Out-of-core streamline visualisation based on adaptive partitioning and data prefetching Online publication date: Fri, 29-Nov-2019
by Yumeng Guo; Wenke Wang; Sikun Li
International Journal of Computational Science and Engineering (IJCSE), Vol. 20, No. 2, 2019
Abstract: As huge amounts of flow data come into being every day, it is challengeable for most flow field visualisation applications on a single PC to handle the large-scale data, because of the memory size restriction. To address the problem, out-of-core strategy with data prefetching is frequently applied to load the data on demand and fill in the speed gap between I/O and computation. In this paper, we focus on improving the efficiency of data-prefetching large-scale streamline visualisation by elevating the hit rate of data block prediction. Our key idea is to extract feature information of the field and then adopt a partitioning strategy that slices important regions into smaller blocks. Experiments show that the major measurement of our partitioning strategy for data prefetching is much better than conventional uniform-partitioned methods, and the total execution time of visualisation system decreases by 10% on average.
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