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内蒙古自治区呼和浩特市赛罕区大学西街235号 邮编: 010021
作者机构:Peking Univ Key Lab Machine Percept Minist Educ Sch EECS Beijing Peoples R China Peking Univ Ctr Computat Sci & Engn Beijing Peoples R China Univ Tennessee Dept Elect Engn & Comp Sci Knoxville TN USA Natl Supercomp Ctr Tianjin Tianjin Peoples R China Natl Supercomp Ctr Jinan Shandong Peoples R China
出 版 物:《IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS》 (IEEE Trans Visual Comput Graphics)
年 卷 期:2014年第20卷第12期
页 面:2555-2564页
核心收录:
学科分类:0808[工学-电气工程] 08[工学] 0835[工学-软件工程]
基 金:NSFC [61170204, 61232012] Chinese Academy of Sciences [XDA05040205]
主 题:Flow visualization Data management High performance visualization Key-value store
摘 要: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.