GitHub - IntelPython/mkl_fft: NumPy-based Python interface to Intel® OneAPI Math Kernel Library (OneMKL) FFT functionality (original) (raw)

mkl_fft -- a NumPy-based Python interface to Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality

mkl_fft started as a part of Intel® Distribution for Python* optimizations to NumPy, and is now being released as a stand-alone package. It can be installed into conda environment from Intel's channel using:

   conda install -c https://software.repos.intel.com/python/conda mkl_fft

or from conda-forge channel:

   conda install -c conda-forge mkl_fft

To install mkl_fft PyPI package please use following command:

   python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft

If command above installs NumPy package from the PyPI, please use following command to install Intel optimized NumPy wheel package from Intel PyPI Cloud:

   python -m pip install --index-url https://software.repos.intel.com/python/pypi --extra-index-url https://pypi.org/simple mkl_fft numpy==<numpy_version>

Where <numpy_version> should be the latest version from https://software.repos.intel.com/python/conda/

Since MKL FFT supports performing discrete Fourier transforms over non-contiguously laid out arrays, OneMKL can be directly used on any well-behaved floating point array with no internal overlaps for both in-place and not in-place transforms of arrays in single and double floating point precision.

This eliminates the need to copy input array contiguously into an intermediate buffer.

mkl_fft directly supports N-dimensional Fourier transforms.

More details can be found in SciPy 2017 conference proceedings:https://github.com/scipy-conference/scipy_proceedings/tree/2017/papers/oleksandr_pavlyk

mkl_fft implements the following functions:

complex-to-complex (c2c) transforms:

fft(x, n=None, axis=-1, overwrite_x=False, fwd_scale=1.0, out=out) - 1D FFT, similar to scipy.fft.fft

fft2(x, s=None, axes=(-2, -1), overwrite_x=False, fwd_scale=1.0, out=out) - 2D FFT, similar to scipy.fft.fft2

fftn(x, s=None, axes=None, overwrite_x=False, fwd_scale=1.0, out=out) - ND FFT, similar to scipy.fft.fftn

and similar inverse FFT (ifft*) functions.

real-to-complex (r2c) and complex-to-real (c2r) transforms:

rfft(x, n=None, axis=-1, fwd_scale=1.0, out=out) - r2c 1D FFT, similar to numpy.fft.rfft

rfft2(x, s=None, axes=(-2, -1), fwd_scale=1.0, out=out) - r2c 2D FFT, similar to numpy.fft.rfft2

rfftn(x, s=None, axes=None, fwd_scale=1.0, out=out) - r2c ND FFT, similar to numpy.fft.rfftn

and similar inverse c2r FFT (irfft*) functions.

The package also provides mkl_fft.interfaces.numpy_fft and mkl_fft.interfaces.scipy_fft interfaces which provide drop-in replacements for equivalent functions in NumPy and SciPy, respectively.

To build mkl_fft from sources on Linux with Intel® OneMKL:

• create a virtual environment: python3 -m venv fft_env
• activate the environment: source fft_env/bin/activate
• install a recent version of OneMKL, if necessary
• execute source /path_to_oneapi/mkl/latest/env/vars.sh
• git clone https://github.com/IntelPython/mkl_fft.git mkl_fft
• cd mkl_fft
• python -m pip install .
• cd ..
• python -c "import mkl_fft"

To build mkl_fft from sources on Linux with conda follow these steps:

• conda create -n fft_env python=3.12 mkl-devel
• conda activate fft_env
• export MKLROOT=$CONDA_PREFIX
• git clone https://github.com/IntelPython/mkl_fft.git mkl_fft
• cd mkl_fft
• python -m pip install .
• cd ..
• python -c "import mkl_fft"