An Improved Parallel Multiple-point Algorithm Using a List Approach
An Improved Parallel Multiple-point Algorithm Using a List Approach
复制标题
DOI：
10.1007/s11004-011-9328-7
复制
发表时间：
2011-03
影响因子：
2.6
通讯作者：
J. Straubhaar;P. Renard;G. Mariéthoz;R. Froidevaux;O. Besson
中科院分区：
文献类型：
--
作者：
J. Straubhaar;P. Renard;G. Mariéthoz;R. Froidevaux;O. Besson
在用于模拟分类变量的技术中，多点统计变得非常流行，因为它允许用户通过训练图像提供明确的概念模型。在经典的实现中，通过将所有观察到的一定大小的模式存储在树结构中，从训练图像推断出多点统计数据。这种类型的算法具有应用速度快的优点，但也存在一些关键的局限性。特别是，树对 RAM 的要求极高。对于多相的三维问题，不能使用大模板。复杂的结构很难模拟。在本文中，我们建议用列表代替树。这种结构需要更少的 RAM。它具有三个主要优点。首先，它允许使用更大的模板。其次，列表结构简洁，可以扩展以包含附加信息。在这里，我们展示了如何使用它来开发一种处理非平稳训练图像的新方法。最后，该列表的一个有趣的方面是它允许并行化计算条件概率密度函数的算法部分。这对于可以在具有分布式内存的 PC 集群或具有共享内存的多核计算机上解决的大型问题尤其重要。
Among the techniques used to simulate categorical variables, multiple-point statistics is becoming very popular because it allows the user to provide an explicit conceptual model via a training image. In classic implementations, the multiple-point statistics are inferred from the training image by storing all the observed patterns of a certain size in a tree structure. This type of algorithm has the advantage of being fast to apply, but it presents some critical limitations. In particular, a tree is extremely RAM demanding. For three-dimensional problems with numerous facies, large templates cannot be used. Complex structures are then difficult to simulate.In this paper, we propose to replace the tree by a list. This structure requires much less RAM. It has three main advantages. First, it allows for the use of larger templates. Second, the list structure being parsimonious, it can be extended to include additional information. Here, we show how this can be used to develop a new approach for dealing with non-stationary training images. Finally, an interesting aspect of the list is that it allows one to parallelize the part of the algorithm in which the conditional probability density function is computed. This is especially important for large problems that can be solved on clusters of PCs with distributed memory or on multicore machines with shared memory.