Content deleted Content added
This will be more notability to this topics in next 10 years |
No edit summary |
||
Line 18:
* The pbdR built on [[pbdMPI]] uses [[SPMD|SPMD Parallelism]] where every processors are considered as workers and own parts of data. This parallelism is particularly for large data, for example, performing [[Singular value decomposition|singular value decomposition]] on a large matrix, or performing [[Mixture model|clustering analysis]] on high-dimensional large data. On the other hand, there is no restriction to use [[Master/slave (technology)|Manager/Workers Parallelism]] in [[SPMD|SPMD Parallelism]] environment.
* The [[Rmpi]]<ref name=rmpi/> uses [[Master/slave (technology)|Manager/Workers Parallelism]] where one main processor (manager) servers as the control of all other processors (workers). This parallelism is particularly efficient for large tasks in small [[Computer cluster|clusters]], for example, [[Bootstrapping (statistics)|bootstrap method]] and [[Monte Carlo method|Monte Carlo simulation]] in applied statistics since [[Independent and identically distributed random variables|i.i.d.]] assumption is commonly used in most [[Statistics|statistical analysis]].
It is clearly that pbdR is suitable for small [[Computer cluster|clusters]], but is stabler for analyzing larger data and is more scalable for [[Supercomputer|supercomputers]].<ref>{{cite journal|author=Schmidt, D., Ostrouchov, G., Chen, W.-C., and Patel, P.|title=Tight Coupling of R and Distributed Linear Algebra for High-Level Programming with Big Data|year=2012|pages=811-815|journal=High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion:}}</ref>
== Package design ==
| |||