<![CDATA[
#### HPSC -- High Performance Statistical Computing for Data Intensive Research
<blockquote class="blockquote-reverse">
<font color="green" size=+1><i><b>
Distributed Read, Compute, Statistics, and Output ...
</i></b></font>
</blockquote>
---
#### What Is This?
This web page introduces a simple computing framework for "Big Data" called
<a href="./spmd.html">single program multiple data (SPMD)</a>, and many
statistical methodology can be fairly easily redesigned in the same way.
We aim to introduce ideas in the sense of
<font color="green">STATISTICS</font>,
and provide
<a href="./cookbook.html">Cookbook</a>
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.
<font color="green"><b>"Have a Big dream of Bigger than Big."</b></font>
---
#### About Computing Environment
By default, all examples of this website are illustrated in the Unix/Linux
system with
<a href="http://r-pbd.org/" target="_blank">pbdMPI</a>.
<code>pbdMPI</code> is mainly developed and tested under
<a href="http://www.open-mpi.org/" target="_blank">OpenMPI</a> in
<a href="http://xubuntu.org" target="_blank">xubuntu</a> system.
Also, all examples are assumed running under the
<a href="./spmd.html">single program multiple data (SPMD)</a>
framework.
For Mac users,
OpenMPI is suggested for <code>pbdMPI</code>.
For MS Windows users,
<a href="http://www.mcs.anl.gov/research/projects/mpich2/"
target="_blank">MPICH2</a>
is suggested and working very well with <code>pbdMPI</code>.
If you don't have many machines/processors,
the easier way you can test and learn is to install
<a href="https://www.virtualbox.org/" target="_blank">VirtualBox</a>
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.
---
#### Authors
<a href="../index.html">Wei-Chen Chen</a> and
<a href="http://www.csm.ornl.gov/~ost/"
target="_blank"> George Ostrouchov</a>.
---
#### Acknowledgment
<a href="../index.html">Wei-Chen</a> thanks
<a href="http://www.csm.ornl.gov/~ost"
target="_blank">Dr. George Ostrouchov</a>
of
<a href="http://www.ornl.gov/"
target="_blank">Oak Ridge National Laboratory</a>
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
<a href="http://science.energy.gov/"
target="_blank">U.S. DOE Office of Sience</a>.
Wei-Chen also thanks
<a href="http://www.stats.uwo.ca/faculty/yu/"
target="_blank">Dr. Hao Yu</a>,
the author of
<a href="http://www.stats.uwo.ca/faculty/yu/Rmpi/"
target="_blank">Rmpi</a>,
for great discussion about Rmpi design and parallel programming in Rmpi.
Also, Wei-Chen thanks
<a href="http://www.oreillynet.com/pub/au/4980"
target="_blank">Stephen Weston</a>,
one author of
<a href="http://shop.oreilly.com/product/0636920021421.do"
target="_blank">Parallel R</a> Data Analysis in the Distributed World,
for sharing MPI and <code>snow</code> information in <code>R</code>.
---
<div w3-include-html="./preamble_tail_date.html"></div>
]]>