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Qualifying Exam

Scalable graph embedding on GPUs


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Thursday, August 19, 2021, 09:00am - 10:30am


Speaker: Azita Nouri

Location : Remote via Zoom


Prof. Badri Nath (advisor)

Prof. Manish Parashar

Prof. Srinivas Narayana Ganapathy

Prof. Konstantinos Michmizos

Event Type: Qualifying Exam

Abstract: Graph embedding techniques have attracted growing interest since they convert the graph data into continuous and low-dimensional space. Effective graph analytic provides users a deeper understanding of what is behind the data and thus can benefit a variety of machine learning tasks. With the current scale of real-world applications, most graph analytic methods suffer high computation and space costs. These methods and systems can process a network with thousands to million nodes and edges. However, scaling to networks with billions of nodes and edges remains a challenge. We propose a hybrid CPU-GPU system for large-scale graphs that overpass the limitation of previous methods. Empirical experiments show the efficiency of our system on a variety of real-world information networks, including language networks, social networks, and citation networks.