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GraphLab 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 using an Apache License. While GraphLab was originally developed for Machine Learning tasks, it has found great success at a broad range of other data-mining tasks; out-performing other abstractions by orders of magnitude.〔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).〕〔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).〕 == Motivation == As the amounts of collected data and computing power grows (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 high level programming interface, allowing a rapid deployment of distributed machine learning algorithms.〔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〕 The main design considerations behind the design of GraphLab are: * Sparse data with local dependencies * Iterative algorithms * Potentially asynchronous execution Main features of GraphLab are: * A unified multicore and distributed API: write once run efficiently in both shared and distributed memory systems * Tuned for performance: optimized C++ execution engine leverages extensive multi-threading and asynchronous IO * Scalable: GraphLab intelligently places data and computation using sophisticated new algorithms * HDFS Integration * Powerful Machine Learning Toolkits 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「GraphLab」の詳細全文を読む スポンサード リンク
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