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In the never ending quest to figure out how to do something useful with never ending streams of data, GraphLab: A New Framework For Parallel Machine Learning wants to go beyond low-level programming, MapReduce, and dataflow languages with a new parallel framework for ML (machine learning) which exploits the sparse structure and common computational patterns of ML algorithms. GraphLab enables ML experts to easily design and implement efficient scalable parallel algorithms by composing problem specific computation, data-dependencies, and scheduling. Our main contributions include:
- A graph-based data model which simultaneously represents data and computational dependencies.
- A set of concurrent access models which provide a range of sequential-consistency guarantees.
- A sophisticated modular scheduling mechanism.
- An aggregation framework to manage global state.
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