A Case Study of Improving Memory Locality in Polygonal Model Simplification: Metrics and Performance

Polygonal model simplifuication algorithms take a full-sized polygonal model as input and output a less-detailed version of the model with fewer polygons. When the internal data structures for the input model are larger than main memory, many simplification algorithms suffer from poor performance due to paging. We present a case study of the recently-introduced R-Simp algorithm and how its data locality and performance can be substantially improved through an off-line spatial sort and an on-line reorganization of its internal data structures. When both techniques are used, R-Simp’s performance improves by up to 7-fold. We empirically characterize the data-access pattern of R-Simp and present an application-specific metric, called cluster pagespan, of R-Simp’s locality of memory reference.