DOC: Moved NumPy Internals into Under-the-hood docs

1 files changed, 5 insertions, 96 deletions

diff --git a/doc/source/reference/alignment.rst b/doc/source/reference/alignment.rst
index 5e4315b38..70ded916a 100644
--- a/doc/source/reference/alignment.rst
+++ b/doc/source/reference/alignment.rst
@@ -1,104 +1,13 @@
-.. _alignment:
+:orphan:
 
+****************
 Memory Alignment
-================
+****************
 
-Numpy Alignment Goals
----------------------
+.. This document has been moved to ../dev/alignment.rst.
 
-There are three use-cases related to memory alignment in numpy (as of 1.14):
+This document has been moved to :ref:`alignment`.
 
- 1. Creating structured datatypes with fields aligned like in a C-struct.
- 2. Speeding up copy operations by using uint assignment in instead of memcpy
- 3. Guaranteeing safe aligned access for ufuncs/setitem/casting code
 
-Numpy uses two different forms of alignment to achieve these goals:
-"True alignment" and "Uint alignment".
-
-"True" alignment refers to the architecture-dependent alignment of an
-equivalent C-type in C. For example, in x64 systems ``numpy.float64`` is
-equivalent to ``double`` in C. On most systems this has either an alignment of
-4 or 8 bytes (and this can be controlled in gcc by the option
-``malign-double``).  A variable is aligned in memory if its memory offset is a
-multiple of its alignment. On some systems (eg sparc) memory alignment is
-required, on others it gives a speedup.
-
-"Uint" alignment depends on the size of a datatype. It is defined to be the
-"True alignment" of the uint used by numpy's copy-code to copy the datatype, or
-undefined/unaligned if there is no equivalent uint. Currently numpy uses uint8,
-uint16, uint32, uint64 and uint64 to copy data of size 1,2,4,8,16 bytes
-respectively, and all other sized datatypes cannot be uint-aligned.
-
-For example, on a (typical linux x64 gcc) system, the numpy ``complex64``
-datatype is implemented as ``struct { float real, imag; }``. This has "true"
-alignment of 4 and "uint" alignment of 8 (equal to the true alignment of
-``uint64``).
-
-Some cases where uint and true alignment are different (default gcc linux):
-   arch     type        true-aln    uint-aln
-   ----     ----        --------    --------
-   x86_64   complex64          4           8
-   x86_64   float128          16           8
-   x86      float96            4           -
-
-
-Variables in Numpy which control and describe alignment
--------------------------------------------------------
-
-There are 4 relevant uses of the word ``align`` used in numpy:
-
- * The ``dtype.alignment`` attribute (``descr->alignment`` in C). This is meant
-   to reflect the "true alignment" of the type. It has arch-dependent default
-   values for all datatypes, with the exception of structured types created
-   with ``align=True`` as described below.
- * The ``ALIGNED`` flag of an ndarray, computed in ``IsAligned`` and checked
-   by ``PyArray_ISALIGNED``. This is computed from ``dtype.alignment``.
-   It is set to ``True`` if every item in the array is at a memory location
-   consistent with ``dtype.alignment``, which is the case if the data ptr and
-   all strides of the array are multiples of that alignment.
- * The ``align`` keyword of the dtype constructor, which only affects structured
-   arrays. If the structure's field offsets are not manually provided numpy
-   determines offsets automatically. In that case, ``align=True`` pads the
-   structure so that each field is "true" aligned in memory and sets
-   ``dtype.alignment`` to be the largest of the field "true" alignments. This
-   is like what C-structs usually do. Otherwise if offsets or itemsize were
-   manually provided ``align=True`` simply checks that all the fields are
-   "true" aligned and that the total itemsize is a multiple of the largest
-   field alignment. In either case ``dtype.isalignedstruct`` is also set to
-   True.
- * ``IsUintAligned`` is used to determine if an ndarray is "uint aligned" in
-   an analogous way to how ``IsAligned`` checks for true-alignment.
-
-Consequences of alignment
--------------------------
-
-Here is how the variables above are used:
-
- 1. Creating aligned structs: In order to know how to offset a field when
-    ``align=True``, numpy looks up ``field.dtype.alignment``. This includes
-    fields which are nested structured arrays.
- 2. Ufuncs: If the ``ALIGNED`` flag of an array is False, ufuncs will
-    buffer/cast the array before evaluation. This is needed since ufunc inner
-    loops access raw elements directly, which might fail on some archs if the
-    elements are not true-aligned.
- 3. Getitem/setitem/copyswap function: Similar to ufuncs, these functions
-    generally have two code paths. If ``ALIGNED`` is False they will
-    use a code path that buffers the arguments so they are true-aligned.
- 4. Strided copy code: Here, "uint alignment" is used instead.  If the itemsize
-    of an array is equal to 1, 2, 4, 8 or 16 bytes and the array is uint
-    aligned then instead numpy will do ``*(uintN*)dst) = *(uintN*)src)`` for
-    appropriate N. Otherwise numpy copies by doing ``memcpy(dst, src, N)``.
- 5. Nditer code: Since this often calls the strided copy code, it must
-    check for "uint alignment".
- 6. Cast code: This checks for "true" alignment, as it does
-    ``*dst = CASTFUNC(*src)`` if aligned. Otherwise, it does
-    ``memmove(srcval, src); dstval = CASTFUNC(srcval); memmove(dst, dstval)``
-    where dstval/srcval are aligned.
-
-Note that the strided-copy and strided-cast code are deeply intertwined and so
-any arrays being processed by them must be both uint and true aligned, even
-though the copy-code only needs uint alignment and the cast code only true
-alignment.  If there is ever a big rewrite of this code it would be good to
-allow them to use different alignments.