RABID: A Distributed Parallel R for Large Datasets
Hao Lin, Shuo Yang, Samuel Pratt Midkiff · 2014
Large-scale data mining and deep data analysis are increasingly important for both enterprise and scientific applications. Statistical languages provide rich functionality and ease of use for data analysis and modeling and have a large user base. R is one of the most widely used of these languages, but is limited to a single threaded execution model and problem sizes that fit in a single node. This paper describes highly parallel R system called RABID (R Analytics for BIg Data) that maintains R compatibility, leverages the MapReducelike distributed Spark and achieves high performance and scaling across clusters. Our experimental evaluation shows that RABID performs up to 5x faster than Hadoop and 20x faster than RHIPE on two data mining applications.