Scalable computation of streamlines on very large datasets
Scalable computation of streamlines on very large datasets
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DOI：
10.1145/1654059.1654076
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发表时间：
2009-11
期刊：
影响因子：
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通讯作者：
D. Pugmire;H. Childs;C. Garth;Sean Ahern;G. Weber
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文献类型：
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作者：
D. Pugmire;H. Childs;C. Garth;Sean Ahern;G. Weber
理解大型科学模拟产生的矢量场是一项重要且往往困难的任务。流线是在每个点上与矢量场相切的曲线，在这种情况下是一种强大的可视化方法。由于流线计算的非局部性和数据依赖性，将基于流线的可视化应用于非常大的矢量场数据是一个巨大的挑战，并且需要仔细平衡对I/O、内存、通信和处理器的计算需求。在本文中，我们回顾了两种基于已建立的并行化范例(静态分解和按需加载)的并行化方法，并提出了一种新的计算流线的混合算法。我们的算法的目标是在基于流线的问题的广泛变化的计算特征上具有良好的可扩展性和性能。我们在一些典型的应用问题上对这三种算法的性能和可扩展性进行了研究，并证明了我们的混合方案能够在不同的环境下很好地执行。
Understanding vector fields resulting from large scientific simulations is an important and often difficult task. Streamlines, curves that are tangential to a vector field at each point, are a powerful visualization method in this context. Application of streamline-based visualization to very large vector field data represents a significant challenge due to the non-local and data-dependent nature of streamline computation, and requires careful balancing of computational demands placed on I/O, memory, communication, and processors. In this paper we review two parallelization approaches based on established parallelization paradigms (static decomposition and on-demand loading) and present a novel hybrid algorithm for computing streamlines. Our algorithm is aimed at good scalability and performance across the widely varying computational characteristics of streamline-based problems. We perform performance and scalability studies of all three algorithms on a number of prototypical application problems and demonstrate that our hybrid scheme is able to perform well in different settings.