Advection-Based Sparse Data Management for Visualizing Unsteady Flow
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.
Flow visualization Data management High performance visualization Key-value store
Hanqi Guo Jiang Zhang Richen Liu Lu Liu Xiaoru Yuan Jian Huang Xiangfei Meng Jingshan Pan
Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University; Key Laboratory of Machine Perception (Ministry of Education), and School of EECS, Peking University Department of Electrical Engineering and Computer Science, University of Tennessee, Knoxville National Supercomputing Center in Tianjin, Binhai, Tianjin, P.R.China National Supercomputing Center in Jinan, Jinan, Shandong, P.R.China
国际会议
昆明
英文
171-184
2014-05-01（万方平台首次上网日期，不代表论文的发表时间）