Manipulating single-precision (float) arrays in .Call functions

"Prof Brian Ripley"<ripley at stats.ox.ac.uk>  wrote in message
news:alpine.LFD.2.02.1107190640280.28269 at gannet.stats.ox.ac.uk...

On Mon, 18 Jul 2011, Alireza Mahani wrote:

Simon,

Thank you for elaborating on the limitations of R in handling float
types. I
think I'm pretty much there with you.

As for the insufficiency of single-precision math (and hence limitations
of
GPU), my personal take so far has been that double-precision becomes
crucial
when some sort of error accumulation occurs. For example, in differential
equations where boundary values are integrated to arrive at interior
values,
etc. On the other hand, in my personal line of work (Hierarchical
Bayesian
models for quantitative marketing), we have so much inherent uncertainty
and
noise at so many levels in the problem (and no significant error
accumulation sources) that single vs double precision issue is often
inconsequential for us. So I think it really depends on the field as well
as
the nature of the problem.

The main reason to use only double precision in R was that on modern CPUs
double precision calculations are as fast as single-precision ones, and
with 64-bit CPUs they are a single access.
So the extra precision comes more-or-less for free.

But, isn't it much more of the 'less free' when large data sets are
considered? If a double matrix takes 3GB, it's 1.5GB in single.
That might alleviate the dreaded out-of-memory error for some
users in some circumstances. On 64bit, 50GB reduces to 25GB
and that might make the difference between getting
something done, or not. If single were appropriate, of course.
For GPU too, i/o often dominates iiuc.

For space reasons, is there any possibility of R supporting single
precision (and single bit logical to reduce memory for logicals by
32 times)? I guess there might be complaints from users using
single inappropriately (or worse, not realising we have an instable
result due to single).

Matthew

You also under-estimate the extent to which stability of commonly used
algorithms relies on double precision.  (There are stable single-precision
versions, but they are no longer commonly used.  And as Simon said, in
some cases stability is ensured by using extra precision where available.)

I disagree slightly with Simon on GPUs: I am told by local experts that
the double-precision on the latest GPUs (those from the last year or so)
is perfectly usable.  See the performance claims on
http://en.wikipedia.org/wiki/Nvidia_Tesla of about 50% of the SP
performance in DP.

Regards,
Alireza


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Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
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