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The library provides a layer on top of DPH unlifted arrays to support multi-dimensional arrays, and shape polymorphic operations for fast sequential and parallel execution. The interface for delayed arrays is similar, but in contrast to operations on the former, any operation on a delayed array is not evaluated. To force evaluation, the programmer has to explicitly convert them to a strict array.

The current implementation of the library exposes some implementation details the user of the library shouldn't have to worry about. Once the design of the library is finalised, most of these will be hidden by distinguishing between internal types and representation types.

## Strict Arrays, Delayed Array and Shape Data Type

Both strict and delayed arrays are parametrised with their shape - that is, their dimensionality and size of each dimension. The shape type class has the following interface:

### DArrays

DArrays are collections of values of `primitive' type, which are member of the class Data.Parallel.Array.Unlifted.Elt, which includes all basic types like Float, Double, Bool, Char, Integer, and pairs constructed with Data.Parallel.Array.Unlifted.(:*:), including ().

### The Shape class

Values of class Shape serve two purposes: they can describe the dimensionality and size of an array (in which case we refer to them as 'shape'), and they can also refer to the position of a particular element in the array (in which case we refer to them as an 'index'). It provides the following methods:

class (Show sh, U.Elt sh, Eq sh, Rebox sh) => Shape sh where dim :: sh -> Int -- ^number of dimensions (>= 0) size :: sh -> Int -- ^for a *shape* yield the total number of elements in that array toIndex :: sh -> sh -> Int -- ^corresponding index into a linear representation of -- the array (first argument is the shape) fromIndex:: sh -> Int -> sh -- ^given index into linear row major representation, calculates -- index into array range :: sh -> U.Array sh -- ^all the valid indices in a shape. The following equality should hold: -- map (toIndex sh) (range sh) = [:0..(size sh)-1:] inRange :: sh -> sh -> Bool -- ^Checks if a given index is in the range of an array shape. I.e., -- inRange sh ind == elem ind (range sh) zeroDim :: sh -- ^shape of an array of size zero of the particular dimensionality intersectDim :: sh -> sh -> sh -- ^shape of an array of size zero of the particular dimensionality next:: sh -> sh -> Maybe sh -- ^shape of an array of size zero of the particular dimensionality

Note that a `Shape`

has to be in the type class `Elt`

imported from `Data.Parallel.Array.Unboxed`

so
that it can be an element of `Data.Parallel.Array.Unboxed.Array`

.

The following instances are defined

instance Shape () instance Shape sh => Shape (sh :*: Int)

so we have inductively defined n-tuples of `Int`

values to represent shapes. This somewhat unusual representation
is necessary to be able to inductively define operations on `Shape`

. It should, however, be hidden from the library
user in favour of the common tuple representation.

## Operations on Arrays and Delayed Arrays

### Array Creation and Conversion

Strict arrays are simply defined as record containing a flat data array and shape information:

data Array dim e where Array:: { arrayShape :: dim -- ^extend of dimensions , arrayData :: U.Array e -- flat parallel array } -> Array dim e deriving Show toArray :: (U.Elt e, Shape dim) => dim -> U.Array e -> Array dim e fromArray:: (U.Elt e, Shape dim) => Array dim e -> U.Array e

Delayed arrays, in contrast, in addition to the shape, only contain a function which, given an index, yields the corresponding element.

data DArray dim e where DArray :: {dArrayShape::dim -> dArrayFn:: (dim -> e)} -> DArray dim e

Delayed arrays can be converted to and from strict arrays:

toDArray:: (U.Elt e, Array.Shape dim) => Array.Array dim e -> DArray dim e fromDArray:: (U.Elt e, Array.Shape dim) => DArray dim e -> Array dim e

the result of `toDArray`

is a DArray which contains an indexing function into
an array. In general, the function `dArrayFn`

can be much more complex. The function
`forceDArray`

(should this be called `normalizeDArray`

?) forces the evaluation `dArrayFn`

on
every index of the range, and replaces `dArrayFn`

by a simple indexing function into an array
of the result values.

forceDArray:: (U.Elt e, A.Shape dim) => DArray dim e -> DArray dim e

## Collection Oriented Operations

### Elementwise Application of Functions

The `map`

operation takes a function over element types and applies it to every
data element of the DArray, which can have arbitrary dimensionality. Note that
it is not possible to use this function to apply an operation for example to every
row or column of a matrix. We will discuss how this can be done later on.

`map:: (U.Elt a, U.Elt b, A.Shape dim) => (a -> b) -> DArray dim a -> DArray dim b`

Similarily, `zip`

and `zipWith`

apply to every data element in the array as well. Both arguments
have to have the same dimensionality (which is enforced by the type system). If they have a different
shape, the result will have the intersection of both shapes. For example, zipping an array of shape
`(() :*: 4 :*: 6)`

and `(() :*: 2 :*: 8)`

results in an array of shape `(() :*: 2 :*: 6)`

.

zipWith:: (U.Elt a, U.Elt b, U.Elt c, A.Shape dim) => (a -> b -> c) -> DArray dim a -> DArray dim b-> DArray dim c zip:: (U.Elt a, U.Elt b, A.Shape dim) => DArray dim a -> DArray dim b-> DArray dim (a :*: b)

The function `fold`

collapses the values of the innermost rows of an array of at least dimensionality 1.

fold :: (U.Elt e, A.Shape dim) => (e -> e-> e) -> e -> DArray (dim :*: Int) e -> DArray dim e

Again, it's not possible to use `fold`

directly to collapse an array along any other axis, but, as
we will see shortly, this can be easily done using other functions in combination with `fold`

.

TODO: MISSING: description of mapStencil

### Reordering, Shifting, Tiling

Backpermute and default backpermute are two very versatile operations which allow the programmer to express all structural operations which reorder or extract elements based on their position in the argument array:

backpermute:: (U.Elt e, A.Shape dim, A.Shape dim') => DArray dim e -> dim' -> (dim' -> dim) -> DArray dim' e backpermuteDft::(U.Elt e, A.Shape dim, A.Shape dim') => DArray dim e -> e -> dim' -> (dim' -> Maybe dim) -> DArray dim' e

The function `backpermute`

gets a source array, the shape of the new array, and
a function which maps each index of the new array to an index of the source array (and
thus indirectly provides a value for each index in the new array). Default backpermute is
additionally provided with a default value which is inserted in the array in cases where the
index function returns `Nothing`

. (Remark: should probably be replaced by a default array instead of
default value for more generality)

`reshape arr newShape`

returns a new array with the same value as the argument array, but a new shape. The
new shape has to have the same size as the original shape.

reshape:: (Shape dim', Shape dim, U.Elt e) => DArray dim e -> dim' -> DArray dim' e

### Shape Polymorphic Computations on Arrays

The array operations described in this and the following subsection
are available on both strict and delayed arrays, and yield the same
result, with the exception that in case of delayed arrays, the result
is only calculated once its forced by calling `fromDArray`

or `forceDArray`

. No
intermediate array structures are ever created.

The library provides a range of operation where the dimensionality of the result depends on the dimensionality of the argument in a non-trivial manner, which we want to be reflected in the type system. Examples of such functions are generalised selection, which allows for extraction of subarrays of arbitrary dimension, and generalised replicate, which allows replication of an array in any dimension (or dimensions).

For selection, we can informally state the relationship between dimensionality of the argument, the selector, and the result as follows:

select:: Array dim e -> <select dim' of dim array> -> Array dim' e

To express this relationship, the library provides the index GADT, which expresses a relationship between the inital and the projected dimensionality. It is defined as follows:

data Index a initialDim projectedDim where IndexNil :: Index a () () IndexAll :: (Shape init, Shape proj) => Index a init proj -> Index a (init :*: Int) (proj :*: Int) IndexFixed :: (Shape init, Shape proj) => a -> Index a init proj -> Index a (init :*: Int) proj type SelectIndex = Index Int type MapIndex = Index ()

Given this definition, the type of `select`

now becomes

select:: (U.Elt e, Shape dim, Shape dim') => Array dim e -> SelectIndex dim dim' -> Array dim' e

Example:

arr:: Array DIM3 Double select arr (IndexFixed 3 (IndexAll (IndexAll IndexNil)))

The index type is also used to express the type of generalised replicate:

replicate:: Array dim' e -> SelectIndex dim dim' -> Array dim e

Even though the index type serves well to express the relationship between the selector/multiplicator and the dimensionality of the argument and the result array, it is somehow inconvenient to use, as the examples demonstrate. This is therefore another example where we need to add another layer to improve the usability of the library.

Note that the library provides no way to statically check the pre- and postconditions on the actual size of arguments and results. This has to be done at run time using assertions.

## Array Operations

Backpermute and default backpermute are two general operations which allow the programmer to express all structural operations which reorder or extract elements based on their position in the argument array:

backpermute:: (U.Elt e, Shape dim, Shape dim') => Array dim e -> dim' -> (dim' -> dim) -> Array dim' e backpermuteDft::(U.Elt e, Shape dim, Shape dim') => Array dim e -> e -> dim' -> (dim' -> Maybe dim) -> Array dim' e

The following operations could be (and in the sequential implementation indeed are) expressed in terms of backpermute and default backpermute. However, a programmer should aim at using more specialised functions when provided, as they carry more information about the pattern of reordering. In particular in the parallel case, this could be used to provide significantly more efficient implementation which make use of locality and communication patterns.

shift:: (Shape dim, U.Elt e) => Array dim e -> e -> dim -> Array dim e rotate:: (Shape dim, U.Elt e) => Array dim e -> e -> dim -> Array dim e tile:: (Shape dim, U.Elt e) => Array dim e -> dim -> dim -> Array dim e