour. We achieve the parallel computation of TOB flow visualisation algorithm based on CUDA implementation on graphics processing unit (GPU). Most time-consuming computations are performed in parallel on GPU, which yields high performance. Experimental results show that our TOB flow visualisation generates results with fast synthesis speed and high synthesis quality. This method can visualise not only static flow fields but also time-varying or dynamic flow fields.

Keywords: parallel computing; flow visualisation; texture optimisation; compute unified device architecture; CUDA; approximate nearest neighbour; high performance computing; graphics processing units; GPU; static flow fields; time-varying flow fields; dynamic flow fields.

DOI: 10.1504/IJHPCN.2016.076231

International Journal of High Performance Computing and Networking, 2016 Vol.9 No.3, pp.258 - 270

Received: 01 Aug 2013
Accepted: 23 May 2014
Published online: 30 Apr 2016 *

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