Presto: distributed machine learning and graph processing with sparse matrices
Proceedings of the 8th ACM European Conference on Computer Systems
Parallel, distributed, and differential processing system for human activity sensing flows
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
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Its tough to argue with R as a high-quality, cross-platform, open source statistical software productunless youre in the business of crunching Big Data. This concise book introduces you to several strategies for using R to analyze large datasets. Youll learn the basics of Snow, Multicore, Parallel, and some Hadoop-related tools, including how to find them, how to use them, when they work well, and when they dont. With these packages, you can overcome Rs single-threaded nature by spreading work across multiple CPUs, or offloading work to multiple machines to address Rs memory barrier.Snow: works well in a traditional cluster environment Multicore: popular for multiprocessor and multicore computers Parallel: part of the upcoming R 2.14.0 release R+Hadoop: provides low-level access to a popular form of cluster computing RHIPE: uses Hadoops power with Rs language and interactive shell Segue: lets you use Elastic MapReduce as a backend for lapply-style operations