👀 Views: 32 💬 Answers: 1 📅 Created: 2025-06-16

numpy performance optimization Python

I recently switched to I've been researching this but I'm currently working with large NumPy arrays and I've noticed that using `np.sum` with the `axis` parameter seems to perform poorly under certain conditions. Specifically, when I sum along axis 1 of a 2D array that's 1 million by 1 million, I noticed that it takes significantly longer than expected. Here’s a sample of what I’ve tried: ```python import numpy as np # Create a large random array arr = np.random.rand(1000000, 1000000) # Attempt to sum along axis 1 total = np.sum(arr, axis=1) ``` When running this code, it takes several minutes to complete, which feels excessive given that, in theory, summing along an axis should be a straightforward operation. I also tried the following alternatives to see if there was any difference: ```python # Using a for loop to explicitly sum along the axis sums = np.zeros(arr.shape[0]) for i in range(arr.shape[0]): sums[i] = np.sum(arr[i, :]) ``` This loop method also takes a considerable amount of time, and I’m worried that the performance scenario is related to the size of the array or how NumPy handles memory. Is there a more efficient way to compute sums along an axis for such large arrays? Are there any known optimizations or best practices for this specific scenario? Also, is there a way to leverage any NumPy settings to enhance performance? I've looked into using `np.einsum` but I'm not sure if that would yield better performance and how to implement it correctly in this case. Any help would be appreciated! What am I doing wrong? This is happening in both development and production on Windows 11. Any examples would be super helpful. This is for a service running on Ubuntu 20.04.