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

Introduction

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 offers a thin layered interface for the Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality that allows efficient access to native FFT optimizations from a range of NumPy and SciPy functions. As a result, its performance is close to the performance of native C/Intel® OneMKL. The optimizations are provided for real and complex data types in both single and double precisions for in-place and out-of-place modes of operation. For analyzing the performance use FFT benchmarks.

Thanks to Intel® OneMKL’s flexibility in its supports for arbitrarily strided input and output arrays both one-dimensional and multi-dimensional Fast Fourier Transforms along distinct axes can be performed directly, without the need to copy the input into a contiguous array first. Furthermore, input strides can be arbitrary, including negative or zero, as long as strides remain an integer multiple of array’s item size, otherwise a copy will be made.

More details can be found in "Accelerating Scientific Python with Intel Optimizations" from Proceedings of the 16th Python in Science Conference (SciPy 2017).

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Installation

mkl_fft 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

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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/

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How to use?

mkl_fft.interfaces module

The recommended way to use mkl_fft package is through mkl_fft.interfaces module. These interfaces act as drop-in replacements for equivalent functions in NumPy and SciPy. Learn more about these interfaces here.

mkl_fft package

While using the interfaces module is the recommended way to leverage mk_fft, one can also use mkl_fft directly with the following FFT functions:

complex-to-complex (c2c) transforms:

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

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

fftn(x, s=None, axes=None, overwrite_x=False, fwd_scale=1.0, out=None) - 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=None) - r2c 1D FFT, similar to numpy.fft.rfft

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

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

and similar inverse c2r FFT (irfft*) functions.

The following example shows how to use mkl_fft for calculating a 1D FFT.

import numpy, mkl_fft
a = numpy.random.randn(10) + 1j*numpy.random.randn(10)

mkl_res = mkl_fft.fft(a)
np_res = numpy.fft.fft(a)
numpy.allclose(mkl_res, np_res)
# True

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Building from source

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"