Optimus: A Parallel Optimization Framework with Topology Aware PSO and Applications

Abstract

This research presents a parallel metaheuristic optimization framework, Optimus (Optimization Methods for Universal Simulators) for integration of a desired population-based search method with a target scientific application. Optimus includes a parallel middleware component, PRIME (Parallel Reconfigurable Iterative Middleware Engine) for scalable deployment on emergent supercomputing architectures. Additionally, we designed TAPSO (Topology Aware Particle Swarm Optimization) for network based optimization problems and applied it to achieve better convergence for water distribution system (WDS) applications. The framework supports concurrent optimization instances, for instance multiple swarms in the case of PSO. PRIME provides a lightweight communication layer to facilitate periodic inter-optimizer data exchanges. We performed scalability analysis of Optimus on Cray XK6(Jaguar) at Oak Ridge Leadership Computing Facility for the leak detection problem in WDS. For a weak scaling scenario, we achieved 84.82% of baseline at 200,000 cores relative to performance at 1000 cores and 72.84% relative to one core scenario.