A Julia package to explore a new system of array views.
By and large, this package is no longer necessary: base julia now has
efficient SubArrays (i.e., sub and slice). In choosing whether
to use SubArrays or the ArrayViews package, here are some
considerations:
Reasons to prefer SubArrays:
-
ArrayViewscan only make a view of anArray, whereasSubArrays can create a view of anyAbstractArray. -
The views created by
ArrayViewsare most efficient forContiguousViews such as column slices. In contrast, the views created bySubArrays are efficient for any type of view (e.g., also row slices), in some cases resulting in a 3- to 10-fold advantage. In either case, it's generally recommended to write functions using cartesian indexing rather than linear indexing (e.g.,for I in eachindex(S)rather thanfor i = 1:length(S)), although in both cases there are some view types that are also efficient under linear indexing. -
SubArrays allow more general slicing behavior, e.g., you can make a view withS = sub(A, [1,3,17], :). -
By default,
SubArrays check bounds upon construction whereasArrayViews do not:V = view(A, -5:10, :)does not generate an error, and ifVis used in a function with an@inboundsdeclaration you are likely to get a segfault. (You can bypass bounds checking withBase._subandBase._slice, in cases where you want out-of-bounds construction forSubArrays.)
Reasons to prefer ArrayViews:
- Construction of
SubArrays is frequently (but not always) 2-4 times slower than construction ofviews. If you are constructing many column views,ArrayViews may still be the better choice.
- An efficient
aviewfunction that implements array views - Support of arrays of arbitrary dimension and arbitrary combinations of indexers
- Support
aviewcomposition (i.e. construct views over views) - Special attention to ensure type stability in most cases
- Efficient indexing (both cartesian and linear)
- Light weight array view construction
- A systematic approach to detect contiguous views (statically)
- Views work with linear algebra functions
The key function in this package is aview. This function is similar to sub in Julia Base, except that it returns an aview instance with more efficient representation:
a = rand(4, 5, 6)
aview(a, :)
aview(a, :, 2)
aview(a, 1:2, 1:2:5, 4)
aview(a, 2, :, 3:6)The aview function returns an array view of type ArrayView.
Here, ArrayView is an abstract type with two derived types (ContiguousView and StridedView), defined as:
abstract type ArrayView{T,N,M} <: DenseArray{T,N} endWe can see that each view type has three static properties: element type T, the number of dimensions N, and the contiguous rank M.
The contiguous rank plays an important role in determining (statically) the contiguousness of a subview. Below are illustrations of 2D views respective with contiguous rank 0, 1, and 2.
2D View with contiguous rank 0
* * * * * *
. . . . . .
* * * * * *
. . . . . .
* * * * * *
. . . . . .
Here, * indicates a position covered by the array view, and . otherwise. We can see that the columns are not contiguous.
2D View with contiguous rank 1
* * * * * *
* * * * * *
* * * * * *
* * * * * *
. . . . . .
. . . . . .
We can see that each column is contiguous, while the entire array view is not.
2D View with contiguous rank 2
* * * * * *
* * * * * *
* * * * * *
* * * * * *
* * * * * *
* * * * * *
The entire 2D array view is contiguous.
Formally, when v is an array view with contiguous rank M, then aview(v, :, :, ..., :, 1) must be contiguous when the number of colons is less than or equal to M.
The package provide a hierarchy of array view types (defined as follows):
# T: the element type
# N: the number of dimensions
# M: the contiguous rank
abstract StridedArrayView{T,N,M} <: DenseArray{T,N}
abstract ArrayView{T,N,M} <: StridedArrayView{T,N,M}
abstract UnsafeArrayView{T,N,M} <: StridedArrayView{T,N,M}
immutable ContiguousView{T,N,Arr<:Array} <: ArrayView{T,N,N}
immutable StridedView{T,N,M,Arr<:Array} <: ArrayView{T,N,M}
immutable UnsafeContiguousView{T,N} <: UnsafeArrayView{T,N,N}
immutable UnsafeStridedView{T,N,M} <: UnsafeArrayView{T,N,M}Here, an instance of ArrayView maintains a reference to the underlying array, and is generally safe to use in most cases. An instance of UnsafeArrayView maintains a raw pointer, and should only be used within a local scope (as it does not guarantee that the source array remains valid if it is passed out of a function).
The following example illustrates how contiguous rank is used to determine aview types in practice.
a = rand(m, n)
# safe views
v0 = aview(a, :) # of type ContiguousView{Float64, 1}
u1 = aview(a, a:b, :) # of type StridedView{Float64, 2, 1}
u2 = aview(u1, :, i) # of type ContiguousView{Float64, 1}
v1 = aview(a, a:2:b, :) # of type StridedView{Float64, 2, 0}
v2 = aview(v1, :, i) # of type StridedView{Float64, 1, 0}
# unsafe views
v0 = unsafe_aview(a, :) # of type UnsafeContiguousView{Float64, 1}
u1 = unsafe_aview(a, a:b, :) # of type UnsafeStridedView{Float64, 2, 1}
u2 = unsafe_aview(u1, :, i) # of type UnsafeContiguousView{Float64, 1}
v1 = unsafe_aview(a, a:2:b, :) # of type UnsafeStridedView{Float64, 2, 0}
v2 = unsafe_aview(v1, :, i) # of type UnsafeStridedView{Float64, 1, 0}Four kinds of indexers are supported, integer, range (e.g. a:b), stepped range (e.g. a:b:c), and colon (i.e., :).
The procedure of constructing a aview consists of several steps:
-
Compute the shape of an array view. This is done by an internal function
vshape. -
Compute the offset of an array view. This is done by an internal function
aoffset. The computation is based on the following formula:offset(v(I1, I2, ..., Im)) = (first(I1) - 1) * stride(v, 1) + (first(I2) - 1) * stride(v, 2) + ... + (first(Im) - 1) * stride(v, m) -
Compute the contiguous rank, based on both view shape and the combination of indexer types. A type
ContRank{M}is introduced for static computation of contiguous rank (please refer tosrc/contrank.jlfor details). -
Construct a aview, where the array view type is determined by both the number of dimensions and the value of contiguous rank (which is determined statically).
For runtime efficiency, specialized methods of these functions are implemented for views of 1D, 2D, and 3D. These methods are extensively tested.
The ArrayViews package provides several functions to make it more convenient to constructing certain views:
diagview(a) # make a strided view of the diagonal elements, the length is `min(size(a)...)`
# `a` needs to be a matrix here (contiguous or strided)
flatten_view(a) # make a contiguous view of `a` as a vector
# `a` needs to be contiguous here
reshape_view(a, shp) # make a contiguous view of `a` of shape `shp`
# `a` needs to be contiguous here.
rowvec_view(a, i) # make a view of `a[i,:]` as a strided vector.
# `a` needs to be a matrix here (contiguous or strided)
ellipview(a, i) # make a view of the i-th slice of a
# e.g. `a` is a matrix => this is equiv. to `aview(a, :, i)`
# `a` is a cube => this is equiv. to `aview(a, :, :, i)`, etc.
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