Seminar: High-performance Graph Analytics

Event Date: 
Fri, 2017-10-20 11:15 - 12:15

Location: 2150 Torgersen Hall

Graph-theoretic abstractions are at the core of data-intensive problems arising in social and technological network analysis (e.g.,identifying implicit online communities, quantifying centrality andinfluence in interaction networks, web algorithms), systems biology (forinstance, interactome analysis, epidemiological studies, disease modeling),and security applications (e.g., detecting anomalous patterns fromsocio-economic interactions and communication data). Due to their large memory footprint, irregular access patterns, and low degrees of spatial locality, computations on massive graphs pose serious challenges on current parallel machines. In this talk, I will present my research group's recent work on enabling large-scale and high-performance graph analysis. Our parallel implementations on multicore servers and leading supercomputers achieve significant parallel speedup for traversal, connectivity, and centrality problems on graph instances in the order of billions of vertices and edges. I will also describe the parallel algorithms and implementation of two software tools that our group has developed: FASCIA for approximately counting and enumerating network motifs, and PULP formulti-objective graph partitioning.

Kamesh Madduri is an assistant professor in the Computer Science andEngineering department at The Pennsylvania State University. He received his PhD in Computer Science from Georgia Institute of Technology's College of Computing in 2008, and was previously a Luis W. Alvarez postdoctoral fellow at Lawrence Berkeley National Laboratory. Madduri conducts research on the design of new parallel algorithms and software tools for analyzing massive data sets and in support of large computational science simulations. His current research focuses on four topics: algorithms for graph analysis on emerging parallel systems, computational genomics, algorithms for particle simulations in plasma physics, and indexing and query strategies for high-dimensional scientific and transportation data sets. Madduri has published extensively in the area of high performance computing, co-authoring over 60 peer-reviewed articles. According to Google Scholar, his published work has been cited over 2300 times with an h-index of 26. He is a recipient of the NSF CAREER award (2013), a co-recipient of the best paper award at the 42nd International Conference on Parallel Processing (2013), and was awarded the first Junior Scientist prize by the SIAM Activity group on Supercomputing (2010). He is a member of ACM and SIAM.