An out-of-core method for computing connectivities of large unstructured meshes

Adjacency graphs of meshes are important for visualizing or compressing unstructured scientific data. However, calculating adjacency graphs requires intensive memory space. For large data sets, the calculation becomes very inefficient on desk-top computers with limited main memory. In this article, an out-of-core method is presented for finding connectivities of large unstructured FEA data sets. Our algorithm composes of three stages. At the first stage, FEA cells are read into main memory in blocks. For each cell block read, cell faces are generated and distributed into disjoint groups. These groups are small enough such that each group can reside in main memory without causing any page swapping. The resulted groups are stored in disk files. At the second stage, the face groups are fetched into main memory and processed there one after another. Adjacency graph edges are determined in each face group by sorting faces and examining consecutive faces. The edges contained in a group are kept in a disk file. At the third stage, edge files are merged into a single file by using external merge sort, and the connectivity information is computed.