Cache-Efficient Parallel Isosurface Extraction for Shared Cache Multicores.

This paper proposes to revisit isosurface extraction algorithms taking into consideration two specific aspects of recent multicore architectures: their intrinsic parallelism associated with the presence of multiple computing cores and their cache hierarchy that often includes private caches as well as caches shared between all cores. Taking advantage of these shared caches require adapting the parallelization scheme to make the core collaborate on cache usage and not compete for it, which can impair performance. We propose to have cores working on independent but close data sets that can all fit in the shared cache. We propose two shared cache aware parallel isosurface algorithms, one based on marching tetrahedra, and one using a min-max tree as acceleration data structure. We theoretically prove that in both cases the number of cache misses is the same as for the sequential algorithm for the same cache size. The algorithms are based on the FastCOL cache-oblivious data layout for irregular meshes. The CO layout also enables to build a very compact min-max tree that leads to a reduced number of cache misses. Experiments confirm the interest of these shared cache aware isosurface algorithms, the performance gain increasing as the shared cache size to core number ratio decreases.