Max-trees, or component trees, are graph structures that represent the connected components of an image in a hierarchical way. Nowadays, many application fields rely on images with high-dynamic range or floating point values. Efficient sequential algorithms exist to build trees and compute attributes for images of any bit depth. However, we show that the current parallel algorithms perform poorly already with integers at bit depths higher than 16 bits per pixel. We propose a parallel method combining the two worlds of flooding and merging max-tree algorithms. First, a pilot max-tree of a quantized version of the image is built in parallel using a flooding method. Later, this structure is used in a parallel leaf-to-root approach to compute efficiently the final max-tree and to drive the merging of the sub-trees computed by the threads. We present an analysis of the performance both on simulated and actual 2D images and 3D volumes. Execution times are about 20× better than the fastest sequential algorithm and speed-up goes up to 30-40 on 64 threads.

中文翻译：

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极端动态范围图像的混合共享内存并行最大树算法


最大树或组件树是一种图形结构，以分层方式表示图像的连接组件。如今，许多应用领域都依赖于具有高动态范围或浮点值的图像。存在有效的顺序算法来构建树并计算任何位深度的图像的属性。然而，我们表明当前的并行算法对于位深度高于每像素 16 位的整数来说性能已经很差了。我们提出了一种结合洪泛和合并最大树算法两个世界的并行方法。首先，使用泛洪方法并行构建图像的量化版本的引导最大树。随后，该结构用于并行叶到根方法，以有效计算最终的最大树并驱动由线程计算的子树的合并。我们对模拟和实际 2D 图像以及 3D 体积的性能进行了分析。执行时间比最快的顺序算法快约 20 倍，并且在 64 个线程上加速可达 30-40。