Apply the sumprod() DoubleLength functions to vector_norm() which is called by both hypot() and dist().
Baseline timing
% ./python.exe -m timeit -r11 -s 'from random import expovariate as r' -s 'from math import hypot' -s 'n=100' -s 'data = [r() for i in range(n)]' 'hypot(*data)'
500000 loops, best of 11: 579 nsec per loop
Timing with PR
% ./python.exe -m timeit -r11 -s 'from random import expovariate as r' -s 'from math import hypot' -s 'n=100' -s 'data = [r() for i in range(n)]' 'hypot(*data)'
500000 loops, best of 11: 476 nsec per loop
Timing with PR if fma() is not used
% ./python.exe -m timeit -r11 -s 'from random import expovariate as r' -s 'from math import hypot' -s 'n=100' -s 'data = [r() for i in range(n)]' 'hypot(*data)'
500000 loops, best of 11: 531 nsec per loop
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Apply the sumprod() DoubleLength functions to vector_norm() which is called by both hypot() and dist().
Baseline timing
Timing with PR
Timing with PR if fma() is not used