A Real Life Research Tool in Second Life: PrimPlotter

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University of Michigan Taubman Health Sciences Library

By Gus Rosania, faculty in the University of Michigan School of Pharmacy, this presentation was part of the Wolverine Island Kickoff Celebrations on October 16, 2008.

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PrimPlotter Gus Rosania, PhD (Caia Alter, SL) Department of Pharmaceutical Sciences University of Michigan College of Pharmacy Ann Arbor, MI 48109

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Please cite the following paper: Toyotaro Suzumura and Koji. Ueno, "ScaleGraph: A high-performance library for billion-scale graph analytics," 2015 IEEE International Conference on Big Data (Big Data), Santa Clara, CA, 2015, pp. 76-84. doi: 10.1109/BigData.2015.7363744 Recently, large-scale graph analytics has become a very popular topic owing to the emergence of gigantic graphs whose number of vertices and edges is in millions, billions or even trillions. Many graph analytics libraries and frameworks have been proposed with various computational models and programming languages to deal with such graphs. X10 programming language is a PGAS language that aims at both software performance and programmer's productivity. We introduce ScaleGraph library developed using X10 programming to illustrate the use of X10 for large-scale graph analytics. ScaleGraph library provides XPregel framework that is inspired by Google's Pregel computation model, serving as a building block for implementing graph kernels. We also optimized X10 runtime in some parts such as collective communication and memory management. We evaluated the performance and scalability of ScaleGraph libraries. The result shows that most graph kernels have good performance and scalability. ScaleGraph library is 9.4 times faster than Giraph in the experiment of PageRank with 16 machine nodes. To the best of our knowledge, ScaleGraph is the first X10-based library to address performance, scalability and productivity issues in dealing with large-scale graph analytics.

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