分布式存储八叉树森林光线投射
Distributed-Memory Forest-of-Octrees Raycasting
摘要：
我们提出了一个MPI并行算法的计算数据，是建立在一个分布式的线性森林八叉树数据结构的原位可视化。这种八叉树经常用于基于元素的数值模拟;它们存储当前并行分区中局部的树的叶子。我们分三个阶段进行。首先，我们通过并行的自顶向下遍历删除所有其限界框不可见的元素，并重新划分剩余的元素以实现负载平衡。其次，我们将每个元素与通过其盒子的每条光线相交，以获得光线段的颜色和不透明度值。为了减少数据，我们在粗化和重新分区的循环中以严格分布的方式将片段聚集到八叉树上。第三，我们将所有剩余的光线片段合成为图像的平铺分区，并使用并行I/O将其写入磁盘。该方法的可扩展性来自三个概念：通过利用空间填充曲线编码的八叉树，并依靠最近开发的树算法自上而下的分区遍历，我们能够确定发送器/接收器对没有握手和/或集体通信。此外，通过将树叶的线性遍历与沿每个段的衰减/发射ODE的组作用相结合，我们避免了元素的前后排序。最后，该方法是问题自适应方面的细化和划分的元素和ODE积分的精度。
英文摘要：
We present an MPI-parallel algorithm for the in-situ visualization of computational data that is built around a distributed linear forest-of-octrees data structure. Such octrees are frequently used in element-based numerical simulations; they store the leaves of the tree that are local in the curent parallel partition. We proceed in three stages. First, we prune all elements whose bounding box is not visible by a parallel top-down traversal, and repartition the remaining ones for load-balancing. Second, we intersect each element with every ray passing its box to derive color and opacity values for the ray segment. To reduce data, we aggregate the segments up the octree in a strictly distributed fashion in cycles of coarsening and repartition. Third, we composite all remaining ray segments to a tiled partition of the image and write it to disk using parallel I/O. The scalability of the method derives from three concepts: By exploiting the space filling curve encoding of the octrees and by relying on recently developed tree algorithms for top-down partition traversal, we are able to determine sender/receiver pairs without handshaking and/or collective communication. Furthermore, by partnering the linear traversal of tree leaves with the group action of the attenuation/emission ODE along each segment, we avoid back-to-front sorting of elements throughout. Lastly, the method is problem adaptive with respect to the refinement and partition of the elements and to the accuracy of ODE integration.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
