A decentralized load balancing approach for parallel search-tree optimization

Abstract

Current generation supercomputers have over one million cores awaiting highly demanding computations and applications. An area that could largely benefit from such processing capabilities is naturally that of exact algorithms for NP-hard problems. We propose a general implementation framework that targets highly scalable parallel exact algorithms for NP-hard graph problems. We tackle the problems of efficiency and scalability by combining a fully decentralized dynamic load balancing strategy with special implementation techniques for exact graph algorithms. As a case-study, we use our framework to implement parallel algorithms for the VERTEX COVER and DOMINATING SET problems. We present experimental results that show notable improved running times on all types of input instances.