- use plain variables
 - clean up aligned allocate
 - use `PyArg_ParseTuple` for empty args
 - use `Py_ssize_t` instead of `unsigned` and `size_t`
 - improve coding style
 - no need for a custom raises assertions
 - use parametrize instead of inner loops
 - leave a comment about nature of mode testing unit
 - shift to get max/min of int72
 - add more info to repr of vector object
 - get ride of exec() and use type() instead
 - use `.inc` as extension for sub-headers instead of `.h`
 - add `FMA4` and drop `SSE41` from _SIMD targets

Co-authored-by: Eric Wieser <wieser.eric@gmail.com>

  '_simd' is a new module to bring the NumPy C SIMD vectorization interface "NPYV"

  The module is designed to be extremely flexible so that it can accommodate any kind
  intrinsics, also to generate a python interface almost similar to the C interface.

  The main purpose of this module is to test NPYV intrinsics in python,
  but still can be used as an effective solution in designing SIMD kernels.

  Also add a new command-line argument `--simd-test` to control of targeted CPU features
  for the `_simd` module.

Co-authored-by: Matti Picus <matti.picus@gmail.com>
Co-authored-by: Eric Wieser <wieser.eric@gmail.com>

  - Put `CCompilerOpt` in action through add two command line
    arguments that passed directly to `CCompilerOpt`'s
    parameters which explained as follows:

    * `--cpu-baseline` minimal set of required optimizations,
      default is 'min' which provides the minimum CPU features
      that can safely run on a wide range of users platforms.

    * `--cpu-dispatch` dispatched set of additional optimizations,
      default is 'max-xop-fma4' which enables all CPU features,
      except for AMD legacy features.

    the new arguments can be reached from `build`, `build_clib`,
    `build_ext`, if `build_clib` or `build_ext` are not specified
    by the user the arguments of `build` will be used, which also
    hold the default values.

  - Activate the new compiler dispatcher that comes with `CCompilerOpt`,
    through adding a hock inside `build_clib` and `build_ext`
    that works as a filter taking any C source files ends with
    `.dispatch.c` and pass it directly to `CCompilerOpt` and
    then take returned objects and linked to the final C lib.

  - Add a third command-line argument `--disable-optimization` which
    explicitly disable the whole new infrastructure, also
    It adds a new compiler definition called `NPY_DISABLE_OPTIMIZATION`.

    when `--disable-optimization` is enabled the dispatch-able sources
    that end with `.dispatch.c` will be treated as a normal
    C sources, also due to this disabling any C headers that
    generated by `CCompilerOpt` must guard it with `NPY_DISABLE_OPTIMIZATION`,
    otherwise, it will definitely break the build.

  - New auto-generated C header located at `core/include/numpy/_cpu_dispatch.h`,
    the new header contains all definitions and headers of CPU features that
    enabled according to specified configuration in `--cpu-baseline`
    and `--cpu-dispatch`.

As numpy is Python 3 only, these import statements are now unnecessary
and don't alter runtime behavior.

python3.5 uses --parallel instead of --jobs