GraphLab: A New Framework for Parallel Machine Learning

Details GraphLab: A New Framework for Parallel Machine Learning GraphLab: A New Framework for Parallel Machine Learning

2010-06-25

arxiv.org/abs/1006.4990v1

The 26th Conference on Uncertainty in Artificial Intelligence (UAI 2010), Catalina Island, California, July 8-11, 2010

Computer Science

Learning

Scientific paper

Designing and implementing efficient, provably correct parallel machine learning (ML) algorithms is challenging. Existing high-level parallel abstractions like MapReduce are insufficiently expressive while low-level tools like MPI and Pthreads leave ML experts repeatedly solving the same design challenges. By targeting common patterns in ML, we developed GraphLab, which improves upon abstractions like MapReduce by compactly expressing asynchronous iterative algorithms with sparse computational dependencies while ensuring data consistency and achieving a high degree of parallel performance. We demonstrate the expressiveness of the GraphLab framework by designing and implementing parallel versions of belief propagation, Gibbs sampling, Co-EM, Lasso and Compressed Sensing. We show that using GraphLab we can achieve excellent parallel performance on large scale real-world problems.

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