[Numpy-discussion] Get rid of special scalar arithmetic

[Robert Kern]

On Wed, Jan 13, 2016 at 5:18 AM, Charles R Harris <charlesr.harris at gmail.com>
wrote:
>
> Hi All,
>
> I've opened issue #7002, reproduced below, for discussion.
>>
>> Numpy umath has a file scalarmath.c.src that implements scalar
arithmetic using special functions that are about 10x faster than the
equivalent ufuncs.
>>
>> In [1]: a = np.float64(1)
>>
>> In [2]: timeit a*a
>> 10000000 loops, best of 3: 69.5 ns per loop
>>
>> In [3]: timeit np.multiply(a, a)
>> 1000000 loops, best of 3: 722 ns per loop
>>
>> I contend that in large programs this improvement in execution time is
not worth the complexity and maintenance overhead; it is unlikely that
scalar-scalar arithmetic is a significant part of their execution time.
Therefore I propose to use ufuncs for all of the scalar-scalar arithmetic.
This would also bring the benefits of __numpy_ufunc__ to scalars with
minimal effort.
>
> Thoughts?

Not all important-to-optimize programs are large in our field; interactive
use is rampant. The scalar optimizations weren't added speculatively:
people noticed that their Numeric code ran much slower under numpy and were
reluctant to migrate. I was forever responding on comp.lang.python, "It's
because scalar arithmetic hasn't been optimized yet. We know how to do it,
we just need a volunteer to do the work. Contributions gratefully
accepted!" The most critical areas tended to be optimization where you are
often working with implicit scalars that pop out in the optimization loop.

--
Robert Kern
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