[R] recommended computing server for R (March 2009)?
bates at stat.wisc.edu
Sat Mar 28 16:59:46 CET 2009
On Sat, Mar 28, 2009 at 9:55 AM, Dirk Eddelbuettel <edd at debian.org> wrote:
> On 28 March 2009 at 14:36, ivowel at gmail.com wrote:
> | thanks, dirk. I just read your tutorial. great information for our needs.
> My pleasure.
> | alas, the Amazon economics do not work well for us. the server that I am
> | planning to purchase should cost around $800 and is the equivalent of the
> | high-intensive CPU, which goes for $0.80/hour. that's about 2 months of
> | amazon server time for the same price. if administering the hardware is
> | very costly, then amazon is cost-effective. fortunately, we believe we can
> | run the hardware easily ourselves.
> A steady supply of grad students can do that do a project, I suppose.
> | I wonder how long it will take before debian will offer a GUI program that
> | allow users like us to "rent out" a server for cash, and credit them via
> | paypal. alas, maybe a special R cloud distribution (that has "everything R"
> | already installed, too) could support the R project itself?! I would donate
> | our free CPU time to the R project when the CPU is not otherwise used.
> | probably some others would do the same, too.
> Debian never will as it is strictly a non-profit. Canonical (Ubuntu's paren)
> might -- the next Ubuntu release will already contain what is said to be an
> 'amazon-ec2-compatible' "build you own cloud" system based on the Eucalyptus
> system from UCSB: http://eucalyptus.cs.ucsb.edu/
> A PS to your original question: IIRC you can also buy systems from Dell and
> HP with Ubuntu pre-installed. Ubuntu gives you Atlas, you can try to add the
> non-free Goto blas, or the commerical MKL blas, or ... to further speed up
> your inner linear models (now that you learned about lm.fit() et al).
Well, there is a bit of a sad story there. Accelerated BLAS are most
effective in speeding up numerical linear algebra when the level-3
BLAS are used. (Level-1 BLAS are vector-vector operations, level-2 are
matrix-vector and level-3 are matrix-matrix operations) Lapack is
based on level-3 BLAS whereas Linpack is based on level-1 BLAS.
Almost all numerical linear algebra in R uses Lapack. The one
exception is - wait for it - the QR decomposition used in ls.fit,
because of the choice of pivoting schemes. It is a long story but
when fitting a linear model you want to detect near-singularity in the
model matrix and move the offending columns to be the last columns in
the matrix but otherwise retain the original order. That is, you
don't want to scramble columns corresponding to different terms in the
model. Neither Linpack nor Lapack offered that type of pivoting but
it was retrofitted onto the dqrdc subroutine from Linpack. (Notice
that the default path in qr.default calls a Fortran subroutine called
You could use the LAPACK = TRUE argument to R's qr function to get the
unconstrained pivoting scheme and use that to get coefficient
estimates according to the estimated rank of the model matrix (see
example(qr)) but that won't give you the information needed for the
analysis of variance decompositions.
qr(hilbert(20), LAPACK = TRUE)$pivot
> The rest of the discussion, incl the hardware and esp administration aspect,
> may be more appropriate for r-sig-hpc (subscription needed for posting...)
> Three out of two people have difficulties with fractions.
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