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