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Graph Analytics in the Accelerator-enabled Exascale Era
January 20 @ 7:00 pm - 8:30 pm
Combinatorial algorithms in general, and graph algorithms in particular, play a critical enabling role in numerous scientific applications. The irregular memory access nature of these algorithms makes them one of the hardest algorithmic kernels to implement on parallel systems. To address the challenges, ExaGraph, the co-design center on combinatorial algorithms, was established to design and develop methods and techniques for efficient implementation of key combinatorial (graph) algorithms chosen from a set of exascale applications, targeting accelerator-enabled pre-exascale and exascale systems. Dr. Halappanavar, a chief computer scientist at PNNL, will present a brief overview of the latest work on multi-GPU systems for two prototypical graph problems — graph clustering and influence maximization — and demonstrate substantial gains in performance. He serves as the group leader of the Data Science and Machine Intelligence group. His research has spanned multiple technical foci and includes combinatorial scientific computing, parallel graph algorithms, artificial intelligence and machine learning, and the application of graph theory and game theory to solve problems in application domains, such as scientific computing, power grids, cybersecurity, and life sciences. Contact Chris Gunning, firstname.lastname@example.org, Boise Computer Society chapter chair, if you have a section, chapter, or student branch that would like to be a co-host for this event. Speaker(s): Dr. Mahantesh Halappanavar, Virtual: https://events.vtools.ieee.org/m/297456