| 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. The framework supports concurrent optimization instances, for instance multiple swarms or islands. PRIME provides a lightweight communication layer to facilitate periodic inter-optimizer data exchanges. A parallel search method, COMSO (Cooperative Multi-Swarm Optimization) was designed and tested on various high dimensional mathematical benchmark problems. COMSO showed improved algorithmic performance over baseline PSO. Additionally, this work presents a novel technique, TAPSO (Topology Aware Particle Swarm Optimization) for network based optimization problems. Empirical studies demonstrate that TAPSO achieves better convergence than standard PSO for Water Distribution Systems (WDS) applications. Scalability analysis of Optimus was performed on the Cray XK6 supercomputer (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. |
