On Jul 18, 2011, at 6:15 PM, Alireza Mahani wrote:
> Duncan,
>
> Thank you for your reply. This is a rather unfortunate limitation, because
> for large data sizes there is a significant difference between the
> performance of '.C' and '.Call'.
I think you may have missed the main point - R does NOT have any objects that
are in "float" (single precision) representations because that is insufficient
precision, so one way or another you'll have to do something like
SEXP foo(SEXP bar) {
double *d = REAL(bar);
int i, n = LENGTH(bar);
float *f = (float*) R_alloc(sizeof(float), n);
for (i = 0; i < n; i++) f[i] = d[i];
// continue with floats ..
There is simply no other way as, again, there are no floats in R anywhere. This
has nothing to do with .C/.Call, either way floats will need to be created from
the input vectors.
If you make up your own stuff, you could use raw vectors to store floats if
only your functions work on it (or external pointers if you want to keep track
of the memory yourself).
> I will have to do some tests to see what
> sort of penalty I incur for copying from double to float inside my C++ code
> (so I can use '.Call'). I won't be able to use memset(); rather, I have to
> have an explicit loop and use casting. Is there a more efficient option?
>
I'm not aware of any, if you use floats, you incur a penalty regardless (one
for additional storage, another for converting).
One of the reasons that GPU processing has not caught much traction in stat
computing is because it's (practically) limited to single precision
computations (and we even need quad precision occasionally). Although some GPUs
support double precision, they are still not fast enough to be a real threat to
CPUs. (I'm talking about generic usability - for very specialized tasks GPU can
deliver big speedups, but those require low-level exploitation of the
architecture).
Cheers,
Simon
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