大规模并行体系结构的α -树算法

IF 13.7
Edwin Carlinet;Quentin Kaci;Nicolas Blin
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引用次数: 0

摘要

α -树,也称为准平坦区层次结构,是数学形态学中广泛使用的图像表示方法。这种结构根据相似度标准将区域组织成树状,便于图像的多尺度分析。存在许多α -树算法,有效地计算这种结构仍然是一个活跃的研究领域。事实上,alpha-tree通常用于遥感领域,在那里需要快速处理大tb级的图像。在本文中,我们提出了第一个大规模并行alpha-tree算法,该算法利用并发联合查找数据结构来利用gpu的SIMT(单指令多线程)编程模型。在台式计算机和服务器上,我们的算法比最先进的并行CPU算法平均性能高出10倍。它还为在GPU管道上使用数学形态学方法打开了新的视角。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Alpha-Tree Algorithm for Massively Parallel Architectures
The alpha-tree, also known as the quasi-flat zone hierarchy is a widely used representation of images in Mathematical Morphology. This structure organizes the regions according to a similarity criterion into a tree, that eases the multiscale analysis of images. Many alpha-tree algorithms exist and computing this structure efficiently is still an active field of research. Indeed, the alpha-tree is commonly used in remote sensing where there is an urge for fast processing of large terabytes images. In this paper, we propose the first massively parallel alpha-tree algorithm that leverages concurrent union-find data structures to exploit the SIMT (Single Instruction Multiple Threads) programming model of GPUs. Our algorithm outperforms the State-of-the-Art parallel CPU algorithms by a factor of 10 on average on desktop computers and servers. It also opens new perspectives for using Mathematical Morphology methods on GPU pipelines.
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