Keynote Talk: Large Scale Parallel Sparse Matrix Streaming Graph/Network Analysis

J. Kepner
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Abstract

Groundbreaking work analyzing early Internet data revealed novel phenomena that became the basis of a new endeavor: Network Science. This exciting new field has revealed fundamental properties about communication, social, and biological networks. Simultaneously, the Internet has expanded enormously and is now a domain of activity as important to civilization as land, sea, air, and space. The initial Internet observations that nurtured network science have ballooned and become the largest dynamic streaming data sets availability; creating fresh opportunities to examine the foundations of network science in previously unimagined detail. The analysis of streaming networks with trillions of events have stimulated the development of novel mathematics (e.g., associative array algebra), algorithms (e.g., hypersparse neural networks), software (e.g., GraphBLAS.org), and hardware. All of these capabilities are critically dependent on parallel processing. Application of these developments to the worlds' largest publicly available streaming event datasets have revealed a variety of new phenomena.
主题演讲:大规模并行稀疏矩阵流图/网络分析
分析早期互联网数据的开创性工作揭示了新现象,这些现象成为一项新努力的基础:网络科学。这一令人兴奋的新领域揭示了通信、社会和生物网络的基本特性。与此同时,互联网也得到了极大的扩展,现在已成为一个与陆地、海洋、空中和太空一样重要的文明活动领域。培育了网络科学的最初的互联网观察已经膨胀并成为可用的最大的动态流数据集;创造新的机会,以以前无法想象的细节来研究网络科学的基础。对具有数万亿事件的流网络的分析刺激了新数学(例如,关联数组代数)、算法(例如,超稀疏神经网络)、软件(例如,GraphBLAS.org)和硬件的发展。所有这些功能都严重依赖于并行处理。将这些发展应用于世界上最大的公开流媒体事件数据集,揭示了各种新现象。
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