R help - Feb 2007 - how much performance penalty does this incur, scalar as a vector of one element?

I have been comparing R with other languages and systems. One peculiar feature
of R is there is no scalar. Instead, it is just a vector of length one. I
wondered how much performance penalty this deign cause, particular in situations
with many scalars in a program. Thanks.

 

Jason Liao, http://www.geocities.com/jg_liao    
Associate Professor of Biostatistics
Drexel University School of Public Health
245 N. 15th Street, Mail Stop 660
Philadelphia, PA 19102-1192
phone 215-762-3934




 
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I think the short answer is not much.

Longer answer: In an interpreted framework with double precision
floating point scalars there is little chance of avoiding fresh
allocations for each scalar; given that, the overhead associated with
length checks can be made negligible.  (That isn't to say it currently
is--it may or may not be, but you asked about design.)  Systems that
support integer scalars often represent them as immediate values within
pointers by sacrificing one or two bits of precision in the integers,
but that doesn't work for double precision floats except possibly on
64-bit systems.  Though even there it would be possible to use an
efficient internal representation of vectors of length one without
changing the concept that everything is a vector.

As we think about compilation there are opportunities to produce more
efficient code if values can be assumed to be scalars, but that can be
accomplished by adding a declaration mechanism.  So again the answer
in terms of efficiency cost is not much.

The APL view of everything as an array, with zero-dimensional arrays
being scalars and higher-dimensional arrays being real entities rather
than decorated vectors, is in many ways conceptually cleaner and might
in hindsight have been a better choice for that reason, but efficiency
isn't really a consideration.

Best,

luke

On Wed, 21 Feb 2007, Jason Liao wrote:
>
> I have been comparing R with other languages and systems. One peculiar
feature of R is there is no scalar. Instead, it is just a vector of length one.
I wondered how much performance penalty this deign cause, particular in
situations with many scalars in a program. Thanks.
>
>
>
> Jason Liao, http://www.geocities.com/jg_liao
> Associate Professor of Biostatistics
> Drexel University School of Public Health
> 245 N. 15th Street, Mail Stop 660
> Philadelphia, PA 19102-1192
> phone 215-762-3934
>
>
>
>
>
>
____________________________________________________________________________________
> TV dinner still cooling?
> Check out "Tonight's Picks" on Yahoo! TV.
>
> ______________________________________________
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> PLEASE do read the posting guide
http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
>
-- 
Luke Tierney
Chair, Statistics and Actuarial Science
Ralph E. Wareham Professor of Mathematical Sciences
University of Iowa                  Phone:             319-335-3386
Department of Statistics and        Fax:               319-335-3017
    Actuarial Science
241 Schaeffer Hall                  email:      luke at stat.uiowa.edu
Iowa City, IA 52242                 WWW:  http://www.stat.uiowa.edu

Dear Prof.  Tierney, thank you very much to answer my question. It is good to
know that the loss of efficiency can be small.

I came to this question after using R to implement a few low level algorithm:
KD-tree and recursive algorithm for conditional Poisson binomial. The R's
speed has been slow and even much slower than Ruby.

I love R dearly and always tell my students that it is the best thing that ever
happened to statistics. R is much more elegant than C or Fortran. Unfortunately
Fortran or C is still needed when speed is a concern and a statistician has then
to confront the ugly and complex large world. A huge gain in productivity and
reduction in mental anguish can be achieved If R's speed can be improved via
compilation.

I did a little research. The following tool claims to make Python as fast as C

http://www-128.ibm.com/developerworks/linux/library/l-psyco.html

Recently, a new Ruby implementation makes it several times faster:

http://www.antoniocangiano.com/articles/2007/02/19/ruby-implementations-shootout-ruby-vs-yarv-vs-jruby-vs-gardens-point-ruby-net-vs-rubinius-vs-cardinal
 
Jason Liao, http://www.geocities.com/jg_liao    
Associate Professor of Biostatistics
Drexel University School of Public Health
245 N. 15th Street, Mail Stop 660
Philadelphia, PA 19102-1192
phone 215-762-3934





 
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