Benchmarking High Performance Graph Analysis Systems with Graph Mining and Pattern Matching Workloads
Authors: Seokyong Hong (North Carolina State University), Seung-Hwan Lim (Oak Ridge National Laboratory), Sangkeun Lee (Oak Ridge National Laboratory), Sreenivas R. Sukumar (Oak Ridge National Laboratory), Ranga R. Vatsavai (North Carolina State University)
Abstract: The increases in volume and inter-connectivity of graph data result in the emergence of recent scalable and high performance graph analysis systems. Those systems provide different graph representation models, a variety of querying interfaces and libraries, and several underlying computation models. As a consequence, such diversities complicate in-situ choices of optimal platforms for data scientists given graph analysis workloads. In this poster presentation, we compare recent high performance and scalable graph analysis systems in distributed and supercomputer-based processing environments with two important graph analysis workloads: graph mining and graph pattern matching. We also compare those systems in terms of expressiveness and suitability of their querying interfaces for the two distinct workloads.
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