Integrated Research and Education in High-Performance Parallel Optimization Algorithms
Nihar Mahapatra Principal Investigator
MetadataShow full item record
Abstract for 0102830 Mahapatra<br/>"Integrated Research and Education in High-Performance Parallel Optimization Algorithms"<br/><br/>This project will perform integrated research and education activities in the multidisciplinary area of parallel optimization. Given the enormous potential role of parallel computing in solving large-scale optimization problems with great societal implications, it is imperative that future scientists and engineers learn its fundamentals. The education component of this project will contribute towards bridging the current gap in knowledge of those professionals. The research activities center around efficient parallelization of an important optimization method called branch-and- bound (B&B), widely used for solving real-world combinatorial optimization problems (COPs). B&B's applications run the gamut of Science, Engineering, Mathematics, and Operations Research, with significant new uses being discovered every year. Research in B&B is performed by two groups of researchers: workers in parallel processing who use sophisticated parallelization techniques in conjunction with simple B&B algorithms, and hence are able to solve COPs of limited size; and workers in operations research who develop and use sophisticated application-specific B&B algorithms with little or no parallelism, to solve larger COPs. The overall objective of the proposed research is to improve solution time and quality for some important optimization problems by an order of magnitude, or to solve previously intractable problems, by applying scalable, high-performance parallelization techniques to application-specific B&B methods.<br/><br/>Technically, the specific goals of the proposed project are as follows. <br/>(1) Adaptive Load Balancing: To develop load balancing schemes that adapt to application and target-system characteristics to maximize processor utilization. <br/>(2) Efficient Limited-Memory Search: To develop efficient search schemes that enable large problems to be solved within the available memory of practical parallel/distributed systems. <br/>(3) Specialized B&B Methods: To develop specialized B&B methods for some important COPs like mixed-integer programming and the traveling salesman problem, and use these to demonstrate solution time and quality improvements for real-world instances of those problems. <br/>(4) Parallel Optimization Course and Web Resource: To develop a model course on parallel optimization for upper-level undergraduate and beginning graduate students, as well as a comprehensive, searchable web resource on parallel optimization useful for education. <br/>(5) Parallel B&B Software Environment: To incorporate the parallelization techniques developed in this project in a software system for use as an educational and research tool for fast, efficient solution of optimization problems using parallel B&B.