Abstract
Big graphs are finding increasing applications in many science and engineering domains, such as computational biology, cybermanufacturing and social media. Graphs provide a very flexible mathematical abstraction for describing relationships between entities in complex systems. Real world graphs are characterized by high connectivity and high irregularity. Such non-uniform characteristics increase the mismatch between the vertex centric parallel computation model and the computer hardware resources. Another problem with the vertex-centric computation model is that it treats vertices symmetrically and this uniform assumption breaks when graphs exhibit high irregularity and graph algorithms reveal non-uniform workloads. In this keynote, I will advocate a fundamental revisit of graph computation models and promotes a methodical framework for support high performance graph parallel abstractions that are resource aware, composable and programmable. I will discuss a suite of graph optimization techniques that explore workload characteristics of graph algorithms and irregularity hidden in graph structures. I will conclude the talk by presenting some interesting research problems and unique opportunities for big graph analytics.
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