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If numpy+mkl is faster, how much faster is it than numpy?
I found that the numpy+mkl installation package is much larger than numpy, but I can't feel the difference in their speed.
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MKL 2022 is essentially the fastest in all three benchmarks—with a particularly noticable lead in eigenvalue computation—while OpenBLAS is barely competitive with MKL 2019.
In the conda defaults channel, NumPy is built against Intel MKL. MKL is a separate package that will be installed in the users' environment when they install NumPy.
NumPy Arrays are faster than Python Lists because of the following reasons: An array is a collection of homogeneous data-types that are stored in contiguous memory locations. On the other hand, a list in Python is a collection of heterogeneous data types stored in non-contiguous memory locations.
NumPy does not require any external linear algebra libraries to be installed. However, if these are available, NumPy's setup script can detect them and use them for building. A number of different LAPACK library setups can be used, including optimized LAPACK libraries such as OpenBLAS or MKL.
numpy's standard pip packages are linked to OpenBLAS. Mabey OpenBLAS is in the same ballpark for speed as MKL. For a detailed comparison of their speeds, please see Boosting numpy: Why BLAS Matters and Benchmark OpenBLAS, Intel MKL vs ATLAS
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