Distributed Domain Generation for Large-Scale Scientific Computing

In this work, we present methods for distributed domain generation within the constraints of our decentral domain management concept. Here, all participating actors only have knowledge of their immediate neighbours, which are defined by geometric and hierarchical relations between nodes that represent subsets of the computational domain. We generate this domain following a hierarchical space tree refinement. First, an initial tree is generated on every participating process. Second, this tree is distributed following a space-filling curve linearisation locally. Every process is assigned at least one leaf node of the initial tree, which acts as a starting point for the subsequent domain generation. From here, every process independently refines a subdomain using a decomposition method, which transforms a triangular surface-based geometry description into a volume-based one, using increasingly complex intersection tests. The resulting domain tree is distributed, yet neighbourhood references of neighbouring subtrees are not resolved. We combine the resolution of these relations with a 2:1 tree balancing, which involves the transfer of the surface of neighbouring subtrees. We provide results of a domain generation testcase, using an input geometry with 84,072 triangles on up to 896 processes of the CoolMUC-2 cluster segment of LRZ’s Linux Cluster System. Here, we bring down the overall time it takes to generate an adaptively refined and balanced octree with depth d = 7 from 5.5 hours on one process to two seconds on 896 processes.