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Please add a reason or a talk parameter to this template to explain the issue with the article. R packages and an environment for statistical computing with big data by using high-performance statistical computation.
S4 classes and methods which is used among statisticians and data miners for developing statistical software. R system mainly focuses on single multi-core machines for data analysis via an interactive mode such as GUI interface. Two main implementations in R using MPI are Rmpi and pbdMPI of pbdR.
The pbdR built on pbdMPI uses SPMD parallelism where every processor is considered as worker and owns parts of data. The SPMD parallelism introduced in mid 1980 is particularly efficient in homogeneous computing environments for large data, for example, performing singular value decomposition on a large matrix, or performing clustering analysis on high-dimensional large data. 2000 is particularly efficient for large tasks in small clusters, for example, bootstrap method and Monte Carlo simulation in applied statistics since i.
In particular, task pull parallelism has better performance for Rmpi in heterogeneous computing environments. The idea of SPMD parallelism is to let every processor do the same amount of work, but on different parts of a large data set. It is clear that pbdR is not only suitable for small clusters, but is also more stable for analyzing big data and more scalable for supercomputers. Programming with pbdR requires usage of various packages developed by pbdR core team.