Hi All, I've opened issue #7002 <https://github.com/numpy/numpy/issues/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? Chuck
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