GraphLab

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Turi
DeveloperCarnegie Mellon University
Stable release
v2.2 / July 1, 2013 (2013-07-01)
Repository
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Written inC++
Engine
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    Operating systemLinux, macOS
    TypeMachine learning platform
    LicenseProprietary
    Websiteturi.com

    Turi is a graph-based, high performance, distributed computation framework written in C++. The GraphLab project was started by Prof. Carlos Guestrin of Carnegie Mellon University in 2009. It is an open source project that uses the Apache License. While GraphLab was originally developed for machine learning tasks, it has also been developed for other data-mining tasks.[1][2]

    Motivation

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    As the amounts of collected data and computing power grow (multicore, GPUs, clusters, clouds), modern datasets no longer fit into one computing node. Efficient distributed parallel algorithms for handling large-scale data are required. The GraphLab framework is a parallel programming abstraction targeted for sparse iterative graph algorithms. GraphLab provides a programming interface, allowing deployment of distributed machine learning algorithms.[3] The main design considerations behind the design of GraphLab are:

    • Sparse data with local dependencies
    • Iterative algorithms
    • Potentially asynchronous execution

    GraphLab toolkits

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    On top of GraphLab, several implemented libraries of algorithms:

    Turi (formerly called Dato and before that GraphLab Inc.) is a company that was founded by Prof. Carlos Guestrin from University of Washington in May 2013 to continue development support of the GraphLab open source project. Dato Inc. raised a $6.75M Series A from Madrona Venture Group and New Enterprise Associates (NEA). They raised a $18.5M Series B from Vulcan Capital and Opus Capital, with participation from Madrona and NEA.[10] On August 5, 2016, Turi was acquired by Apple Inc. for $200,000,000.[11][12]

    References

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    1. ^ Joseph Gonzalez, Yucheng Low, Haijie Gu, Danny Bickson, Carlos Guestrin (2012). "PowerGraph: Distributed Graph-Parallel Computation on Natural Graphs." Proceedings of Operating Systems Design and Implementation (OSDI).
    2. ^ Yucheng Low, Joseph Gonzalez, Aapo Kyrola, Danny Bickson, Carlos Guestrin and Joseph M. Hellerstein (2012). "Distributed GraphLab: A Framework for Machine Learning and Data Mining in the Cloud." Proceedings of Very Large Data Bases (PVLDB).
    3. ^ Y. Low, J. Gonzalez, A. Kyrola, D. Bickson, C. Guestrin and J. Hellerstein. GraphLab: A New Framework for Parallel Machine Learning. In the 26th Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, USA, 2010
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    [edit | edit source]