电子学报 ›› 2016, Vol. 44 ›› Issue (2): 472-478.DOI: 10.3969/j.issn.0372-2112.2016.02.031

• 科研通信 • 上一篇    下一篇

海量超声数据体可视化研究

潘卫国1, 何宁2, 薛健1, 吕科1, 翟锐1, 代双凤1   

  1. 1. 中国科学院大学工程管理与信息技术学院, 北京 100049;
    2. 北京联合大学信息学院, 北京 100101
  • 收稿日期:2014-06-19 修回日期:2015-03-02 出版日期:2016-02-25 发布日期:2016-02-25
  • 通讯作者: 吕科
  • 作者简介:潘卫国 男,1984年生于河北邯郸,中国科学院大学在读博士生.研究方向为三维可视化,计算机图形学.E-mail:asherbuu@163.com
  • 基金资助:

    国家自然科学基金(No.U1301251,No.61271435);北京市自然科学基金(No.4141003)

Research of Large Ultrasonic Data Visualization

PAN Wei-guo1, HE Ning2, XUE Jian1, LÜ Ke1, ZHAI Rui1, DAI Shuang-feng1   

  1. 1. College of Engineering and Information Technology, University of Chinese Academy of Sciences, Beijing 100049, China;
    2. Beijing Union University, College of Information Technology, Beijing 100101, China
  • Received:2014-06-19 Revised:2015-03-02 Online:2016-02-25 Published:2016-02-25

摘要:

近年来,随着科学数据的快速增长,海量数据的可视化分析成了急需解决的难题.越来越多的处理海量数据的方法向着并行、分布式处理的方向发展.本文提出了一种混合的框架来处理海量的超声数据,该框架通过整合多种硬件环境和计算资源来处理海量数据;所有的数据都存放在一个基于高速网络环境的数据共享中心,具有高性能显卡的前端工作站将耗时的处理任务分配到网络中的计算结点,而自身处理显示和交互的操作;同时基于OpenCL和OpenMP实现了可视化算法在GPU和CPU上的并行计算;核外算法应用在本框架中来处理海量的体数据.实验结果表明,本文提出的框架不仅可以处理海量数据,而且具有较高的交互性能.

关键词: 体绘制, 图形处理器, 核外技术, 并行计算, 海量数据

Abstract:

In recent years, with the rapid growth of scientific data, large data analysis has become urgent problems. More and more large-data processing methods are modified to perform computation under parallel and distributed computing environment.In this paper, we present a hybrid architecture for large volume data visualization and processing.Various hardware environments and technologies are integrated in this architecture to perform interactive operations on very large volume datasets.All the datasets are stored in a data center with a gigabit network environment.The time-consuming data processing tasks are dispatched to the computing nodes connected to the same network, while the visualization and interaction operations are executed on a high-performance graphics workstation.OpenCL and OpenMP are used to implement volume rendering algorithms for accelerating visualization of a hierarchical volume data structure by both GPU and CPU with multi-cores, and some out-of-core algorithms are also presented to process the large dataset directly.The experimental results and practical application indicate that the hybrid architecture and methods presented in this paper are effective and efficient for the processing and visualization of very large volume datasets.

Key words: volume rendering, graphics processing unit(GPU), out-of-core, parallel computing, large data

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