On Mon, Jun 1, 2009 at 10:22 AM, Francesc Alted <fal...@pytables.org> wrote:
> Hi,
>
> In the process of adding single precision support to Numexpr, I'm
> experimenting a divergence between Numexpr and NumPy computations. It all
> boils down to the fact that my implementation defined single precision
> functions completely. As for one, consider my version of expm1f:
>
> inline static float expm1f(float x)
> {
> float u = expf(x);
> if (u == 1.0) {
> return x;
> } else if (u-1.0 == -1.0) {
> return -1;
> } else {
> return (u-1.0) * x/logf(u);
> }
> }
>
> while NumPy seems to declare expm1f as:
>
> static float expm1f(float x)
> {
> return (float) expm1((double)x);
> }
>
> This leads to different results on Windows when computing expm1(x) for
> large
> values of x (like 99.), where my approach returns a 'nan', while NumPy
> returns
> an 'inf'. Curiously, on Linux both approaches returns 'inf'.
>
> I suppose that the NumPy crew already experimented this divergence and
> finally
> used the cast approach for computing the single precision functions.
It was inherited and was no doubt the simplest approach at the time. It has
always bothered me a bit, however, and if you have good single/long double
routines we should look at including them. It will affect the build so David
needs to weigh in here.
However,
> this is effectively preventing the use of optimized functions for single
> precision (i.e. double precision 'exp' and 'log' are used instead of single
> precision specific 'expf' and 'logf'), which could perform potentially
> better.
That depends on the architecture and how fast single vs double computations
are. I don't know how the timings compare on current machines.
>
> So, I'm wondering if it would not be better to use a native implementation
> instead. Thoughts?
>
Some benchmarks would be interesting. Could this be part of the corepy GSOC
project?
Chuck
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