2021年2月13日 · Isn't the answer to How to use np.vectorize? usually "Don't. It just pretends to be a vectorized function but is just a loop with a different name"?

2009年10月4日 · Is it a good idea to vectorize the code? What are good practices in terms of when to do it? What happens underneath?

2016年3月10日 · I have noticed that the gcc flag -ftree-vectorize is very useful for optimizing code. I am trying to understand better how it works, but the doc is fairly concise: Perform vectorization on trees...

2009年9月14日 · Many CPUs have "vector" or "SIMD" instruction sets which apply the same operation simultaneously to two, four, or more pieces of data. Modern x86 chips have the SSE instructions, …

The purpose of np.vectorize is to transform functions which are not numpy-aware (e.g. take floats as input and return floats as output) into functions that can operate on (and return) numpy arrays.

2016年2月5日 · What is the most efficient way to map a function over a numpy array? I am currently doing: import numpy as np x = np.array ( [1, 2, 3, 4, 5]) # Obtain array of square ...

2018年10月6日 · I am using Pandas dataframes and want to create a new column as a function of existing columns. I have not seen a good discussion of the speed difference between df.apply() and …

2017年9月13日 · As the title, I'd like to know how to define a vectorized function in R. Is it just by using a loop in the function? Is this method efficient? And what's the best practice ?

2024年7月19日 · Import and vectorize data in Azure only supports text-embedding-3-small if you create that model in Azure Open AI. If you are creating the embedding model in Azure Foundry (studio) you …

2009年1月3日 · The v4 series of the gcc compiler can automatically vectorize loops using the SIMD processor on some modern CPUs, such as the AMD Athlon or Intel Pentium/Core chips. How is this …