Manipulating single-precision (float) arrays in .Call functions
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. 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 -- View this message in context: http://r.789695.n4.nabble.com/Manipulating-single-precision-float-arrays-in-Call-functions-tp3675684p3677232.html Sent from the R devel mailing list archive at Nabble.com.
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