CAREER: Scalable Combinatorial Scientific Computing

Ohio State University Research Foundation -Do Not Use, Columbus OH

Investigators

Abstract

CAREER: Scalable Combinatorial Scientific Computing PI: Umit V. Catalyurek Abstract: Combinatorial algorithms are an important enabling technology for scientific computing, especially for large-scale problems and high performance computing. The overarching goals of this project are development of 1) a mathematical and computational infrastructure for solving graph and hypergraph-based combinatorial problems on extreme-scale architectures and 2) techniques and tools for analysis of complex and multivalent interaction networks. In this project, effective novel combinatorial models, such as hypergraph, k-partite hypergraph and directed hypergraph models, are developed for modeling complex workflows (computation, communication and data dependencies, and data access patterns) of large-scale scientific applications and the complicated interactions of chemical and biological entities. New scalable algorithms, based on a multi-level framework, are designed for graph and hypergraph clustering and partitioning, and dynamic load balancing problems. Scalable graph coloring techniques and graph search-based analysis techniques for semantic graphs are developed on top of an extensible distributed memory graph/hypergraph runtime middleware. The technical impact of this work will be in designing efficient models and algorithms for the above mentioned computationally challenging problems. Hence, in many fields of science the work will allow the use of parallel computing where it was not possible before. The education and outreach activities of this project include a summer program for high school students from underrepresented groups, significant expansion of graduate courses, undergraduate student involvement in research projects, and graduate student education through multi-disciplinary projects. The implementations of the novel models and algorithms developed in the project will be distributed as open source to maximize the impact and dissemination of the research results.