use oneMKL instead OneMKL

Author: vtavana

README.md

@@ -1,14 +1,14 @@
-## `mkl_fft` --  a NumPy-based Python interface to Intel® oneAPI Math Kernel Library (OneMKL) FFT functionality
+## `mkl_fft` --  a NumPy-based Python interface to Intel® oneAPI Math Kernel Library (oneMKL) FFT functionality
 [![Conda package](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package.yml/badge.svg)](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package.yml)
 [![Editable build using pip and pre-release NumPy](https://github.com/IntelPython/mkl_fft/actions/workflows/build_pip.yaml/badge.svg)](https://github.com/IntelPython/mkl_fft/actions/workflows/build_pip.yaml)
 [![Conda package with conda-forge channel only](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package-cf.yml/badge.svg)](https://github.com/IntelPython/mkl_fft/actions/workflows/conda-package-cf.yml)
 [![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/IntelPython/mkl_fft/badge)](https://securityscorecards.dev/viewer/?uri=github.com/IntelPython/mkl_fft)
 
 # 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](https://github.com/intelpython/fft_benchmark).
+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](https://github.com/intelpython/fft_benchmark).
 
-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.
+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"](https://proceedings.scipy.org/articles/shinma-7f4c6e7-00f) from Proceedings of the 16th Python in Science Conference (SciPy 2017).
 
@@ -85,10 +85,10 @@ numpy.allclose(mkl_res, np_res)
 ---
 # Building from source
 
-To build `mkl_fft` from sources on Linux with Intel® OneMKL:
+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
+  - 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`

mkl_fft/interfaces/README.md

@@ -101,7 +101,7 @@ print(f"Time with scipy.fft default backend: {t1:.1f} seconds")
 with scipy.fft.set_backend(mkl_backend, only=True):
     t2 = timeit.timeit(lambda: scipy.signal.fftconvolve(a, b), number=10)
 
-print(f"Time with OneMKL FFT backend installed: {t2:.1f} seconds")
+print(f"Time with oneMKL FFT backend installed: {t2:.1f} seconds")
 # Time with MKL FFT backend installed: 9.1 seconds
 ```