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NIPS 2011 Big Learning - Algorithms, Systems, & Tools Workshop: Graphlab 2...

Big Learning Workshop: Algorithms, Systems, and Tools for Learning at Scale at NIPS 2011 Invited Talk: Graphlab 2: The Challenges of Large Scale Computation on Natural Graphs by Carlos Guestrin Carlos Guestrin is an Assistant Professor at Carnegie Mellon's Computer Science and Machine Learning Departments. Carlos has conducted research in (1) learning and control in large-scale structured environments; (2) distributed multiagent coordination; (3) robust, efficient and resource-aware algorithms for sensor networks; (4) sensor placement and tasking; (5) query specific probabilistic modeling. Abstract: 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.
Length: 49:03

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