array_scalar * numpy_scalar is slow.

[charris]

Some timings:

In [8]: a = np.float64(123)

In [9]: b = np.array(4.)

In [10]: timeit a*b
10000000 loops, best of 3: 110 ns per loop

In [11]: timeit b*a
1000000 loops, best of 3: 985 ns per loop

In [12]: timeit a*a
10000000 loops, best of 3: 66.6 ns per loop

In [13]: timeit b*b
1000000 loops, best of 3: 349 ns per loop

In [14]: timeit a + b
10000000 loops, best of 3: 109 ns per loop

In [15]: timeit b + a
1000000 loops, best of 3: 1.01 µs per loop

Maybe a fastpath it needed.

[juliantaylor]

[charris]

Cool! Or maybe, hot stuff ;) I love those graphs but I'm still coming to grips with how to understand them.

[juliantaylor]

it shows the sampled callstack with the width being the cumulative runtime. The main bottlenecks are ufunc_hasattr and GetPriority which have an array fastpath but no scalar one. Next largest fraction are a bunch of subtype check from PyArray_DescrFromScalar