Isn't the better abstraction here to use a higher level library in the style of pandas/polars that will operate as vectors, compose and feel readable and inuitive, while (almost?) maxing out SIMD?
NumPy-like:
mean = np.mean(array)
maximum = np.max(np.abs(array)
return mean / maximum
Highway:
HWY_FULL(float) d;
using V = decltype(hn::Zero(d));
V sum = hn::Zero(d);
V max = hn::Zero(d);
hn::Foreach(d, values, N, hn::Zero(d), [&](auto d, auto v) HWY_ATTR {
sum = hn::Add(sum, v);
max = hn::Max(max, hn::Abs(v));
});
const float mean = hn::ReduceSum(d, sum) / N;
return mean / hn::ReduceMax(d, max);
When the actual computation is made so much faster by SIMD, memory bandwidth starts to be a significant bottleneck.