|Title||:||On Scaling Graph Algorithms for Parallel Data-driven Scientific Applications|
|Speaker||:||Ananth Kalyanaraman (Washington State University, USA)|
|Details||:||Fri, 16 Dec, 2016 10:00 AM @ BSB 361|
|Abstract:||:||The notion of networks is inherent in the structure, function and behavior of the natural and engineered world that surround us. Consequently, graph models and methods have assumed a prominent role to play in this modern era of Big Data, and are taking a center stage in the discovery pipelines of various data-driven scientific domains. In this talk, I will focus on a subset of such widely used graph operations and related algorithmic needs and challenges. More specifically, I will describe our recent efforts in parallelizing community detection and graph coloring – presenting efficient parallel algorithms and heuristics that are designed to scale on a wide variety of parallel architectures including multi-core/many-core architectures and distributed memory machines. As appropriate and time permitting, I will also relate to some of the unique challenges that one faces while applying and scaling such graph methods on biological datasets.
Biography: Ananth Kalyanaraman is an Associate Professor and Boeing Centennial Chair in Computer Science at the School of Electrical Engineering and Computer Science in Washington State University. He received his PhD from Iowa State University. His main research interests are in designing parallel algorithms and software for solving large-scale data-driven problems in the life sciences. He is a recipient of a DOE Early Career Award, Early Career Impact Award from Iowa State University, and three best paper awards (at CSB 05, IPDPS 06, ACM-BCB 16). He serves on editorial boards of leading parallel computing journals (TPDS, JPDC). Ananth is a member of AAAS, ACM, IEEE-CS, ISCB, and SIAM.