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Distributed GraphLab: A Framework for Machine Learning in the Cloud
[article]
2012
arXiv
pre-print
While high-level data parallel frameworks, like MapReduce, simplify the design and implementation of large-scale data processing systems, they do not naturally or efficiently support many important data mining and machine learning algorithms and can lead to inefficient learning systems. To help fill this critical void, we introduced the GraphLab abstraction which naturally expresses asynchronous, dynamic, graph-parallel computation while ensuring data consistency and achieving a high degree of
arXiv:1204.6078v1
fatcat:g3cnoya2qbc7hpeqv4ttmua74e