[Rd] Snow or alternative MPI packages on Windows

[Jay Emerson]

Giuseppe,

I've been able to use NetWorkSpaces on all platforms, and have used
snow under Linux.  It's possible to use snow under windows, but you'll
need to build the binary yourself I think.  As an aside, I think this
makes it impossible to write a CRAN-friendly package that Requires: or
Suggests: snow, because it will fail the windows check.  It would be
great if we could get around this some way, but I know this topic has
been covered before (the subtle nature of Suggests: in particular, and
what it really means).  I would like to add SNOW support to two of my
own packages, bcp and bigmemory.

SNOW and NWS are similarly easy to use, at least at the superficial
level which has been my experience.

With NWS, you need both the package (nws, available on CRAN), and a
NWS server (open-source, available on sourceforge).  But REvolution
has a Windows installer available, and are working to make the whole
process easier on the end user/developer on all platforms.  I started
a rough page to help in the interim, and will update it as thing
change to assist the end user/developer:

http://www.stat.yale.edu/~jay/nws/

Jay


<< original message below >>

Guys,

I'm running R on both Windows & Linux. I'm looking at a number of packages
for parallel execution. It seems that the most used packages are "snow" and
"Rmpi".

snow seems more user friendly, but it doesn't run on windows. I see from
searching the mailing list that I'm not the first one to try it on Windows.
There was a message that kind of shed some hope on the subject, but nothing
else.

Rmpi works well on windows (with DeinoMPI) but it's kind of low level, so
before I embark in writing code similar to snow to have some high level
constructs I though I'd ask here about other peoples experiences...

Is anyone around here doing parallel R on Windows? If so could you share
your experience?

// Giuseppe

-- 
John W. Emerson (Jay)
Assistant Professor of Statistics
Department of Statistics
Yale University
http://www.stat.yale.edu/~jay
Statistical Advisor, REvolution Computing