Performance guide with np.sum on large arrays with dtype='float32' versus dtype='float64'
I'm working on a project where I'm analyzing large datasets using NumPy, and I've encountered a important performance scenario with the `np.sum` function... I expected that using `float32` would be faster and use less memory compared to `float64`, but it seems that the opposite is true in my case. When I run the following code: ```python import numpy as np size = 10**7 arr_float32 = np.random.rand(size).astype(np.float32) arr_float64 = np.random.rand(size).astype(np.float64) %timeit np.sum(arr_float32) %timeit np.sum(arr_float64) ``` I get this output: ``` 1.23 ms ± 10.5 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each) 2.55 ms ± 53.5 µs per loop (mean ± std. dev. of 7 runs, 100 loops each) ``` From this, it seems like the `float32` array is taking longer to sum than the `float64` array. I understand that `float32` is generally faster due to its smaller size, but in this case, it's behaving contrary to my expectations. I've tried updating to NumPy version 1.23 hoping for improvements, but the scenario continues. Is there something specific I might be missing with the way `np.sum` is optimized for different dtypes? Could there be a specific configuration or setting in NumPy that influences this performance? Any insights or suggestions on how to improve the performance would be greatly appreciated! The project is a web app built with Python. I'm open to any suggestions.