Metadata-Version: 1.1
Name: numkl
Version: 0.0.2
Summary: A thin cython/python wrapper on some routines from Intel MKL
Home-page: https://github.com/refraction-ray/numkl
Author: refraction-ray
Author-email: refraction-ray@protonmail.com
License: UNKNOWN
Description: # NUMKL
        
        This package works as the python wrapper to directly call some MKL routines while keep the same interface with numpy.
        
        ## Install
        
        `pip install numkl`
        
        You should make sure Intel MKL library and Intel compilers are installed and configured for relevant enviroment variables.
        Intel parallel studio XE is recommended. 
        
        No gurantee on GNU compilers. 
        
        Only linux is supported.
        
        ## Example
        
        ```python
        import numpy as np
        from numkl import eig
        a = np.array([[0.,1.0],[1.0,0.]])
        e,v = eig.eighx(a)
        ```
        
        ## Why
        
        This package is not reinventing wheels like numpy, instead, it provide features that current numpy doesn't provide.
        
        For the symmetric or Hermitian matrix eigenproblem, numpy has already provided the interface `numpy.linalg.eigh` and `numpy.linalg.eigvalsh`. By correctly configuring and linking, these two functions also can directly calling MKL routines. So why bother?
        
        There are at least two aspects why numpy eigenproblem interface is not good enough:
        
        1. The 32 bit int overflow and unable to calculate eigenproblem for large matrix. See [this issue](https://github.com/numpy/numpy/issues/13956). Note currently this issue cannot be solve by simply hacking the compiling parameters, instead one need to change the source code of numpy.
        2. The memory waste due to keeping the input matrix. See [this issue](https://github.com/numpy/numpy/issues/14024). Actually, it costs two times of the space in numpy for getting all eigenvalues than directly using lapack routine.
        
        In a word, this package is designed for "push-to-the-limit" style computations, where you can compute the eigenproblem for matrix dimension larger than 32766. And the interface is seamlessly integrated with numpy.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
