NUMA-ICTM: A parallel version of ICTM exploiting memory placement strategies for NUMA machines

In geophysics, the appropriate subdivision of a region into segments is extremely important. ICTM (interval categorizer tesselation model) is an application that categorizes geographic regions using information extracted from satellite images. The categorization of large regions is a computational intensive problem, what justifies the proposal and development of parallel solutions in order to improve its applicability. Recent advances in multiprocessor architectures lead to the emergence of NUMA (non-uniform memory access) machines. In this work, we present NUMA-ICTM: a parallel solution of ICTM for NUMA machines. First, we parallelize ICTM using OpenMP. After, we improve the OpenMP solution using the MAI (memory affinity interface) library, which allows a control of memory allocation in NUMA machines. The results show that the optimization of memory allocation leads to significant performance gains over the pure OpenMP parallel solution.