Feb 13, 2021 · 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"?

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.

Sep 14, 2009 · 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, …

Dec 23, 2022 · SIMD, Vectorize. is enabled by -o2, loop unrolling makes the code much larger so it is a separate flag in -o2 and included in -o3. many times -o2 will out-perform -o3.

Oct 6, 2018 · 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 …

Feb 18, 2025 · 2 numpy.vectorize conveniently converts a scalar function to vectorized functions that can be applied directly to arrays. However, when inputting a single value into the vectorized function, …

Mar 10, 2016 · 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...

Feb 27, 2024 · As title My setup is as follows: I select "Import and vectorize data" on the Azure AI Search Portal and I get an index with vector values. I am used to using python for Azure AI Search.

Apr 4, 2020 · 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 …

Jul 8, 2018 · You could actually get decent auto vectorization with -ftree-vectorize combined with -O1, for example: Godbolt. With -O0, however, vectorized code won't be generated, even for very simple …