Difference between revisions of "Numeric Haskell: A Repa Tutorial"
m (Fixed incorrect indices) 
(Decreased header indentation) 

Line 30:  Line 30:  
* [http://www.cse.unsw.edu.au/~benl/papers/stencil/stencilicfp2011sub.pdf Efﬁcient Parallel Stencil Convolution in Haskell] 
* [http://www.cse.unsw.edu.au/~benl/papers/stencil/stencilicfp2011sub.pdf Efﬁcient Parallel Stencil Convolution in Haskell] 

−  +  = Importing the library = 

Download the `repa` package: 
Download the `repa` package: 

Line 55:  Line 55:  
* [http://hackage.haskell.org/package/repaexamples repaexamples] 
* [http://hackage.haskell.org/package/repaexamples repaexamples] 

−  +  = Index types and shapes = 

Before we can get started manipulating arrays, we need a grasp of repa's notion of array shape. Much like the classic 'array' library in Haskell, repabased arrays are parameterized via a type which determines the dimension of the array, and the type of its index. However, while classic arrays take tuples to represent multiple dimensions, Repa arrays use a [http://hackage.haskell.org/packages/archive/repa/2.0.0.3/doc/html/DataArrayRepaShape.html#t:Shape richer type language] for describing multidimensional array indices and shapes (technically, a ''heterogeneous snoc list''). 
Before we can get started manipulating arrays, we need a grasp of repa's notion of array shape. Much like the classic 'array' library in Haskell, repabased arrays are parameterized via a type which determines the dimension of the array, and the type of its index. However, while classic arrays take tuples to represent multiple dimensions, Repa arrays use a [http://hackage.haskell.org/packages/archive/repa/2.0.0.3/doc/html/DataArrayRepaShape.html#t:Shape richer type language] for describing multidimensional array indices and shapes (technically, a ''heterogeneous snoc list''). 

Line 98:  Line 98:  
over arrays with different shape. 
over arrays with different shape. 

−  +  == Building shapes == 

To build values of shape type, we can use the <code>Z</code> and <code>:.</code> constructors. Open the ghci and import Repa: 
To build values of shape type, we can use the <code>Z</code> and <code>:.</code> constructors. Open the ghci and import Repa: 

Line 132:  Line 132:  
Additional convenience types for selecting particular parts of a shape are also provided (<code>All, Any, Slice</code> etc.) which are covered later in the tutorial. 
Additional convenience types for selecting particular parts of a shape are also provided (<code>All, Any, Slice</code> etc.) which are covered later in the tutorial. 

−  +  == Working with shapes == 

That one key operation, <code>extent</code>, gives us many attributes of an array: 
That one key operation, <code>extent</code>, gives us many attributes of an array: 

Line 169:  Line 169:  
</haskell> 
</haskell> 

−  +  = Generating arrays = 

New repa arrays ("arrays" from here on) can be generated in many ways, and we always begin by importing the <code>Data.Array.Repa</code> module: 
New repa arrays ("arrays" from here on) can be generated in many ways, and we always begin by importing the <code>Data.Array.Repa</code> module: 

Line 180:  Line 180:  
Loading package base ... linking ... done. 
Loading package base ... linking ... done. 

Prelude > :m + Data.Array.Repa 
Prelude > :m + Data.Array.Repa 

−  
−  
They may be constructed from lists, for example. Here is a one dimensional array of length 10, here, given the shape <code>(Z :. 10)</code>: 
They may be constructed from lists, for example. Here is a one dimensional array of length 10, here, given the shape <code>(Z :. 10)</code>: 

Line 235:  Line 233:  
</haskell> 
</haskell> 

−  +  == Building arrays from vectors == 

It is also possible to build arrays from unboxed vectors, from the 'vector' package: 
It is also possible to build arrays from unboxed vectors, from the 'vector' package: 

Line 265:  Line 263:  
to create a 3x3 array. 
to create a 3x3 array. 

−  +  == Generating random arrays == 

The [http://hackage.haskell.org/package/repaalgorithms repaalgorithms] package lets us generate new arrays with random data: 
The [http://hackage.haskell.org/package/repaalgorithms repaalgorithms] package lets us generate new arrays with random data: 

Line 276:  Line 274:  
</haskell> 
</haskell> 

−  +  == Reading arrays from files == 

Using the [http://hackage.haskell.org/package/repaio repaio] package, arrays may be written and read from files in a number of formats: 
Using the [http://hackage.haskell.org/package/repaio repaio] package, arrays may be written and read from files in a number of formats: 

Line 315:  Line 313:  
xx :: Array U DIM2 Double 
xx :: Array U DIM2 Double 

</haskell> 
</haskell> 

−  
To process [http://en.wikipedia.org/wiki/BMP_file_format .bmp files], use [http://hackage.haskell.org/packages/archive/repaio/2.0.0.3/doc/html/DataArrayRepaIOBMP.html Data.Array.Repa.IO.BMP], as follows (currently reading only works for 24 bit .bmp): 
To process [http://en.wikipedia.org/wiki/BMP_file_format .bmp files], use [http://hackage.haskell.org/packages/archive/repaio/2.0.0.3/doc/html/DataArrayRepaIOBMP.html Data.Array.Repa.IO.BMP], as follows (currently reading only works for 24 bit .bmp): 

Line 331:  Line 328:  
For image IO in many, many formats, use the [http://hackage.haskell.org/package/repadevil repadevil] library. 
For image IO in many, many formats, use the [http://hackage.haskell.org/package/repadevil repadevil] library. 

−  +  == Copying arrays from pointers == 

You can also generate new repa arrays by copying data from a pointer, using the [http://hackage.haskell.org/package/repabytestring repabytestring] package. Here is an example, using <code>copyFromPtrWord8</code>: 
You can also generate new repa arrays by copying data from a pointer, using the [http://hackage.haskell.org/package/repabytestring repabytestring] package. Here is an example, using <code>copyFromPtrWord8</code>: 

Line 372:  Line 369:  
http://i.imgur.com/o0Cv2.png 
http://i.imgur.com/o0Cv2.png 

−  +  = Indexing arrays = 

To access elements in repa arrays, you provide an array and a shape, to access the element: 
To access elements in repa arrays, you provide an array and a shape, to access the element: 

Line 422:  Line 419:  
</haskell> 
</haskell> 

−  +  = Operations on arrays = 

Besides indexing, there are many regular, listlike operations on arrays. Since many of the names parallel those in the Prelude, we import Repa qualified: 
Besides indexing, there are many regular, listlike operations on arrays. Since many of the names parallel those in the Prelude, we import Repa qualified: 

Line 428:  Line 425:  
Repa> import qualified Data.Array.Repa as Repa 
Repa> import qualified Data.Array.Repa as Repa 

−  +  == Maps, zips, filters and folds == 

We can map over multidimensional arrays: 
We can map over multidimensional arrays: 

Line 505:  Line 502:  
((Z :. 3) :. 3) :. 3 
((Z :. 3) :. 3) :. 3 

−  +  == Mapping, with indices == 

A very powerful operator is <code>traverse</code>, a parallel array traversal which also supplies the current index: 
A very powerful operator is <code>traverse</code>, a parallel array traversal which also supplies the current index: 

Line 557:  Line 554:  
The documentation on [http://hackage.haskell.org/packages/archive/repa/2.0.2.1/doc/html/DataArrayRepa.html#g:7 traverse] provides further information. 
The documentation on [http://hackage.haskell.org/packages/archive/repa/2.0.2.1/doc/html/DataArrayRepa.html#g:7 traverse] provides further information. 

−  +  == Numeric operations: negation, addition, subtraction, multiplication == 

Repa arrays are instances of the <code>Num</code>. This means that 
Repa arrays are instances of the <code>Num</code>. This means that 

Line 579:  Line 576:  
[1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0,100.0] 
[1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0,100.0] 

−  +  = Changing the shape of an array = 

One of the main advantages of repastyle arrays over other arrays in 
One of the main advantages of repastyle arrays over other arrays in 

Line 629:  Line 626:  
arrays into larger ones. 
arrays into larger ones. 

−  +  = Examples = 

Following are some examples of useful functions that exercise the API. 
Following are some examples of useful functions that exercise the API. 

−  +  == Example: Rotating an image with backpermute == 

Flip an image upside down: 
Flip an image upside down: 

Line 667:  Line 664:  
http://i.imgur.com/YsGA8.jpg 
http://i.imgur.com/YsGA8.jpg 

−  +  == Example: matrixmatrix multiplication == 

A more advanced example from the Repa paper: matrixmatrix multiplication: the result of 
A more advanced example from the Repa paper: matrixmatrix multiplication: the result of 

Line 713:  Line 710:  
[http://hackage.haskell.org/package/repaalgorithms repaalgorithms] package. 
[http://hackage.haskell.org/package/repaalgorithms repaalgorithms] package. 

−  +  == Example: parallel image desaturation == 

To convert an image from color to greyscale, we can use the luminosity method to average RGB pixels into a common grey value, where the average is weighted for human perception of green. 
To convert an image from color to greyscale, we can use the luminosity method to average RGB pixels into a common grey value, where the average is weighted for human perception of green. 

Line 770:  Line 767:  
http://i.imgur.com/REhA5.png 
http://i.imgur.com/REhA5.png 

−  +  = Optimising Repa programs = 

−  +  == Fusion, and why you need it == 

Repa depends critically on array fusion to achieve fast code. Fusion is a fancy name for the combination of inlining and code transformations performed by GHC when it compiles your program. The fusion process merges the array filling loops defined in the Repa library, with the "worker" functions that you write in your own module. If the fusion process fails, then the resulting program will be much slower than it needs to be, often 10x slower an equivalent program using plain Haskell lists. On the other hand, provided fusion works, the resulting code will run as fast as an equivalent cleanly written C program. Making fusion work is not hard once you understand what's going on. 
Repa depends critically on array fusion to achieve fast code. Fusion is a fancy name for the combination of inlining and code transformations performed by GHC when it compiles your program. The fusion process merges the array filling loops defined in the Repa library, with the "worker" functions that you write in your own module. If the fusion process fails, then the resulting program will be much slower than it needs to be, often 10x slower an equivalent program using plain Haskell lists. On the other hand, provided fusion works, the resulting code will run as fast as an equivalent cleanly written C program. Making fusion work is not hard once you understand what's going on. 

−  +  == The <code>force</code> function has the loops == 

Suppose we have the following binding: 
Suppose we have the following binding: 

Line 785:  Line 782:  
Importantly, the code that does the allocation, iteration and update is defined as part of the <code>force</code> function. This forcing code has been written to break up the result into several chunks, and evaluate each chunk with a different thread. This is what makes your code run in parallel. If you do ''not'' use <code>force</code> then your code will be slow and ''not'' run in parallel. 
Importantly, the code that does the allocation, iteration and update is defined as part of the <code>force</code> function. This forcing code has been written to break up the result into several chunks, and evaluate each chunk with a different thread. This is what makes your code run in parallel. If you do ''not'' use <code>force</code> then your code will be slow and ''not'' run in parallel. 

−  +  == Delayed and Manifest arrays == 

In the example from the previous section, think of the <code>R.map (\x > x + 1) arr</code> expression as a ''specification'' for a new array. In the library, this specification is referred to as a ''delayed'' array. A delayed array is represented as a function that takes an array index, and computes the value of the element at that index. 
In the example from the previous section, think of the <code>R.map (\x > x + 1) arr</code> expression as a ''specification'' for a new array. In the library, this specification is referred to as a ''delayed'' array. A delayed array is represented as a function that takes an array index, and computes the value of the element at that index. 

Line 792:  Line 789:  
All Repa array operators will accept both delayed and manifest arrays. However, if you index into a delayed array without forcing it first, then each indexing operation costs a function call. It also ''recomputes'' the value of the array element at that index. 
All Repa array operators will accept both delayed and manifest arrays. However, if you index into a delayed array without forcing it first, then each indexing operation costs a function call. It also ''recomputes'' the value of the array element at that index. 

−  +  == Shells and Springs == 

Here is another way to think about Repa's approach to array fusion. Suppose we write the following binding: 
Here is another way to think about Repa's approach to array fusion. Suppose we write the following binding: 

Line 801:  Line 798:  
When GHC compiles this example, the two worker functions are fused into a fresh unfolding of the parallel loop defined in the code for <code>R.force</code>. Imagine holding <code>R.force</code> in your left hand, and squashing the calls to <code>R.map</code> into it, like a spring. Doing this breaks all the shells, and you end up with the worker functions fused into an unfolding of <code>R.force</code>. 
When GHC compiles this example, the two worker functions are fused into a fresh unfolding of the parallel loop defined in the code for <code>R.force</code>. Imagine holding <code>R.force</code> in your left hand, and squashing the calls to <code>R.map</code> into it, like a spring. Doing this breaks all the shells, and you end up with the worker functions fused into an unfolding of <code>R.force</code>. 

−  +  == INLINE worker functions == 

Consider the following example: 
Consider the following example: 

Line 820:  Line 817:  
arr' = R.force $ R.zipWith (*) (R.map f arr1) (R.map f arr2) 
arr' = R.force $ R.zipWith (*) (R.map f arr1) (R.map f arr2) 

−  +  = Advanced techniques = 

−  +  == Repa's parallel programming model == 

''Discussion about the gang threads and hooks to help'' 
''Discussion about the gang threads and hooks to help'' 

−  +  == Programming with stencils == 

''Discuss the stencil types model'' 
''Discuss the stencil types model'' 

−  
[[Category:Libraries]] 
[[Category:Libraries]] 
Revision as of 09:17, 5 October 2012
Note: This tutorial is for an old version of Repa. The current version (Repa 3.1) has a slightly different API. You can read more about Repa 3 in this paper.
Repa is a Haskell library for high performance, regular, multidimensional parallel arrays. All numeric data is stored unboxed and functions written with the Repa combinators are automatically parallel (provided you supply "+RTS N" on the command line when running the program).
This document provides a tutorial on array programming in Haskell using the repa package.
Note: a companion tutorial to this is provided as the vector tutorial, and is based on the NumPy tutorial.
Authors: Don Stewart.
Contents
Quick Tour
Repa (REgular PArallel arrays) is an advanced, multidimensional parallel arrays library for Haskell, with a number of distinct capabilities:
 The arrays are "regular" (i.e. dense, rectangular and store elements all of the same type); and
 Functions may be written that are polymorphic in the shape of the array;
 Many operations on arrays are accomplished by changing only the shape of the array (without copying elements);
 The library will automatically parallelize operations over arrays.
This is a quick start guide for the package. For further information, consult:
 The Haddock Documentation
 Regular, Shapepolymorphic, Parallel Arrays in Haskell.
 Efﬁcient Parallel Stencil Convolution in Haskell
Importing the library
Download the `repa` package:
$ cabal install repa
and import it qualified:
import qualified Data.Array.Repa as R
The library needs to be imported qualified as it shares the same function names as list operations in the Prelude.
Note: Operations that involve writing new index types for Repa arrays will require the 'XTypeOperators' language extension.
For noncore functionality, a number of related packages are available:
 repabytestring
 repaio
 repaalgorithms
 repadevil (image loading)
and example algorithms in:
Index types and shapes
Before we can get started manipulating arrays, we need a grasp of repa's notion of array shape. Much like the classic 'array' library in Haskell, repabased arrays are parameterized via a type which determines the dimension of the array, and the type of its index. However, while classic arrays take tuples to represent multiple dimensions, Repa arrays use a richer type language for describing multidimensional array indices and shapes (technically, a heterogeneous snoc list).
Shape types are built somewhat like lists. The constructor Z
corresponds
to a rank zero shape, and is used to mark the end of the list. The :.
constructor adds additional dimensions to the shape. So, for example, the shape:
(Z :. 3 :. 2 :. 3)
is the shape of a small 3D array, with shape type
(Z :. Int :. Int :. Int)
The most common dimensions are given by the shorthand names:
type DIM0 = Z
type DIM1 = DIM0 :. Int
type DIM2 = DIM1 :. Int
type DIM3 = DIM2 :. Int
type DIM4 = DIM3 :. Int
type DIM5 = DIM4 :. Int
thus,
Array U DIM2 Double
is the type of a twodimensional array of unboxed doubles, indexed via Int
keys, while
Array U Z Double
is a zerodimension object (i.e. a point) holding an unboxed Double.
Many operations over arrays are polymorphic in the shape / dimension
component. Others require operating on the shape itself, rather than
the array. A typeclass, Shape
, lets us operate uniformly
over arrays with different shape.
Building shapes
To build values of shape type, we can use the Z
and :.
constructors. Open the ghci and import Repa:
Prelude> :m +Data.Array.Repa
Repa> Z  the zerodimension
Z
For arrays of nonzero dimension, we must give a size. Note: a common error is to leave off the type of the size.
Repa> :t Z :. 10
Z :. 10 :: Num head => Z :. head
leading to annoying type errors about unresolved instances, such as:
No instance for (Shape (Z :. head0))
To select the correct instance, you will need to annotate the size literals with their concrete type:
Repa> :t Z :. (10 :: Int)
Z :. (10 :: Int) :: Z :. Int
is the shape of 1D arrays of length 10, indexed via Ints.
Given an array, you can always find its shape by calling extent
.
Additional convenience types for selecting particular parts of a shape are also provided (All, Any, Slice
etc.) which are covered later in the tutorial.
Working with shapes
That one key operation, extent
, gives us many attributes of an array:
 Extract the shape of the array
extent :: (Shape sh, Source r e) => Array r sh e > sh
So, given a 3x3x3 array, of type Array U DIM3 Int
, we can:
 build an array
Repa> let x :: Array U DIM3 Int; x = fromListUnboxed (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27]
Repa> :t x
x :: Array U DIM3 Int
 query the extent
Repa> extent x
((Z :. 3) :. 3) :. 3
 compute the rank (number of dimensions)
Repa> let sh = extent x
Repa> rank sh
3
 compute the size (total number of elements)
> size sh
27
 extract the elements of the array as a flat vector
Repa> toUnboxed x
fromList [1,2,3,4,5,6,7,8,9,10
,11,12,13,14,15,16,17,18,19
,20,21,22,23,24,25,26,27] :: Data.Vector.Unboxed.Base.Vector Int
Generating arrays
New repa arrays ("arrays" from here on) can be generated in many ways, and we always begin by importing the Data.Array.Repa
module:
$ ghci GHCi, version 7.4.1: http://www.haskell.org/ghc/ :? for help Loading package ghcprim ... linking ... done. Loading package integergmp ... linking ... done. Loading package base ... linking ... done. Prelude > :m + Data.Array.Repa
They may be constructed from lists, for example. Here is a one dimensional array of length 10, here, given the shape (Z :. 10)
:
Repa> let inputs = [1..10] :: [Double]
Repa> let x = fromListUnboxed (Z :. (10::Int)) inputs
Repa> x
AUnboxed (Z :. 10) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0])
The type of x
is inferred as:
Repa> :t x
x :: Array U (Z :. Int) Double
which we can read as "an array of dimension 1, indexed via Int
keys, holding elements of type Double
stored using unboxed vectors"
We could also have written the type as:
Repa> let x' = fromListUnboxed (Z :. 10 :: DIM1) inputs
Repa> :t x'
x' :: Array U DIM1 Double
The same data may also be treated as a two dimensional array, by changing the shape parameter:
Repa> let x2 = fromListUnboxed (Z :. (5::Int) :. (2::Int)) inputs
Repa> x2
AUnboxed ((Z :. 5) :. 2) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0])
which has the type:
Repa> :t x2
x2 :: Array U ((Z :. Int) :. Int) Double
or, as above, if we define it with the type synonym for 2 dimensional Int
 indexed arrays, DIM2
:
Repa> let x2' = fromListUnboxed (Z :. 5 :. 2 :: DIM2) inputs
Repa> x2'
AUnboxed ((Z :. 5) :. 2) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0])
Repa> :t x2'
x2' :: Array U DIM2 Double
Building arrays from vectors
It is also possible to build arrays from unboxed vectors, from the 'vector' package:
fromUnboxed :: (Shape sh, Unbox e) => sh > Vector e > Array U sh e
New arrays are built by applying a shape to the vector. For example:
Repa> :m + Data.Vector.Unboxed
Repa Unboxed> let x = fromUnboxed (Z :. (10::Int)) (enumFromN 0 10)
Repa Unboxed> x
AUnboxed (Z :. 10) (fromList [0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0])
is a onedimensional array of doubles. As usual, we can also impose other shapes:
Repa Unboxed> let x = fromUnboxed (Z :. (3::Int) :. (3::Int)) (enumFromN 0 9)
Repa Unboxed> x
AUnboxed ((Z :. 3) :. 3) (fromList [0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
Repa Unboxed> :t x
x :: Array U ((Z :. Int) :. Int) Double
to create a 3x3 array.
Generating random arrays
The repaalgorithms package lets us generate new arrays with random data:
 3d array of Ints, bounded between 0 and 255.
Repa Randomish> randomishIntArray (Z :. (3::Int) :. (3::Int) :. (3::Int)) 0 255 1
AUnboxed (((Z :. 3) :. 3) :. 3) (fromList [217,42,130,200,216,254,67,77,152,
85,140,226,179,71,23,17,152,84,47,17,45,5,88,245,107,214,136])
Reading arrays from files
Using the repaio package, arrays may be written and read from files in a number of formats:
 as BMP files; and
 in a number of text formats.
with other formats rapidly appearing. An example: to write an 2D array to an ascii file:
Repa> :m +Data.Array.Repa.IO.Matrix
Repa Matrix> let x = fromList (Z :. 5 :. 2 :: DIM2) [1..10]
Repa Matrix> writeMatrixToTextFile "test.dat" x
This will result in a file containing:
MATRIX 5 2 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0
In turn, this file may be read back in via readMatrixFromTextFile
.
Repa Matrix> xx < readMatrixFromTextFile "test.dat"
Repa Matrix> xx
AUnboxed ((Z :. 5) :. 2) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0])
Repa Matrix> :t xx
xx :: Array U DIM2 Double
To process .bmp files, use Data.Array.Repa.IO.BMP, as follows (currently reading only works for 24 bit .bmp):
Data.Array.Repa.IO.BMP> x < readImageFromBMP "/tmp/test24.bmp"
Reads this .bmp image:
as a 3D array of Word8
, which can be further processed.
For image IO in many, many formats, use the repadevil library.
Copying arrays from pointers
You can also generate new repa arrays by copying data from a pointer, using the repabytestring package. Here is an example, using copyFromPtrWord8
:
import Data.Word
import Foreign.Ptr
import qualified Data.Vector.Storable as V
import qualified Data.Array.Repa as R
import Data.Array.Repa
import qualified Data.Array.Repa.ByteString as R
import Data.Array.Repa.IO.DevIL
i, j, k :: Int
(i, j, k) = (255, 255, 4 {RGBA})
 1d vector, filled with pretty colors
v :: V.Vector Word8
v = V.fromList . take (i * j * k) . cycle $ concat
[ [ r, g, b, 255 ]
 r < [0 .. 255]
, g < [0 .. 255]
, b < [0 .. 255]
]
ptr2repa :: Ptr Word8 > IO (R.Array R.DIM3 Word8)
ptr2repa p = R.copyFromPtrWord8 (Z :. i :. j :. k) p
main = do
 copy our 1d vector to a repa 3d array, via a pointer
r < V.unsafeWith v ptr2repa
runIL $ writeImage "test.png" r
return ()
This fills a vector, converts it to a pointer, then copies that pointer to a 3d array, before writing the result out as this image:
Indexing arrays
To access elements in repa arrays, you provide an array and a shape, to access the element:
(!) :: (Shape sh, Elt a) => Array sh a > sh > a
Indices start with 0. So:
> let x = fromList (Z :. (10::Int)) [1..10]
> x ! (Z :. 2)
3.0
Note that we can't give just a bare literal as the shape, even for onedimensional arrays, :
> x ! 2
No instance for (Num (Z :. Int))
arising from the literal `2'
as the Z
type isn't in the Num
class, and Haskell's numeric literals are overloaded.
What if the index is out of bounds, though?
> x ! (Z :. 11)
*** Exception: ./Data/Vector/Generic.hs:222 ((!)): index out of bounds (11,10)
an exception is thrown. An alternative is to use indexing functions that return a Maybe
:
(!?) :: (Shape sh, Elt a) => Array sh a > sh > Maybe a
An example:
> x !? (Z :. 9)
Just 10.0
> x !? (Z :. 11)
Nothing
Operations on arrays
Besides indexing, there are many regular, listlike operations on arrays. Since many of the names parallel those in the Prelude, we import Repa qualified:
Repa> import qualified Data.Array.Repa as Repa
Maps, zips, filters and folds
We can map over multidimensional arrays:
Repa> let x = fromList (Z :. (3::Int) :. (3::Int)) [1..9] Repa> x [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0]
since this map
conflicts with the definition in the Prelude, we have to use it with the qualifier we requested:
Repa> Repa.map (^2) x [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]
Repa's map
leaves the dimension unchanged:
Repa> extent x (Z :. 3) :. 3 Repa> extent (Repa.map (^2) x) (Z :. 3) :. 3
A fold
reduces the inner dimension of the array:
fold :: (Shape sh, Elt a) => (a > a > a) > a > Array (sh :. Int) a > Array sh a
The x
defined above was a 2D array:
Repa> extent x (Z :. 3) :. 3
but if we sum each row:
Repa> Repa.fold (+) 0 x [6.0,15.0,24.0]
we get a 1D array instead:
Repa> extent (Repa.fold (+) 0 x) Z :. 3
Similarly, if y
is a (3 x 3 x 3) 3D array:
Repa> let y = fromList ((Z :. 3 :. 3 :. 3) :: DIM3) [1..27] Repa> y [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0,11.0,12.0,13.0,14.0,15.0,16.0,17.0,18.0,19.0,20.0,21.0,22.0,23.0,24.0,25.0,26.0,27.0]
we can fold over the inner dimension:
Repa> Repa.fold (+) 0 y [6.0,15.0,24.0,33.0,42.0,51.0,60.0,69.0,78.0]
yielding a 2D (3 x 3) array in place of our 3D (3 x 3 x 3) array:
Repa> extent y ((Z :. 3) :. 3) :. 3 Repa> extent (Repa.fold (+) 0 y) (Z :. 3) :. 3
Two arrays may be combined via zipWith
:
zipWith :: (Shape sh, Elt b, Elt c, Elt a) => (a > b > c) > Array sh a > Array sh b > Array sh c
an example:
Repa> Repa.zipWith (*) x x [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0] Repa> extent it (Z :. 3) :. 3
Repa> Repa.zipWith (*) y y [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0, 100.0,121.0,144.0,169.0,196.0,225.0,256.0,289.0,324.0, 361.0,400.0,441.0,484.0,529.0,576.0,625.0,676.0,729.0] reformatted Repa> extent it ((Z :. 3) :. 3) :. 3
Mapping, with indices
A very powerful operator is traverse
, a parallel array traversal which also supplies the current index:
traverse :: (Shape sh, Shape sh', Elt a)
=> Array sh a
 Source array
> (sh > sh')
 Function to produce the extent of the result.
> ((sh > a) > sh' > b)
 Function to produce elements of the result.
 It is passed a lookup function to
 get elements of the source.
> Array sh' b
This is quite a compicated type, because it is very general. Let's take it apart. The first argument is the source array, which is obvious. The second argument is a function that transforms the shape of the input array to yield the output array. So if the arrays are the same size, this function is id
. It might grow or resize the shape in other ways.
Finally, the 3rd argument is where the magic is. Given an index, return a new element, and you also get a lookup function which when applied yields the current element.
So we see this generalizes map to support indexes, and optional inspection of the current element. Let's try some examples:
$ ghci
GHCi, version 7.0.3: http://www.haskell.org/ghc/ :? for help
*> :m + Data.Array.Repa
*> :m + Data.Array.Repa.Algorithms.Randomish
*> let a :: Array DIM3 Int;
a = fromList (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27]
*> a
[1,2,3,4,5,6,7,8,9
,10,11,12,13,14,15,16,17,18
,19,20,21,22,23,24,25,26,27]
 Keeping the shape the same, and just overwriting elements
 Use `traverse` to set all elements to their `x` axis:
*> traverse a id (\_ (Z :. i :. j :. k) > i)
[0,0,0,0,0,0,0,0,0
,1,1,1,1,1,1,1,1,1
,2,2,2,2,2,2,2,2,2]
 Shuffle elements around, based on their index.
 Rotate elements by swapping elements from rotated locations:
> traverse a id (\f (Z :. i :. j :. k) > f (Z :. j :. k :. i))
[1,4,7,10,13,16,19,22,25
,2,5,8,11,14,17,20,23,26
,3,6,9,12,15,18,21,24,27]
The documentation on traverse provides further information.
Numeric operations: negation, addition, subtraction, multiplication
Repa arrays are instances of the Num
. This means that
operations on numerical elements are lifted automagically onto arrays of
such elements. For example, (+)
on two double values corresponds to
elementwise addition, (+)
, of the two arrays of doubles:
> let x = fromList (Z :. (10::Int)) [1..10] > x + x [2.0,4.0,6.0,8.0,10.0,12.0,14.0,16.0,18.0,20.0]
Other operations from the Num class work just as well:
> x [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0] > x ^ 3 [1.0,8.0,27.0,64.0,125.0,216.0,343.0,512.0,729.0,1000.0]
> x * x [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0,100.0]
Changing the shape of an array
One of the main advantages of repastyle arrays over other arrays in Haskell is the ability to reshape data without copying. This is achieved via *indexspace transformations*.
An example: transposing a 2D array (this example taken from the repa paper). First, the type of the transformation:
transpose2D :: Elt e => Array DIM2 e > Array DIM2 e
Note that this transform will work on DIM2 arrays holding any elements. Now, to swap rows and columns, we have to modify the shape:
transpose2D a = backpermute (swap e) swap a where e = extent a swap (Z :. i :. j) = Z :. j :. i
The swap function reorders the index space of the array. To do this, we extract the current shape of the array, and write a function that maps the index space from the old array to the new array. That index space function is then passed to backpermute which actually constructs the new array from the old one.
backpermute generates a new array from an old, when given the new shape, and a function that translates between the index space of each array (i.e. a shape transformer).
backpermute :: (Shape sh, Shape sh', Elt a) => sh' > (sh' > sh) > Array sh a > Array sh' a
Note that the array created is not actually evaluated (we only modified the index space of the array).
Transposition is such a common operation that it is provided by the library:
transpose :: (Shape sh, Elt a) => Array ((sh :. Int) :. Int) a > Array ((sh :. Int) :. Int)
the type indicate that it works on the lowest two dimensions of the array.
Other operations on index spaces include taking slices and joining arrays into larger ones.
Examples
Following are some examples of useful functions that exercise the API.
Example: Rotating an image with backpermute
Flip an image upside down:
import System.Environment
import Data.Word
import Data.Array.Repa hiding ((++))
import Data.Array.Repa.IO.DevIL
main = do
[f] < getArgs
runIL $ do
v < readImage f
writeImage ("flip"++f) (rot180 v)
rot180 :: Array DIM3 Word8 > Array DIM3 Word8
rot180 g = backpermute e flop g
where
e@(Z :. x :. y :. _) = extent g
flop (Z :. i :. j :. k) =
(Z :. x  i  1 :. y  j  1 :. k)
Running this:
$ ghc O2 make A.hs $ ./A haskell.jpg
Results in:
Example: matrixmatrix multiplication
A more advanced example from the Repa paper: matrixmatrix multiplication: the result of matrix multiplication is a matrix whose elements are found by multiplying the elements of each row from the first matrix by the associated elements of the same column from the second matrix and summing the result.
if and
then
So we take two, 2D arrays and generate a new array, using our transpose function from earlier:
mmMult :: (Num e, Elt e)
=> Array DIM2 e
> Array DIM2 e
> Array DIM2 e
mmMult a b = sum (zipWith (*) aRepl bRepl)
where
t = transpose2D b
aRepl = extend (Z :.All :.colsB :.All) a
bRepl = extend (Z :.rowsA :.All :.All) t
(Z :.colsA :.rowsA) = extent a
(Z :.colsB :.rowsB) = extent b
The idea is to expand both 2D argument arrays into 3D arrays by
replicating them across a new axis. The front face of the cuboid that
results represents the array a
, which we replicate as often
as b
has columns (colsB)
, producing
aRepl
.
The top face represents t
(the transposed b), which we
replicate as often as a has rows (rowsA)
, producing
bRepl,
. The two replicated arrays have the same extent,
which corresponds to the index space of matrix multiplication
Optimized implementations of this function are available in the repaalgorithms package.
Example: parallel image desaturation
To convert an image from color to greyscale, we can use the luminosity method to average RGB pixels into a common grey value, where the average is weighted for human perception of green.
The formula for luminosity is 0.21 R + 0.71 G + 0.07 B.
We can write a parallel image desaturation tool using repa and the repadevil image library:
import Data.Array.Repa.IO.DevIL
import Data.Array.Repa hiding ((++))
import Data.Word
import System.Environment

 Read an image, desaturate, write out with new name.

main = do
[f] < getArgs
runIL $ do
a < readImage f
let b = traverse a id luminosity
writeImage ("grey" ++ f) b
And now the luminosity transform itself, which averages the 3 RGB colors based on perceived weight:

 (Parallel) desaturation of an image via the luminosity method.

luminosity :: (DIM3 > Word8) > DIM3 > Word8
luminosity _ (Z :. _ :. _ :. 3) = 255  alpha channel
luminosity f (Z :. i :. j :. _) = ceiling $ 0.21 * r + 0.71 * g + 0.07 * b
where
r = fromIntegral $ f (Z :. i :. j :. 0)
g = fromIntegral $ f (Z :. i :. j :. 1)
b = fromIntegral $ f (Z :. i :. j :. 2)
And that's it! The result is a parallel image desaturator, when compiled with
$ ghc O threaded rtsopts make A.hs fforcerecomp
which we can run, to use two cores:
$ time ./A sunflower.png +RTS N2 H ./A sunflower.png +RTS N2 H 0.19s user 0.03s system 135% cpu 0.165 total
Given an image like this:
The desaturated result from Haskell:
Optimising Repa programs
Fusion, and why you need it
Repa depends critically on array fusion to achieve fast code. Fusion is a fancy name for the combination of inlining and code transformations performed by GHC when it compiles your program. The fusion process merges the array filling loops defined in the Repa library, with the "worker" functions that you write in your own module. If the fusion process fails, then the resulting program will be much slower than it needs to be, often 10x slower an equivalent program using plain Haskell lists. On the other hand, provided fusion works, the resulting code will run as fast as an equivalent cleanly written C program. Making fusion work is not hard once you understand what's going on.
The force
function has the loops
Suppose we have the following binding:
arr' = R.force $ R.map (\x > x + 1) arr
The right of this binding will compile down to code that first allocates the result array arr'
, then iterates over the source array arr
, reading each element in turn and adding one to it, then writing to the corresponding element in the result.
Importantly, the code that does the allocation, iteration and update is defined as part of the force
function. This forcing code has been written to break up the result into several chunks, and evaluate each chunk with a different thread. This is what makes your code run in parallel. If you do not use force
then your code will be slow and not run in parallel.
Delayed and Manifest arrays
In the example from the previous section, think of the R.map (\x > x + 1) arr
expression as a specification for a new array. In the library, this specification is referred to as a delayed array. A delayed array is represented as a function that takes an array index, and computes the value of the element at that index.
Applying force
to a delayed array causes all elements to be computed in parallel. The result of a force
is referred to as a manifest array. A manifest array is a "real" array represented as a flat chunk of memory containing array elements.
All Repa array operators will accept both delayed and manifest arrays. However, if you index into a delayed array without forcing it first, then each indexing operation costs a function call. It also recomputes the value of the array element at that index.
Shells and Springs
Here is another way to think about Repa's approach to array fusion. Suppose we write the following binding:
arr' = R.force $ R.map (\x > x * 2) $ R.map (\x > x + 1) arr
Remember from the previous section, that the result of each of the applications of R.map
is a delayed array. A delayed array is not a "real", manifest array, it's just a shell that contains a function to compute each element. In this example, the two worker functions correspond to the lambda expressions applied to R.map
.
When GHC compiles this example, the two worker functions are fused into a fresh unfolding of the parallel loop defined in the code for R.force
. Imagine holding R.force
in your left hand, and squashing the calls to R.map
into it, like a spring. Doing this breaks all the shells, and you end up with the worker functions fused into an unfolding of R.force
.
INLINE worker functions
Consider the following example:
f x = x + 1 arr' = R.force $ R.zipWith (*) (R.map f arr1) (R.map f arr2)
During compilation, we need GHC to fuse our worker functions into a fresh unfolding of R.force
. In this example, fusion includes inlining the definition of f
. If f
is not inlined, then the performance of the compiled code will be atrocious. It will perform a function call for each application of f
, where it really only needs a single machine instruction to increment the x
value.
Now, in general, GHC tries to avoid producing binaries that are "too big". Part of this is a heuristic that controls exactly what functions are inlined. The heuristic says that a function may be inlined only if it is used once, or if its definition is less than some particular size. If neither of these apply, then the function won't be inlined, killing performance.
For Repa programs, as fusion and inlining has such a dramatic effect on performance, we should absolutely not rely on heuristics to control whether or not this inlining takes place. If we rely on a heuristic, then even if our program runs fast today, if this heuristic is ever altered then some functions that used to be inlined may no longer be.
The moral of the story is to attach INLINE pragmas to all of your client functions that compute array values. This ensures that these critical functions will be inlined now, and forever.
{# INLINE f #} f x = x + 1
arr' = R.force $ R.zipWith (*) (R.map f arr1) (R.map f arr2)
Advanced techniques
Repa's parallel programming model
Discussion about the gang threads and hooks to help
Programming with stencils
Discuss the stencil types model