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Short introduction to ML frameworks on Hadoop


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Short introduction to ML frameworks on Hadoop

  1. 1. Short introduction to ML frameworks on Hadoop Yuya Takashina 2016 1
  2. 2. Hadoop(2011-) • De facto standard for storage distribution and parallel processing on big data in application. • Google, Yahoo, Facebook, IBM, Twitter, … • The largest Hadoop cluster in the world has 4,500 nodes (Yahoo) • Consists of two parts. • Hadoop Distributed File System • MapReduce • There are some replacements for MapReduce. 2 Barrier
  3. 3. Spark(2014-) • Framework for data analytics on Hadoop. • Use memory to cache data. • Up to 10x faster than MapReduce for certain applications. • Machine learning • Graph computation • Stream processing • API for Scala/Java/Python/R. 3
  4. 4. Petuum(2015-) • Framework for machine learning on Hadoop. • Faster than Spark • Barrier synchronization as bottleneck in MapReduce and Spark. • Adopt P2P and async-like communication strategy to reduce network communication costs. • Guarantee the theoretical convergence to the optimal value using the unique characters of ML programs. • optimization-centric • iterative convergent • Implemented in C++. • Providing Deep learning API. 4
  5. 5. Reference • Powered by Apache Hadoop: • The Hadoop Ecosystem Table: • A New Look at the System, Algorithm and Theory Foundations of Distributed Machine Learning: • Strategies and Principles of Distributed Machine Learning on Big Data: 5