Exploiting Domain Knowledge to Optimize Mesh Partitioning for Multi-Scale Methods
Authors: Muhammad Hasan Jamal (Purdue University), Milind Kulkarni (Purdue University), Arun Prakash (Purdue University)
Abstract: Multi-scale computational methods are widely used for complex scientific computing problems that span multiple spatial and temporal scales. These problem meshes are decomposed into multiple subdomains that are solved independently at different timescales and granularity and then coupled back to get the desired solution. The computational cost associated with different scales can vary by multiple orders of magnitude. Hence the problem of finding an optimal mesh partitioning, choosing appropriate timescales for the partitions, and determining the number of partitions at each timescale is non-trivial. Existing partitioning tools, overlook the constraints posed by multi-scale methods, leading to sub-optimal partitions with a high performance penalty.
Our partitioning approach exploits domain knowledge to handle multi-scale problems appropriately and produces optimized mesh partitioning automatically. Our approach produce decompositions that perform as well as, if not better than, decompositions produced by state-of-the-art partitioners, like METIS, and even those that are manually constructed by domain scientists.
Two-page extended abstract: pdf