摘要 : 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 probl... 展开 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. 收起