Advection-Based Sparse Data Management for Visualizing Unsteady Flow

作者：Guo, Hanqi^*; Zhang, Jiang; Liu, Richen; Liu, Lu; Yuan, Xiaoru; Huang, Jian; Meng, Xiangfei; Pan, Jingshan

来源：IEEE Transactions on Visualization and Computer Graphics, 2014, 20(12): 2555-2564.

DOI：10.1109/TVCG.2014.2346418

• Flow visualization
• Data management
• High performance visualization
• Key-value store

When computing integral curves and integral surfaces for large-scale unsteady flow fields. a major bottleneck is the widening gap between data access demands and the available bandwidth (both I/O and in-memory). In this work, we explore a novel advection-based scheme to manage flow field data for both efficiency and scalability. The key is to first partition flow field into blocklets (e.g. cells or very fine-grained blocks of cells), and then (pre)fetch and manage blocklets on-demand using a parallel key-value store. The benefits are (1) greatly increasing the scale of local-range analysis (e.g. source-destination queries, streak surface generation) that can fit within any given limit of hardware resources; (2) improving memory and I/O bandwidth-efficiencies as well as the scalability of naive task-parallel particle advection. We demonstrate our method using a prototype system that works on workstation and also in supercomputing environments. Results show significantly reduced I/O overhead compared to accessing raw flow data. and also high scalability on a supercomputer for a variety of applications.

• 出版日期2014-12
• 单位北京大学