异构环境下基于分组的图像合成算法

系统仿真学报 ›› 2015, Vol. 27 ›› Issue (8): 1801-1808.

异构环境下基于分组的图像合成算法

刘宁^1,2, 朱登明¹, 王兆其¹, 魏毅¹

1.中国科学院计算技术研究所,北京 100190;
2.中国科学院大学,北京 100049

收稿日期:2015-06-04 修回日期:2015-07-02 出版日期:2015-08-08 发布日期:2020-08-03
作者简介:刘宁(1987-),男,博士生,研究方向为科学数据可视化;朱登明(1973-),男,博士,副研究员,研究方向为自然现象模拟和数据可视化;王兆其(1966-),男,博士,研究员,研究方向为虚拟现实和智能人机交互。
基金资助:
国家自然科学基金(61173067, 61379085)

Group-based Image Compositing in Heterogeneous Environments

Liu Ning^1,2, Zhu Dengming¹, Wang Zhaoqi¹, Wei Yi¹

1. Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China;
2. University of Chinese Academy of Sciences, Beijing 100049, China

Received:2015-06-04 Revised:2015-07-02 Online:2015-08-08 Published:2020-08-03

摘要/Abstract

摘要： 图像合成由于需要密集的节点间通信,因此很容易成为整个并行可视化系统的瓶颈部分。传统的图像合成算法均假定环境是同构的,这种假定在异构环境下可能会造成严重的负载不均衡。针对这个问题,提出了一个异构环境下基于分组的图像合成算法。它能够根据环境的计算与网络特性更合理地进行任务分配。在ns-3网络模拟器上进行了详尽的实验评估。实验结果表明当环境存在比较大的异构性时,该方法相对于传统图像合成算法能够取得显著的加速比。

关键词: 图像合成, 异构环境, 并行可视化, ns-3网络模拟器

Abstract: Image compositing requires dense inter-node communications and could easily become the bottleneck of a parallel visualization system. Conventional image compositing algorithms assume the environment is homogeneous and take no heterogeneity into consideration. In a heterogeneous environment, this assumption is likely to cause unreasonable load distribution among the nodes. To address this problem, we present a group-based image compositing algorithm in heterogeneous environments. It is able to distribute the workloads among the nodes according to computing and networking characteristics of the heterogeneous environments. We conduct a comprehensive evaluation on the ns-3 network simulator. Experiment results shows that when the environment has great heterogeneity, our algorithm gains significant speedup over conventional methods.

Key words: image compositing, heterogeneous environment, parallel visualization, ns-3 simulator

中图分类号:

TP391

刘宁, 朱登明, 王兆其, 魏毅. 异构环境下基于分组的图像合成算法[J]. 系统仿真学报, 2015, 27(8): 1801-1808.

Liu Ning, Zhu Dengming, Wang Zhaoqi, Wei Yi. Group-based Image Compositing in Heterogeneous Environments[J]. Journal of System Simulation, 2015, 27(8): 1801-1808.

参考文献

[1] Bentes C., Labronici B.B., Drummond L. M., Farias R. Towards an efficient parallel raycasting of unstructured volumetric data on distributed environments[J]. Cluster Computing (S1386-7857), 2014, 17(2): 423-439.
[2] 单桂华, 田东, 谢茂金, 等. 千万亿次科学计算的原位可视化[J]. 计算机辅助设计与图形学学报, 2013, 25(3): 286-293.
[3] 丁治宇, 陈海东, 吴斐然, 等. 多变量空间数据场可视化综述[J]. 计算机辅助设计与图形学学报, 2013, 25(11): 1597-1605.
[4] Molnar S., Cox M., Ellsworth D., Fuchs H.A sorting classification of parallel rendering[J]. IEEE Computer Graphics and Applications (S0272-1716),1994, 14(4): 23-32.
[5] Neumann U.Parallel volume-rendering algorithm performance on mesh-connected multicomputers [C]// Proceedings of the Symposium on Parallel Rendering. New York, USA: ACM Press, 1993: 97-104.
[6] Ma K.L., Painter J., Hansen C., Krogh M. Parallel volume rendering using binary-swap compositing[J]. IEEE Computer Graphics and Applications (S0272-1716), 1994, 14(4): 59-68.
[7] Yu H., Wang C., Ma K.L. Massively parallel volume rendering using 2-3 swap image compositing [C] // Proceedings of the ACM/IEEE Conference on Supercomputing. New Jersey, USA: IEEE Press, 2008, 48: 1-11.
[8] Peterka T., Goodell D., Ross R., Shen H.W., Thakur R. A configurable algorithm for parallel image-compositing applications [C] // Proceedings of the Conference on High Performance Computing Networking, Storage and Analysis. New York, USA: ACM Press, 2009, 4: 1-10.
[9] Wu Q., Gao J., Chen Z., Zhu M.Pipelining parallel image compositing and delivery for efficient remote visualization[J]. Journal of Parallel and Distributed Computing (S0743-7315), 2009, 69(3): 230-238
[10] ns-3 network simulator [OL]. (2015-04-01). http://www.nsnam.org/
[11] Yu H., Wang C., Ma K.L. Parallel hierarchical visualization of large time-varying 3d vector fields [C]// Proceedings of the ACM/IEEE Conference on Supercomputing. New York, USA: ACM Press, 2007, 24:1-12.
[12] Bentes C., Labronici B.B., Drummond L. M., Farias R. Towards an efficient parallel raycasting of unstructured volumetric data on distributed environments[J]. Cluster Computing (S1386-7857), 2014, 17(2): 423-439
[13] Kendall W., Peterka T., Huang J., Shen H.W., Ross R. Accelerating and benchmarking radix-k image compositing at large scale[C] //Proceedings of the Eurographics Conference on Parallel Graphics and Visualization. Aire-la-Ville: Eurographics Association, 2010: 101-110.
[14] Moreland K., Kendall W., Peterka T., Huang J.An image compositing solution at scale [C]// Proceedings of International Conference for High Performance Computing, Networking, Storage and Analysis. New York, USA: ACM Press, 2011, 25: 1-10.
[15] D.L. Yang, J.C. Yu,Y.C. Chung.Efficient compositing methods for the sort-last-sparse parallel volume rendering system on distributed memory multicomputers[C]// Proceedings of the International Conference on Parallel Processing. Washington, USA: IEEE Computer Society, 1999: 200-220.