nested openmp for efficient computation of 3d critical points in multi-block cfd datasets
Extraction of complex data structures like vector field topologies in large-scale, unsteady flow field datasets for the interactive exploration in virtual environments cannot be carried out without parallelization strategies. We present an approach based on Nested OpenMP to find critical points, which are the essential parts of velocity field topologies. We evaluate our parallelization scheme on several multi-block datasets, and present the results for various thread counts and loop schedules on all parallelization levels. Our experience suggests that upcoming massively multi-threaded processor architectures can be very advantageously for large-scale feature extractions.
multi-threading nested parallelization flow field topology cfd post-processing virtual reality