Dynamic repartitioning of large data model in Distribution Management Systems

Abstract

In this paper, modern Distribution Management Systems (DMS) that utilize multiprocessor systems for efficient processing of large data model are considered. The aim of the research is to obtain an optimal load balancing among processors in terms of memory usage and the calculation execution time. The dynamic repartitioning is performed during execution when an imbalance is detected. Diffusion Repartitioning (DR) and Cut-Paste (CP) algorithms for dynamic repartitioning are discussed. Furthermore, modified versions of DR and CP algorithms, named MDR and MCP, are developed in order to improve dynamic repartitioning running in Non-Uniform Memory Architecture (NUMA) multiprocessor systems. The proposed algorithms were applied on data model describing large power distribution network. Experimental results prove reductions of processors' load imbalance and performance improvements. Bibl. 12, tabl. 2 (in English; abstracts in English and Lithuanian).