CCF: EAGER: Collaborative Research: Scalable Graph Mining and Clustering on Desktop Supercomputers

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Real world data, such as World Wide Web, social networks, corporate knowledge networks, biological networks, semantic networks, etc., can be abstracted in the form of a massive and complex graph, with millions to billions of nodes and edges. With the explosion of such data, there is a pressing need for data mining, analysis, and querying tools to rapidly make sense of and extract knowledge. However, effectively leveraging the resources of modern architectures and mining such large graphs for interesting patterns remains challenging. At the same time commodity desktop architectures that have processors with multiple cores and graphics processors (with hundreds of stream cores) are opening up significant opportunities for parallel graph analytics and management on the desktop.This exploratory research seeks to scale up the performance of graph mining and clustering algorithms on modern desktop supercomputers to leverage the power of multi-core systems equipped with graphics processors, and to explore and develop new algorithms for reducing the search space and and the amount of data processed.