On Fri, Oct 29, 2021 at 6:10 AM Skip Montanaro <skip.montan...@gmail.com>
wrote:
> 1. I use numpy arrays filled with random values, and the output array is
> also a numpy array. The vector multiplication is done in a simple for loop
> in my vecmul() function.
>
probably doesn't make a difference for this exercise, but numpy arrays make
lousy replacements for a regular list -- i.e. as a container alone. The
issue is that floats need to be "boxed" and "unboxed" as you put them in
and pull them out of an array. whereas with lists, they float objects
themselves are already there.
OK, maybe not as bad as I remember. but not great:
In [61]: def multiply(vect, scalar, out):
...: """
...: multiply all the elements in vect by a scalar in place
...: """
...: for i, val in enumerate(vect):
...: out[i] = val * scalar
...:
In [62]: arr = np.random.random((100000,))
In [63]: arrout = np.zeros_like(arr)
In [64]: l = list(arr)
In [65]: lout = [None] * len(l)
In [66]: %timeit multiply(arr, 1.1, arrout)
19.3 ms ± 125 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
In [67]: %timeit multiply(l, 1.1, lout)
12.8 ms ± 83.9 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
> That said, I have now run my example code using both PYTHONGIL=0 and
PYTHONGIL=1 of Sam's nogil branch as well as the following other Python3
versions:
* Conda Python3 (3.9.7)
* /usr/bin/python3 (3.9.1 in my case)
* 3.9 branch tip (3.9.7+)
The results were confusing, so I dredged up a copy of pystone to make sure
I wasn't missing anything w.r.t. basic execution performance. I'm still
confused, so will keep digging.
I'll be interested to see what you find out :-)
It would also be fun to see David Beezley’s example from his seminal talk:
>
> https://youtu.be/ph374fJqFPE
>
Thanks, I'll take a look when I get a chance
That may not be the best source of the talk -- just the one I found first
:-)
-CHB
--
Christopher Barker, PhD (Chris)
Python Language Consulting
- Teaching
- Scientific Software Development
- Desktop GUI and Web Development
- wxPython, numpy, scipy, Cython
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