A working animal have to attend a domestic university.
Programming with Big Data in R -LRB- pbdR -RRB- is a series of R packages and an environment for statistical computing with Big Data by using high-performance statistical computation .. R ( programming language ). statistical computing. statistical computing. Big Data. Big Data. The pbdR uses the same programming language as R with S3 / S4 classes and methods which is used among statisticians and data miners for developing statistical software .. R ( programming language ). S3 / S4. S ( programming language ). data miners. Data mining. statistical software. statistical software. The significant difference between pbdR and R code is that pbdR mainly focuses on distributed memory systems , where data are distributed across several processors and analyzed in a batch mode , while communications between processors are based on MPI that is easily used in large high-performance computing -LRB- HPC -RRB- systems .. R ( programming language ). MPI. Message Passing Interface. distributed memory. distributed memory. batch mode. Batch processing. R system mainly focuses on single multi-core machines for data analysis via an interactive mode such as GUI interface .. R ( programming language ). multi-core. Multi-core processor. GUI interface. Graphical user interface. Two main implementations in R using MPI are Rmpi and pbdMPI of pbdR .. R ( programming language ). MPI. Message Passing Interface. The pbdR built on pbdMPI uses SPMD parallelism where every processor is considered as worker and owns parts of data .. SPMD. SPMD. SPMD parallelism. SPMD. 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 .. SPMD. SPMD. SPMD parallelism. SPMD. singular value decomposition. singular value decomposition. clustering analysis. Mixture model. On the other hand , there is no restriction to use manager / workers parallelism in SPMD parallelism environment .. SPMD. SPMD. SPMD parallelism. SPMD. manager / workers parallelism. Master / slave ( technology ). The Rmpi uses manager / workers parallelism where one main processor -LRB- manager -RRB- servers as the control of all other processors -LRB- workers -RRB- .. manager / workers parallelism. Master / slave ( technology ). The manager / workers parallelism introduced around early 2000 is particularly efficient for large tasks in small clusters , for example , bootstrap method and Monte Carlo simulation in applied statistics since i.i.d. assumption is commonly used in most statistical analysis .. manager / workers parallelism. Master / slave ( technology ). clusters. Computer cluster. bootstrap method. Bootstrapping ( statistics ). Monte Carlo simulation. Monte Carlo method. statistical analysis. Statistics. 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 .. SPMD. SPMD. SPMD parallelism. SPMD. For example , a modern GPU is a large collection of slower co-processors that can simply apply the same computation on different parts of relatively smaller data , but the SPMD parallelism ends up with an efficient way to obtain final solutions -LRB- i.e. time to solution is shorter -RRB- .. SPMD. SPMD. SPMD parallelism. SPMD. GPU. Graphics processing unit. 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 .. clusters. Computer cluster. Big data. Big data. In short , pbdR. does not like Rmpi , nor parallel packages in R ,. R ( programming language ). does not focus on interactive computing nor master / workers ,. but is able to use both SPMD and task parallelisms .. SPMD. SPMD