v0.3.0

What's Changed

Added

• Added mkl implementation for floating point data-types of exp2, log2, fabs, copysign, nextafter, fmax, fmin and remainder functions gh-81
• Added mkl implementation for complex data-types of conjugate and absolute functions gh-86
• Enabled support of Python 3.13 gh-101
• Added mkl implementation for complex data-types of add, subtract, multiply and divide functions gh-102

Changed

• Dropped support for maximum and minimum gh-104
• Disabled -fast-math by default gh-105
• Used a common umath loop for log2 function to match NumPy gh-109
• Dropped support for remainder function gh-110

New Contributors

• @antonwolfy made their first contribution in #109
• @jharlow-intel made their first contribution in #112

Contributors

• @antonwolfy
• @ekomarova
• @jharlow-intel
• @ndgrigorian
• @vtavana

Full Changelog: v0.2.0...0.3.0

v0.2.0

This release updates mkl_umath to be aligned with both numpy-1.26.x and numpy-2.x.x.

Added

• The definition of sign function for complex floating point data types is updated to match numpy-2.x.x gh-65
• ldexp function is updated to allow int64 explicitly similar to numpy-2.x.x behavior gh-73

Changed

• Migrated from setup.py to pyproject toml gh-63
• Changed to dynamic linking and added interface and threading layers gh-72

Fixed

• Fixed a bug for mkl_umath.is_patched function gh-66

v0.1.5

This release

• Fixes failures to import mkl_umath from virtual environment on Linux

v0.1.4

This release

• Adds a fix for a bug in in-place addition with negative zeros
• Adds support for mkl_umath out-of-the-box in virtual environments on Windows

v0.1.2

Update build system from removed NumPy distutils to scikit-build, gain ability to build with Intel LLVM compiler ICX. Update to build with NumPy 2.0 and older.

v0.1.1

Released version of mkl_umath.

The package implements numpy.ufunc instances which are powered by Intel(R) Math Kernel Library's Vector Math component and Intel(R) C Compiler generated calls to Intel(R) Short Vector Math Library functions for appropriate data types.

The package provides for a way to register typed evaluation loops provided by this package with the numpy.ufunc instances, thus monkey patching NumPy for improved performance (e.g. mkl_umath.use_in_numpy() and mkl_umath.restore().)

Supported types can be queries using attributes of numpy.ufunc instance:

In [44]: mkl_umath.multiply.types
Out[44]: ['ff->f', 'dd->d', 'FF->F', 'DD->D']