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

现实世界的数据，如万维网、社交网络、企业知识网络、生物网络、语义网络等，可以抽象为具有数百万到数十亿个节点和边的巨大而复杂的图的形式。随着这些数据的爆炸性增长，迫切需要数据挖掘、分析和查询工具来快速理解和提取知识。然而，有效地利用现代体系结构的资源并从如此大的图中挖掘有趣的模式仍然具有挑战性。同时，具有多核处理器和图形处理器(具有数百个流核)的商用桌面架构正在为桌面上的并行图形分析和管理打开重要的机会。这项探索性研究旨在提高图挖掘和聚类算法在现代桌面超级计算机上的性能，以利用配备图形处理器的多核系统的能力，并探索和开发新的算法，以减少搜索空间和处理的数据量。

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.