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Two years ago we introduced GraphLab to address the critical need for a high-level abstraction for large-scale graph structured computation in machine learning. Since then, we have implemented the abstraction on multicore and cloud systems, evaluated its performance on a wide range of applications, developed new ML algorithms, and fostered a growing community of users. Along the way, we have identified new challenges to the abstraction, our implementation, and the important task of fostering a community around a research project. However, one of the most interesting and important challenges we have encountered is large-scale distributed computation on natural power law graphs. To address the unique challenges posed by natural graphs, we introduce GraphLab 2, a fundamental redesign of the GraphLab abstraction which provides a much richer computational framework. In this talk, we will describe the GraphLab 2 abstraction in the context of recent progress in graph computation frameworks (e.g., Pregel/Giraph). We will review some of the special challenges associated with distributed computation on large natural graphs and demonstrate how GraphLab 2 addresses these challenges. Finally, we will conclude with some preliminary results from GraphLab 2 as well as a live demo. This talk represents joint work with Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Alex Smola, and Joseph Hellerstein.
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