|HPSC -- High Performance Statistical Computing for Data Intensive Research|
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Section: About HPSC
``Distributed Read, Compute, Statistics, and Output ...''
This web page introduces a simple computing framework for "Big Data" called
single program multiple data (SPMD), and many
statistical methodology can be fairly easily redesigned in the same way.
We aim to introduce ideas in the sense of
Cookbook to illustrate the framework covering from
fundamental statistics to advance methodology.
Tentatively, the pages will cover basic ideas of parallel computing,
statistical computing, and R programming, and they will be illustrated
in a simple manner.
"Have a Big dream of Bigger than Big."
By default, all examples of this website are illustrated in the Unix/Linux
For Mac users,
OpenMPI is suggested for
If you don't have many machines/processors,
the easier way you can test and learn is to install
with Unix/Linux system. The VirtualBox allows to generate simultaneously
multiple virtual computers in most common systems.
You can duplicate the virtual machines/processors
inside VirtualBox as many as you want.
Therefore, a parallel
computing environment can be done in a single machine.
Regardless of computing performance,
it is helpful for testing programs and for building projects in
a consistent environment.
When mentioning the HPSC, please cite:
Wei-Chen thanks Dr. George Ostrouchov of Oak Ridge National Laboratory for helpful discussion, and provide insightful suggestions and materials about general parallel computing. The contents are outcomes part of the project "Visual Data Exploration and Analysis of Ultra-large Climate Data" supported by U.S. DOE Office of Sience.
Wei-Chen also thanks
Dr. Hao Yu,
the author of
for great discussion about Rmpi design and parallel programming in Rmpi.
Also, Wei-Chen thanks
one author of
Parallel R Data Analysis in the Distributed World,
for sharing MPI and
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Created: Oct 19 2011 |
Last Revised: Aug 14 2013, 08:47 (CDT Ames, IA, USA)
Maintained: Wei-Chen Chen
E-Mail: wccsnow @ gmail.com
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