Performance implementing large array operations using NumPy 1.24.1
I'm relatively new to this, so bear with me. I'm currently working with NumPy 1.24.1 and working with important performance optimization when performing operations on very large arrays. I have a use case where I need to apply a function over two large arrays (each about 10 million elements) to compute the element-wise maximum while also applying a threshold. Here’s the relevant part of my code: ```python import numpy as np array1 = np.random.rand(10000000) * 100 array2 = np.random.rand(10000000) * 100 threshold = 50 # Using np.maximum and then applying the threshold result = np.where(np.maximum(array1, array2) > threshold, np.maximum(array1, array2), threshold) ``` I've noticed that this operation takes a considerable amount of time to execute. I tried using `np.maximum()` directly and then applying the threshold, but the performance is still suboptimal. I also attempted to utilize NumPy’s `vectorize` function in an attempt to speed things up: ```python max_with_threshold = np.vectorize(lambda x: x if x > threshold else threshold) result = max_with_threshold(np.maximum(array1, array2)) ``` However, this made it even slower. My question is whether there are more efficient ways to handle this type of operation, especially when dealing with such large arrays. Are there any best practices or optimizations I might be overlooking? Any help would be greatly appreciated! I'm working with Python in a Docker container on Windows 11. Am I missing something obvious?