Memory Alignment¶

Numpy Alignment Goals¶

There are three use-cases related to memory alignment in numpy (as of 1.14):

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.