Task-Tree Based Large-Scale Mosaicking for Massive Remote Sensed Imageries with Dynamic DAG Scheduling
Task-Tree Based Large-Scale Mosaicking for Massive Remote Sensed Imageries with Dynamic DAG Scheduling
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DOI：
10.1109/tpds.2013.272
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发表时间：
2014-08
影响因子：
5.3
通讯作者：
Yan Ma;Lizhe Wang;Albert Y. Zomaya;Dan Chen;R. Ranjan
中科院分区：
文献类型：
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作者：
Yan Ma;Lizhe Wang;Albert Y. Zomaya;Dan Chen;R. Ranjan
大尺度遥感影像镶嵌在区域和全球研究中受到越来越多的关注。然而，当扩展到大范围时，图像马赛克对于大量任务之间的依赖关系变得非常具有挑战性，这导致了排序限制、对显著处理能力的需求以及组织这些巨大任务和遥感图像数据所固有的困难。提出了一种基于任务树的大尺度遥感图像拼接动态DAG调度算法。它将大规模马赛克表示为具有最小高度的数据驱动的任务树。提出了一种基于关键路径的状态队列动态DAG调度方案CPDS-SQ，在多核集群上以最小的完成时间优化调度。所有独立的相关任务都由一个用MPI实现的核心并行马赛克程序运行，以在不同的图像对上执行马赛克。最后，通过将任务之间的依赖关系从复杂的并行处理过程中解耦出来，为提高大规模处理能力提供了一种有效而简单的方法。通过大规模镶嵌实验，我们证实了我们的方法是有效的和可扩展的。
Remote sensed imagery mosaicking at large scale has been receiving increasing attentions in regional to global research. However, when scaling to large areas, image mosaicking becomes extremely challenging for the dependency relationships among a large collection of tasks which give rise to ordering constraint, the demand of significant processing capabilities and also the difficulties inherent in organizing these enormous tasks and RS image data. We propose a task-tree based mosaicking for remote sensed imageries at large scale with dynamic DAG scheduling. It expresses large scale mosaicking as a data-driven task tree with minimal height. And also a critical path based dynamical DAG scheduling solution with status queue named CPDS-SQ is provided to offer an optimized schedule on multi-core cluster with minimal completion time. All the individual dependent tasks are run by a core parallel mosaicking program implemented with MPI to perform mosaicking on different pairs of images. Eventually, an effective but easier approach is offered to improve the large-scale processing capability by decoupling the dependence relationships among tasks from the complex parallel processing procedure. Through experiments on large-scale mosaicking, we confirmed that our approach were efficient and scalable.