GraphLab(2): Machine Learning for Big Data in the Cloud
Carlos Guestrin

Citation
Carlos Guestrin. "GraphLab(2): Machine Learning for Big Data in the Cloud". Tutorial, May, 2013.

Abstract
Today, machine learning (ML) methods play a central role in industry and science. The growth of the Web and improvements in sensor data collection technology have been rapidly increasing the magnitude and complexity of the ML tasks we must solve. This growth is driving the need for scalable, parallel ML algorithms that can handle "Big Data." Unfortunately, implementing efficient parallel ML algorithms is challenging. Existing high-level parallel abstractions such as MapReduce and Pregel are insufficiently expressive to achieve the desired performance, while low-level tools such as MPI are difficult to use, leaving ML experts repeatedly solving the same design challenges. In this talk, I will also describe the GraphLab framework, which naturally expresses asynchronous, dynamic graph computations that are key for state-of-the-art ML algorithms. When these algorithms are expressed in our higher-level abstraction, GraphLab will effectively address many of the underlying parallelism challenges, including data distribution, optimized communication, and guaranteeing sequential consistency, a property that is surprisingly important for many ML algorithms. On a variety of large-scale tasks, GraphLab provides 20-100x performance improvements over Hadoop. In recent months, GraphLab has received thousands of downloads, and is being actively used by a number of startups, companies, research labs and universities.

Electronic downloads

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Citation formats

• HTML

  Carlos Guestrin. <a
  href="http://www.terraswarm.org/pubs/57.html"
  ><i>GraphLab(2): Machine Learning for Big Data in
  the Cloud</i></a>, Tutorial,  May, 2013.

• Plain text

  Carlos Guestrin. "GraphLab(2): Machine Learning for Big
  Data in the Cloud". Tutorial,  May, 2013.

• BibTeX

  @tutorial{Guestrin13_GraphLab2MachineLearningForBigDataInCloud,
      author = {Carlos Guestrin},
      title = {GraphLab(2): Machine Learning for Big Data in the
                Cloud},
      month = {May},
      year = {2013},
      abstract = {Today, machine learning (ML) methods play a
                central role in industry and science. The growth
                of the Web and improvements in sensor data
                collection technology have been rapidly increasing
                the magnitude and complexity of the ML tasks we
                must solve. This growth is driving the need for
                scalable, parallel ML algorithms that can handle
                "Big Data." Unfortunately, implementing efficient
                parallel ML algorithms is challenging. Existing
                high-level parallel abstractions such as MapReduce
                and Pregel are insufficiently expressive to
                achieve the desired performance, while low-level
                tools such as MPI are difficult to use, leaving ML
                experts repeatedly solving the same design
                challenges. In this talk, I will also describe the
                GraphLab framework, which naturally expresses
                asynchronous, dynamic graph computations that are
                key for state-of-the-art ML algorithms. When these
                algorithms are expressed in our higher-level
                abstraction, GraphLab will effectively address
                many of the underlying parallelism challenges,
                including data distribution, optimized
                communication, and guaranteeing sequential
                consistency, a property that is surprisingly
                important for many ML algorithms. On a variety of
                large-scale tasks, GraphLab provides 20-100x
                performance improvements over Hadoop. In recent
                months, GraphLab has received thousands of
                downloads, and is being actively used by a number
                of startups, companies, research labs and
                universities.},
      URL = {http://terraswarm.org/pubs/57.html}
  }
  

Posted by Mila MacBain on 6 May 2013.