# Numeric Haskell: A Repa Tutorial

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 Revision as of 08:52, 4 October 2012 (edit) (→Index types and shapes: - updating to Repa 3.2)← Previous diff Current revision (10:40, 14 February 2013) (edit) (undo) (→Examples) (34 intermediate revisions not shown.) Line 1: Line 1: - 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 [http://www.cse.unsw.edu.au/~benl/papers/guiding/guiding-Haskell2012-sub.pdf this paper.] - [http://hackage.haskell.org/package/repa Repa] is a Haskell library for [http://hackage.haskell.org/package/repa Repa] is a Haskell library for high performance, regular, multi-dimensional parallel arrays. All high performance, regular, multi-dimensional parallel arrays. All Line 9: Line 7: [[Image:Grid.png|right]] [[Image:Grid.png|right]] - This document provides a tutorial on array programming in Haskell using the repa package. + This document provides a tutorial on array programming in Haskell using the Repa package and was based on version 3.2 of the library. Repa versions earlier than 3.0 use different API and are not compatible with this tutorial. ''Note: a companion tutorial to this is provided as the [http://www.haskell.org/haskellwiki/Numeric_Haskell:_A_Vector_Tutorial vector tutorial], and is based on the [http://www.scipy.org/Tentative_NumPy_Tutorial NumPy tutorial]''. ''Note: a companion tutorial to this is provided as the [http://www.haskell.org/haskellwiki/Numeric_Haskell:_A_Vector_Tutorial vector tutorial], and is based on the [http://www.scipy.org/Tentative_NumPy_Tutorial NumPy tutorial]''. - + - ''Authors: [http://donsbot.wordpress.com Don Stewart].'' + ''Authors: [http://donsbot.wordpress.com Don Stewart] (original version), [http://ics.p.lodz.pl/~stolarek Jan Stolarek] (update from Repa 2 to Repa 3).'' = Quick Tour = = Quick Tour = Line 27: Line 25: * [http://hackage.haskell.org/packages/archive/repa/2.0.0.3/doc/html/Data-Array-Repa.html The Haddock Documentation] * [http://hackage.haskell.org/packages/archive/repa/2.0.0.3/doc/html/Data-Array-Repa.html The Haddock Documentation] + * [http://www.cse.unsw.edu.au/~benl/papers/guiding/guiding-Haskell2012-sub.pdf ​Guiding Parallel Array Fusion with Indexed Types] - describes architecture of Repa 3 * [http://research.microsoft.com/en-us/um/people/simonpj/papers/ndp/rarrays.pdf Regular, Shape-polymorphic, Parallel Arrays in Haskell]. * [http://research.microsoft.com/en-us/um/people/simonpj/papers/ndp/rarrays.pdf Regular, Shape-polymorphic, Parallel Arrays in Haskell]. * [http://www.cse.unsw.edu.au/~benl/papers/stencil/stencil-icfp2011-sub.pdf Efﬁcient Parallel Stencil Convolution in Haskell] * [http://www.cse.unsw.edu.au/~benl/papers/stencil/stencil-icfp2011-sub.pdf Efﬁcient Parallel Stencil Convolution in Haskell] + * [http://repa.ouroborus.net/ Repa project homepage] - == Importing the library == + = Importing the library = Download the repa package: Download the repa package: Line 46: Line 46: For non-core functionality, a number of related packages are available: For non-core functionality, a number of related packages are available: - * [http://hackage.haskell.org/package/repa-bytestring repa-bytestring] * [http://hackage.haskell.org/package/repa-io repa-io] * [http://hackage.haskell.org/package/repa-io repa-io] * [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] * [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] Line 55: Line 54: * [http://hackage.haskell.org/package/repa-examples repa-examples] * [http://hackage.haskell.org/package/repa-examples repa-examples] - == Index types and shapes == + = Type indexes = + Repa 3 introduced a notion of ''type index'': + + data family Array rep sh e + + In the above definition rep is the type index, which is represented with a data type name and defines the type of underlying array representation. This allows to explicitly specify representation of an array on type system level. Repa distinguishes ten different types of representation, which fall into three categories: manifest, delayed and meta. Manifest arrays are represented by concrete values stored in memory using e.g. unboxed vectors (represented with data type U) or strict bytestrings (B). Delayed array (D) is a function from index to a value. Every time an element is requested from a delayed array it is calculated anew, which means that delayed arrays are inefficient when the data is needed multiple times. On the other hand delaying an array allows to perform optimisation by fusion. Meta arrays provide additional information about underlying array data, which can be used for load balancing or speeding up calculation of border effects. See [http://www.cse.unsw.edu.au/~benl/papers/guiding/guiding-Haskell2012-sub.pdf Guiding Parallel Array Fusion with Indexed Types] paper for a detailed explanation of each representation. + + = Array 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, repa-based 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/Data-Array-Repa-Shape.html#t:Shape richer type language] for describing multi-dimensional 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, repa-based 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/latest/doc/html/Data-Array-Repa-Shape.html#t:Shape richer type language] for describing multi-dimensional array indices and shapes (technically, a ''heterogeneous snoc list''). Shape types are built somewhat like lists. The constructor Z corresponds Shape types are built somewhat like lists. The constructor Z corresponds Line 98: Line 104: over arrays with different shape. over arrays with different shape. - === Building shapes === + == Building shapes == To build values of shape type, we can use the Z and :. constructors. Open the ghci and import Repa: To build values of shape type, we can use the Z and :. constructors. Open the ghci and import Repa: - Prelude> :m +Data.Array.Repa + ghci> :m +Data.Array.Repa - Repa> Z -- the zero-dimension + ghci> Z -- the zero-dimension Z Z Line 111: Line 117: - Repa> :t Z :. 10 + ghci> :t Z :. 10 Z :. 10 :: Num head => Z :. head Z :. 10 :: Num head => Z :. head Line 122: Line 128: - Repa> :t Z :. (10 :: Int) + ghci> :t Z :. (10 :: Int) Z :. (10 :: Int) :: Z :. Int Z :. (10 :: Int) :: Z :. Int Line 132: Line 138: Additional convenience types for selecting particular parts of a shape are also provided (All, Any, Slice etc.) which are covered later in the tutorial. 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 === + == Working with shapes == That one key operation, extent, gives us many attributes of an array: That one key operation, extent, gives us many attributes of an array: - -- Extract the shape of the array + -- Extract the shape of the array - extent :: Array sh a -> sh + extent :: (Shape sh, Source r e) => Array r sh e -> sh - So, given a 3x3x3 array, of type Array DIM3 Int, we can: + So, given a 3x3x3 array, of type Array U DIM3 Int, we can: -- build an array -- build an array - Repa> let x :: Array DIM3 Int; x = fromList (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27] + ghci> let x :: Array U DIM3 Int; x = fromListUnboxed (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27] - Repa> :t x + ghci> :t x - x :: Array DIM3 Int + x :: Array U DIM3 Int -- query the extent -- query the extent - Repa> extent x + ghci> extent x ((Z :. 3) :. 3) :. 3 ((Z :. 3) :. 3) :. 3 -- compute the rank (number of dimensions) -- compute the rank (number of dimensions) - Repa> let sh = extent x + ghci> let sh = extent x - Repa> rank sh + ghci> rank sh 3 3 Line 163: Line 169: -- extract the elements of the array as a flat vector -- extract the elements of the array as a flat vector - Repa> toVector x + ghci> toUnboxed x fromList [1,2,3,4,5,6,7,8,9,10 fromList [1,2,3,4,5,6,7,8,9,10 ,11,12,13,14,15,16,17,18,19 ,11,12,13,14,15,16,17,18,19 - ,20,21,22,23,24,25,26,27] :: Data.Vector.Unboxed.Vector + ,20,21,22,23,24,25,26,27] :: Data.Vector.Unboxed.Base.Vector Int - == Generating arrays == + = 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: 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 - GHCi, version 7.0.3: http://www.haskell.org/ghc/ :? for help + GHCi, version 7.4.1: http://www.haskell.org/ghc/ :? for help Loading package ghc-prim ... linking ... done. Loading package ghc-prim ... linking ... done. Loading package integer-gmp ... linking ... done. Loading package integer-gmp ... linking ... done. Loading package base ... linking ... done. Loading package base ... linking ... done. - Loading package ffi-1.0 ... linking ... done. + ghci> :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 (Z :. 10): 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] + ghci> let inputs = [1..10] :: [Double] - Repa> let x = fromList (Z :. (10::Int)) inputs + ghci> let x = fromListUnboxed (Z :. (10::Int)) inputs - Repa> x + ghci> x - [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0] + AUnboxed (Z :. 10) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]) Line 195: Line 199: - Repa> :t x + ghci> :t x - x :: Array (Z :. Int) Double + 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" + 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: We could also have written the type as: - Repa> let x' = fromList (Z :. 10 :: DIM1) inputs + ghci> let x' = fromListUnboxed (Z :. 10 :: DIM1) inputs - Repa> :t x' + ghci> :t x' - x' :: Array DIM1 Double + x' :: Array U DIM1 Double Line 212: Line 216: - Repa> let x2 = fromList (Z :. (5::Int) :. (2::Int)) inputs + ghci> let x2 = fromListUnboxed (Z :. (5::Int) :. (2::Int)) inputs - Repa> x2 + ghci> x2 - [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0] + 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]) Line 220: Line 224: - Repa> :t x2 + ghci> :t x2 - x2 :: Array ((Z :. Int) :. Int) Double + x2 :: Array U ((Z :. Int) :. Int) Double Line 227: Line 231: - Repa> let x2' = fromList (Z :. 5 :. 2 :: DIM2) inputs + ghci> let x2' = fromListUnboxed (Z :. 5 :. 2 :: DIM2) inputs - Repa> x2' + ghci> x2' - [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0] + 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' + ghci> :t x2' - x2' :: Array DIM2 Double + x2' :: Array U DIM2 Double + - === Building arrays from vectors === + == 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: - fromVector :: Shape sh => sh -> Vector a -> Array sh a + fromUnboxed :: (Shape sh, Unbox e) => sh -> Vector e -> Array U sh e Line 245: Line 250: - Repa> import Data.Vector.Unboxed --recent ghci's support this import syntax + ghci> :m + Data.Vector.Unboxed - Repa Unboxed> let x = fromVector (Z :. (10::Int)) (enumFromN 0 10) + ghci> let x = fromUnboxed (Z :. (10::Int)) (enumFromN 0 10) - [0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0] + ghci> 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]) Line 254: Line 260: - Repa Unboxed> let x = fromVector (Z :. (3::Int) :. (3::Int)) (enumFromN 0 9) + ghci> let x = fromUnboxed (Z :. (3::Int) :. (3::Int)) (enumFromN 0 9) - Repa Unboxed> x + ghci> x - [0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0] + 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 + ghci> :t x - x :: Array ((Z :. Int) :. Int) Double + x :: Array U ((Z :. Int) :. Int) Double to create a 3x3 array. to create a 3x3 array. - === Generating random arrays === + == Generating random arrays == The [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] package lets us generate new arrays with random data: The [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] package lets us generate new arrays with random data: Line 269: Line 275: -- 3d array of Ints, bounded between 0 and 255. -- 3d array of Ints, bounded between 0 and 255. - Repa> :m +Data.Array.Repa.Algorithms.Randomish + ghci> randomishIntArray (Z :. (3::Int) :. (3::Int) :. (3::Int)) 0 255 1 - 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, - [217,42,130,200,216,254 + 85,140,226,179,71,23,17,152,84,47,17,45,5,88,245,107,214,136]) - ,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 === + == Reading arrays from files == Using the [http://hackage.haskell.org/package/repa-io repa-io] package, arrays may be written and read from files in a number of formats: Using the [http://hackage.haskell.org/package/repa-io repa-io] package, arrays may be written and read from files in a number of formats: Line 284: Line 287: * in a number of text formats. * in a number of text formats. - with other formats rapidly appearing. For the special case of arrays of Word8 values, the [http://hackage.haskell.org/package/repa-bytestring repa-bytestring] library supports generating bytestrings in memory. + with other formats rapidly appearing. An example: to write an 2D array to an ascii file: - + - An example: to write an 2D array to an ascii file: + - Repa> :m +Data.Array.Repa.IO.Matrix + ghci> :m +Data.Array.Repa.IO.Matrix - Repa Matrix> let x = fromList (Z :. 5 :. 2 :: DIM2) [1..10] + ghci> let x = fromList (Z :. 5 :. 2 :: DIM2) [1..10] - Repa Matrix> writeMatrixToTextFile "test.dat" x + ghci> writeMatrixToTextFile "test.dat" x Line 297: Line 298: MATRIX MATRIX - 2 5 + 5 2 1.0 1.0 2.0 2.0 Line 312: Line 313: - Repa Matrix> xx <- readMatrixFromTextFile "test.dat" + ghci> xx <- readMatrixFromTextFile "test.dat" - Repa Matrix> xx + ghci> xx - [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0] + 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 + ghci> :t xx - it :: Array DIM2 Double + xx :: Array U DIM2 Double - To process [http://en.wikipedia.org/wiki/BMP_file_format .bmp files], use [http://hackage.haskell.org/packages/archive/repa-io/2.0.0.3/doc/html/Data-Array-Repa-IO-BMP.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/repa-io/2.0.0.3/doc/html/Data-Array-Repa-IO-BMP.html Data.Array.Repa.IO.BMP], as follows (currently reading only works for 24 bit .bmp): Line 331: Line 331: as a 3D array of Word8, which can be further processed. as a 3D array of Word8, which can be further processed. - - ''Note'': at the time of writing, there are no binary instances for repa arrays. For image IO in many, many formats, use the [http://hackage.haskell.org/package/repa-devil repa-devil] library. For image IO in many, many formats, use the [http://hackage.haskell.org/package/repa-devil repa-devil] library. - === Copying arrays from pointers === + == Copying arrays from pointers == - You can also generate new repa arrays by copying data from a pointer, using the [http://hackage.haskell.org/package/repa-bytestring repa-bytestring] package. Here is an example, using copyFromPtrWord8: + You can also generate new repa arrays by copying data from a pointer. Here is an example, using fromForeignPtr: import Data.Word import Data.Word import Foreign.Ptr import Foreign.Ptr + import qualified Foreign.ForeignPtr.Safe as FPS - import qualified Data.Vector.Storable as V + import qualified Data.Vector.Storable as V - import qualified Data.Array.Repa as R + import qualified Data.Array.Repa as R + import qualified Data.Array.Repa.Repr.ForeignPtr as RFP import Data.Array.Repa import Data.Array.Repa - import qualified Data.Array.Repa.ByteString as R import Data.Array.Repa.IO.DevIL import Data.Array.Repa.IO.DevIL - i, j, k :: Int + i, j, k :: Int - (i, j, k) = (255, 255, 4 {-RGBA-}) + (i, j, k) = (255, 255, 4) -- RGBA - -- 1d vector, filled with pretty colors + -- 1D vector, filled with pretty colors v :: V.Vector Word8 v :: V.Vector Word8 v = V.fromList . take (i * j * k) . cycle $concat v = V.fromList . take (i * j * k) . cycle$ concat Line 361: Line 360: , g <- [0 .. 255] , g <- [0 .. 255] , b <- [0 .. 255] , b <- [0 .. 255] - ] + ] - ptr2repa :: Ptr Word8 -> IO (R.Array R.DIM3 Word8) + ptr2repa :: Ptr Word8 -> IO (R.Array RFP.F R.DIM3 Word8) - ptr2repa p = R.copyFromPtrWord8 (Z :. i :. j :. k) p + ptr2repa p = do + fp <- FPS.newForeignPtr_ p + return $RFP.fromForeignPtr (Z :. i :. j :. k) fp main = do main = do -- copy our 1d vector to a repa 3d array, via a pointer -- copy our 1d vector to a repa 3d array, via a pointer r <- V.unsafeWith v ptr2repa r <- V.unsafeWith v ptr2repa - runIL$ writeImage "test.png" r + runIL $writeImage "test.png" (RGBA r) return () 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: + The v function creates a vector filled with pixel data (RGBA values); ptr2repa takes a Ptr, converts it to ForeignPtr which is then used to copy data into a 3D REPA array (first two dimensions correspond to pixel location, the third dimension corresponds to colour channel). In the main function we use unsafeWith function operating on storable vectors. It passes Ptr to vector's internal data to a function that operates on that Ptr (it may not modify the data). In our case this is the ptr2repa array, which returns image data wrapped in an array. That array is then passed as data to writeImage function. RGBA is a data constructor for Image type. This allows writeImage to interpret array data correctly. runIL is a wrapper around IO monad that guarantees that IL library will be properly initialized. The result is written to disk as this image: http://i.imgur.com/o0Cv2.png http://i.imgur.com/o0Cv2.png - == Indexing arrays == + = 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: - (!) :: (Shape sh, Elt a) => Array sh a -> sh -> a + (!) :: (Shape sh, Source r e) => Array r sh e -> sh -> e Line 388: Line 389: - > let x = fromList (Z :. (10::Int)) [1..10] + ghci> let x = fromListUnboxed (Z :. (10::Int)) [1..10] - > x ! (Z :. 2) + ghci> x ! (Z :. 2) 3.0 3.0 - Note that we can't give just a bare literal as the shape, even for one-dimensional arrays, : + Note that we can't give just a bare literal as the shape, even for one-dimensional arrays: Line 408: Line 409: > x ! (Z :. 11) > x ! (Z :. 11) - *** Exception: ./Data/Vector/Generic.hs:222 ((!)): index out of bounds (11,10) + *** Exception: ./Data/Vector/Generic.hs:244 ((!)): index out of bounds (11,10) - an exception is thrown. An alternative is to use indexing functions that return a Maybe: + An exception is thrown. Older versions of repa offered safe indexing operator (!?) that returned Maybe, but it is no longer available in repa 3.2. + + = Operations on arrays = + + Besides indexing, there are many regular, list-like operations on arrays. Since many of the names parallel those in the Prelude, we import Repa qualified: + + ghci> import qualified Data.Array.Repa as Repa + + == Maps, zips, filters and folds == + + Let us define an unboxed array: - (!?) :: (Shape sh, Elt a) => Array sh a -> sh -> Maybe a + ghci> let x = fromListUnboxed (Z :. (3::Int) :. (3::Int)) [1..9] + ghci> x + AUnboxed ((Z :. 3) :. 3) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0]) - An example: + We can map over multi-dimensional arrays with map, but since it conflicts with the definition in the Prelude, we have to use it with the qualifier we requested: - > x !? (Z :. 9) + ghci> let y = Repa.map (^2) x - Just 10.0 + - > x !? (Z :. 11) + The type of Repa.map is: - Nothing + + + map :: (Shape sh, Source r a) => + (a -> b) -> Array r sh a -> Array D sh b - == Operations on arrays == + This means that result is a delayed array (represented by letter D in the return type). We can index into delayed array (remember that indices are 0-based): - Besides indexing, there are many regular, list-like operations on arrays. Since many of the names parallel those in the Prelude, we import Repa qualified: + + ghci> y Repa.! (Z :. 0 :. 2) + 9.0 + ghci> y Repa.! (Z :. 2 :. 0) + 49.0 + - Repa> import qualified Data.Array.Repa as Repa + Passing an incorrect index will NOT cause an exception: - === Maps, zips, filters and folds === + + ghci> y Repa.! (Z :. 0 :. 3) + 16.0 + ghci> y Repa.! (Z :. 0 :. -4) + 0.0 + ghci> y Repa.! (Z :. 10 :. 10) + 0.0 + - We can map over multi-dimensional arrays: + Since delayed arrays are not instances of Show type class, we cannot display the whole array directly: - Repa> let x = fromList (Z :. (3::Int) :. (3::Int)) [1..9] + - Repa> x + ghci> y - [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0] + No instance for (Show (Array D ((Z :. Int) :. Int) Double)) + arising from a use of print' + - since this map conflicts with the definition in the Prelude, we have to use it with the qualifier we requested: + To display y array it must be turned into manifest array by computing all its values. This can be done either with computeP, which evaluates array in parallel, or computeS, which evaluates values sequentially. Looking at the type of computeP: - Repa> Repa.map (^2) x + - [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0] + ghci> :t computeP + computeP + :: (Monad m, Source r2 e, Load r1 sh e, Target r2 e) => + Array r1 sh e -> m (Array r2 sh e) + + + we can see that the result is enclosed within a monad. The reason for this is that monads give a well defined notion of sequence and thus computeP enforces completion of parallel evaluation in a particular point of monadic computations. This prevents nested data parallelism, which is not supported by Repa. We don't really care here about monadic effect, so we can take the manifest array out of a monad after computeP is done: + + + ghci> computeP y :: IO (Array U DIM2 Double) + AUnboxed ((Z :. 3) :. 3) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]) + ghci> z <- computeP y :: IO (Array U DIM2 Double) + ghci> z + AUnboxed ((Z :. 3) :. 3) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]) + ghci> let [w] = computeP y :: [Array U DIM2 Double] + ghci> w + AUnboxed ((Z :. 3) :. 3) (fromList [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's map leaves the dimension unchanged: - Repa> extent x + - (Z :. 3) :. 3 + ghci> extent x - Repa> extent (Repa.map (^2) x) + (Z :. 3) :. 3 - (Z :. 3) :. 3 + ghci> extent y + (Z :. 3) :. 3 + - A fold reduces the inner dimension of the array: + There are four fold operations in Repa library: foldP, foldS, foldAllP and foldAllS. First two reduce the the inner dimension of the array, the last two reduce whole array to one value. The P and S suffixes stand for parallel and sequential evaluation. First parameter of parallel folds must be an associative sequential operator and the starting element must be a neutral element of that operator. This is required in order to ensure correctness of parallel evaluation. - fold :: (Shape sh, Elt a) + Type of foldP is given as: - => (a -> a -> a) -> a -> Array (sh :. Int) a -> Array sh a + + + foldP :: (Monad m, Shape sh, Source r a, Unbox a, Elt a) => + (a -> a -> a) -> a -> Array r (sh :. Int) a -> m (Array U sh a) + The x defined above was a 2D array: The x defined above was a 2D array: - Repa> extent x + - (Z :. 3) :. 3 + ghci> extent x + (Z :. 3) :. 3 + but if we sum each row: but if we sum each row: - Repa> Repa.fold (+) 0 x + - [6.0,15.0,24.0] + ghci> Repa.foldP (+) 0 x + AUnboxed (Z :. 3) (fromList [6.0,15.0,24.0]) + - we get a 1D array instead: + we get a 1D array instead (liftM is used to run extent function within a monad returned by foldP): - Repa> extent (Repa.fold (+) 0 x) + - Z :. 3 + ghci> liftM extent (Repa.foldP (+) 0 x) + Z :. 3 + - Similarly, if y is a (3 x 3 x 3) 3D array: + Similarly, if y is a (3 x 3 x 2) 3D array: - Repa> let y = fromList ((Z :. 3 :. 3 :. 3) :: DIM3) [1..27] + - Repa> y + ghci> let y = fromListUnboxed ((Z :. 3 :. 3 :. 2) :: DIM3) [1..18] - [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] + ghci> y + AUnboxed (((Z :. 3) :. 3) :. 2) (fromList [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]) + we can fold over the inner dimension: 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] + ghci> Repa.foldP (+) 0 y + AUnboxed ((Z :. 3) :. 3) (fromList [3.0,7.0,11.0,15.0,19.0,23.0,27.0,31.0,35.0]) + - yielding a 2D (3 x 3) array in place of our 3D (3 x 3 x 3) array: + 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: 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 + zipWith :: (Shape sh, Source r2 b, Source r1 a) => + (a -> b -> c) -> Array r1 sh a -> Array r2 sh b -> Array D sh c + an example: 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] + ghci> let y = Repa.zipWith (*) x x - Repa> extent it + ghci> computeP y :: IO (Array U DIM2 Double) - (Z :. 3) :. 3 + AUnboxed ((Z :. 3) :. 3) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]) + ghci> extent y + (Z :. 3) :. 3 + - Repa> Repa.zipWith (*) y y + If the extent of the two array arguments differ, then the resulting array's extent is their intersection. - [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 === + == Mapping, with indices == - A very powerful operator is traverse, a parallel array traversal which also supplies the current index: + A very powerful operator is traverse, an array transformation which also supplies the current index: - traverse :: (Shape sh, Shape sh', Elt a) + traverse :: (Shape sh', Shape sh, Source r a) - => Array sh a + => Array r sh a -- Source array - -- Source array + -> (sh -> sh') -- Function to produce the extent of the result. - -> (sh -> sh') + -> ((sh -> a) -> sh' -> b) -- Function to produce elements of the result. - -- Function to produce the extent of the result. + -- It is passed a lookup function to - -> ((sh -> a) -> sh' -> b) + -- get elements of the source. - -- Function to produce elements of the result. + -> Array D sh' b - -- 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. + This is quite a complicated 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. + Finally, the 3rd argument is where the magic is. It is a function (often anonymous one) that takes two parameters: a function that looks up elements of an original array and a location of the currently processed element in the new array. Based on these two parameters a new value is determined for the resulting array. Note that traverse goes through all elements of the resulting array and it is up to you how to get values of original array based on an index of the new array. + + traverse returns a delayed array, which means we will use computeP to evaluate its values and display them on the screen. So we see this generalizes map to support indexes, and optional inspection of the current element. Let's try some examples: So we see this generalizes map to support indexes, and optional inspection of the current element. Let's try some examples: -$ ghci + ghci> let x = fromListUnboxed (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27] :: Array U DIM3 Int - GHCi, version 7.0.3: http://www.haskell.org/ghc/ :? for help + ghci> x - *> :m + Data.Array.Repa + AUnboxed (((Z :. 3) :. 3) :. 3) (fromList [1,2,3,4,5,6,7,8,9, - *> :m + Data.Array.Repa.Algorithms.Randomish + 10,11,12,13,14,15,16,17,18, - *> let a :: Array DIM3 Int; + 19,20,21,22,23,24,25,26,27]) - 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 -- Keeping the shape the same, and just overwriting elements -- Use traverse to set all elements to their x axis: -- Use traverse to set all elements to their x axis: - *> traverse a id (\_ (Z :. i :. j :. k) -> i) + ghci> computeP $traverse x id (\_ (Z :. i :. j :. k) -> i) :: IO (Array U DIM3 Int) - [0,0,0,0,0,0,0,0,0 + AUnboxed (((Z :. 3) :. 3) :. 3) (fromList [0,0,0,0,0,0,0,0,0, - ,1,1,1,1,1,1,1,1,1 + 1,1,1,1,1,1,1,1,1, - ,2,2,2,2,2,2,2,2,2] + 2,2,2,2,2,2,2,2,2]) -- Shuffle elements around, based on their index. -- Shuffle elements around, based on their index. -- Rotate elements by swapping elements from rotated locations: -- Rotate elements by swapping elements from rotated locations: - > traverse a id (\f (Z :. i :. j :. k) -> f (Z :. j :. k :. i)) + ghci> computeP$ traverse x id (\f (Z :. i :. j :. k) -> f (Z :. j :. k :. i)) :: IO (Array U DIM3 Int) - [1,4,7,10,13,16,19,22,25 + AUnboxed (((Z :. 3) :. 3) :. 3) (fromList [1,4,7,10,13,16,19,22,25, - ,2,5,8,11,14,17,20,23,26 + 2,5,8,11,14,17,20,23,26, - ,3,6,9,12,15,18,21,24,27] + 3,6,9,12,15,18,21,24,27]) + + -- Take a slice of one dimension. The resulting shape of the array changes + ghci> computeP $traverse x (\(e :. _) -> e) (\f (Z :. i :. j) -> f (Z :. i :. j :. 0)) :: IO (Array U DIM2 Int) + AUnboxed ((Z :. 3) :. 3) (fromList [1,4,7,10,13,16,19,22,25]) - The documentation on [http://hackage.haskell.org/packages/archive/repa/2.0.2.1/doc/html/Data-Array-Repa.html#g:7 traverse] provides further information. + The documentation on [http://hackage.haskell.org/packages/archive/repa/latest/doc/html/Data-Array-Repa.html#v:traverse traverse] provides further information. - === Numeric operations: negation, addition, subtraction, multiplication === + == Numeric operations: negation, addition, subtraction, multiplication == - Repa arrays are instances of the Num. This means that + To perform basic numeric operations on Repa arrays you must use specialized operators: (+^) (addition), (-^) (subtraction), (*^) (multiplication) and (/^) (division). All these operators work element-wise and produce delayed arrays (they have to be computed if you want to have results as unboxed arrays). - operations on numerical elements are lifted automagically onto arrays of + - such elements. For example, (+) on two double values corresponds to + - element-wise addition, (+), of the two arrays of doubles: + - > let x = fromList (Z :. (10::Int)) [1..10] + - > x + x + ghci> let x = fromListUnboxed (Z :. (10 :: Int)) [1..10] - [2.0,4.0,6.0,8.0,10.0,12.0,14.0,16.0,18.0,20.0] + ghci> computeP$ x +^ x :: IO (Array U DIM1 Double) + AUnboxed (Z :. 10) (fromList [2.0,4.0,6.0,8.0,10.0,12.0,14.0,16.0,18.0,20.0]) + ghci> computeP $x -^ x :: IO (Array U DIM1 Double) + AUnboxed (Z :. 10) (fromList [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0]) + ghci> computeP$ x *^ x :: IO (Array U DIM1 Double) + AUnboxed (Z :. 10) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0,100.0]) + ghci> computeP $x /^ x :: IO (Array U DIM1 Double) + AUnboxed (Z :. 10) (fromList [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]) + - Other operations from the Num class work just as well: + = Changing the shape of an array = - + - > -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 repa-style arrays over other arrays in One of the main advantages of repa-style arrays over other arrays in Line 590: Line 640: via *index-space transformations*. via *index-space transformations*. - An example: transposing a 2D array (this example taken from the repa + An example: transposing a 2D array. First, the type of the transformation: - paper). First, the type of the transformation: + - transpose2D :: Elt e => Array DIM2 e -> Array DIM2 e + + transpose2D :: (Source r e) => Array r DIM2 e -> Array D DIM2 e + Note that this transform will work on DIM2 arrays holding any elements. Note that this transform will work on DIM2 arrays holding any elements. Now, to swap rows and columns, we have to modify the shape: Now, to swap rows and columns, we have to modify the shape: - transpose2D a = backpermute (swap e) swap a + - where + transpose2D a = backpermute (swap e) swap a - e = extent a + where - swap (Z :. i :. j) = Z :. j :. i + e = extent a + swap (Z :. i :. j) = Z :. j :. i + The swap function reorders the index space of the array. 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 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 + 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. - 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 + 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). - function that translates between the index space of each array (i.e. a shape + - transformer). + - backpermute + - :: (Shape sh, Shape sh', Elt a) + backpermute :: (Shape sh2, Shape sh1, Source r e) => - => sh' + sh2 -> (sh2 -> sh1) -> Array r sh1 e -> Array D sh2 e - -> (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). Note that the array created is not actually evaluated (we only modified the index space of the array). Line 625: Line 672: library: library: - transpose :: (Shape sh, Elt a) + - => Array ((sh :. Int) :. Int) a -> Array ((sh :. Int) :. Int) + transpose :: (Shape sh, Source r e) => + Array r ((sh :. Int) :. Int) e -> Array D ((sh :. Int) :. Int) e + - the type indicate that it works on the lowest two dimensions of the + The types indicate that it works on the lowest two dimensions of the array. array. - Other operations on index spaces include taking slices and joining + Arrays shape can be changed without altering the underlying data by using reshape: - arrays into larger ones. + - == Examples == + + ghci> let x = fromListUnboxed (Z :. (5 :: Int) :. (2 :: Int)) [1..10] + ghci> x + 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]) + ghci> computeP$ reshape (Z :. (2::Int) :. (5::Int)) x :: IO (Array U DIM2 Double) + AUnboxed ((Z :. 2) :. 5) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]) + + + = 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 === + == Example: Rotating an image with backpermute == Flip an image upside down: Flip an image upside down: + import Foreign.Ptr import System.Environment import System.Environment import Data.Word import Data.Word import Data.Array.Repa hiding ((++)) import Data.Array.Repa hiding ((++)) import Data.Array.Repa.IO.DevIL import Data.Array.Repa.IO.DevIL + import Data.Array.Repa.Repr.ForeignPtr + main :: IO () main = do main = do [f] <- getArgs [f] <- getArgs - runIL $do + (RGB v) <- runIL$ readImage f - v <- readImage f + rotated <- (computeP $rot180 v) :: IO (Array F DIM3 Word8) - writeImage ("flip-"++f) (rot180 v) + runIL$ writeImage ("flip-"++f) (RGB rotated) - + - rot180 :: Array DIM3 Word8 -> Array DIM3 Word8 + rot180 :: (Source r e) => Array r DIM3 e -> Array D DIM3 e rot180 g = backpermute e flop g rot180 g = backpermute e flop g where where e@(Z :. x :. y :. _) = extent g e@(Z :. x :. y :. _) = extent g - flop (Z :. i :. j :. k) = flop (Z :. i :. j :. k) = (Z :. x - i - 1 :. y - j - 1 :. k) (Z :. x - i - 1 :. y - j - 1 :. k) Line 665: Line 723: Running this: Running this: - $ghc -O2 --make A.hs +$ ghc -O2 --make -threaded -rtsopts repa.hs - $./A haskell.jpg +$ ./repa "haskell.jpg" +RTS -N2 Results in: Results in: Line 672: Line 730: http://i.imgur.com/YsGA8.jpg http://i.imgur.com/YsGA8.jpg - === Example: matrix-matrix multiplication === + == Example: matrix-matrix multiplication == - A more advanced example from the Repa paper: matrix-matrix multiplication: the result of + A more advanced example from the Repa paper is matrix-matrix 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. - 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 $A=\begin{bmatrix}a&b\\c&d\end{bmatrix}$ and $B=\begin{bmatrix}e&f\\g&h\end{bmatrix}$ if $A=\begin{bmatrix}a&b\\c&d\end{bmatrix}$ and $B=\begin{bmatrix}e&f\\g&h\end{bmatrix}$ Line 690: Line 744: - mmMult :: (Num e, Elt e) + mmMult :: Monad m - => Array DIM2 e + => Array U DIM2 Double - -> Array DIM2 e + -> Array U DIM2 Double - -> Array DIM2 e + -> m (Array U DIM2 Double) - + mmMult a b = sumP (Repa.zipWith (*) aRepl bRepl) - mmMult a b = sum (zipWith (*) aRepl bRepl) + where - where + t = transpose2D b - t = transpose2D b + aRepl = extend (Z :.All :.colsB :.All) a - aRepl = extend (Z :.All :.colsB :.All) a + bRepl = extend (Z :.rowsA :.All :.All) t - bRepl = extend (Z :.rowsA :.All :.All) t + (Z :.colsA :.rowsA) = extent a - (Z :.colsA :.rowsA) = extent a + (Z :.colsB :.rowsB) = extent b - (Z :.colsB :.rowsB) = extent b + The idea is to expand both 2D argument arrays into 3D arrays by 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 + replicating them across a new axis using extend function. The front face of the cuboid that results represents the array a, which we replicate as often as b has columns (colsB), producing aRepl. - 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 The top face represents t (the transposed b), which we replicate as often as a has rows (rowsA), producing replicate as often as a has rows (rowsA), producing bRepl,. The two replicated arrays have the same extent, bRepl,. The two replicated arrays have the same extent, - which corresponds to the index space of matrix multiplication + which corresponds to the index space of matrix multiplication. Optimized implementations of this function are available in the Optimized implementations of this function are available in the [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] package. [http://hackage.haskell.org/package/repa-algorithms repa-algorithms] package. - === Example: parallel image desaturation === + == 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 727: Line 777: - import Data.Array.Repa.IO.DevIL - import Data.Array.Repa hiding ((++)) - import Data.Word import System.Environment import System.Environment + import Data.Word + import Data.Array.Repa hiding ((++)) + import Data.Array.Repa.IO.DevIL + import Data.Array.Repa.Repr.ForeignPtr -- -- -- Read an image, desaturate, write out with new name. -- Read an image, desaturate, write out with new name. -- -- - main = do + main = do [f] <- getArgs [f] <- getArgs runIL $do runIL$ do - a <- readImage f + (RGB a) <- readImage f - let b = traverse a id luminosity + b <- (computeP $traverse a id luminosity) :: IL (Array F DIM3 Word8) - writeImage ("grey-" ++ f) b + writeImage ("grey-" ++ f) (RGB b) Line 760: Line 811: And that's it! The result is a parallel image desaturator, when compiled with And that's it! The result is a parallel image desaturator, when compiled with -$ ghc -O -threaded -rtsopts --make A.hs -fforce-recomp + $ghc -O -threaded -rtsopts --make repa.hs which we can run, to use two cores: which we can run, to use two cores: -$ time ./A sunflower.png +RTS -N2 -H + $time ./repa sunflower.png +RTS -N2 -H - ./A sunflower.png +RTS -N2 -H 0.19s user 0.03s system 135% cpu 0.165 total + + real 0m0.048s + user 0m0.052s + sys 0m0.016s + + We can also see that program takes a bit longer when run on a single core: + +$ time ./repa sunflower.png +RTS -N1 -H + + real 0m0.064s + user 0m0.060s + sys 0m0.008s Given an image like this: Given an image like this: Line 775: Line 837: http://i.imgur.com/REhA5.png http://i.imgur.com/REhA5.png - == Optimising Repa programs == + == Example: Partial Differential equations, idiomatic option pricing and risk == - === Fusion, and why you need it === + http://idontgetoutmuch.wordpress.com/2013/01/05/option-pricing-using-haskell-parallel-arrays/ + http://stackoverflow.com/questions/14082158/idiomatic-option-pricing-and-risk-using-repa-parallel-arrays + http://idontgetoutmuch.wordpress.com/2013/02/10/parallelising-path-dependent-options-in-haskell-2/ + + = Optimising Repa programs = + + '''Note''': This section is based on older version of Repa (2.x). It seems that it is not entirely accurate for Repa 3. For example there is no force function and array type (manifest or delayed) is now explicitly defined using type index. I do not feel sufficiently competent to update this section so I am leaving it "as is" for the time being. --[[User:Killy9999|killy9999]] 09:12, 11 October 2012 (UTC) + + == 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 force function has the loops === + == The force function has the loops == Suppose we have the following binding: Suppose we have the following binding: Line 790: Line 860: 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. 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 === + == 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. 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. Line 797: Line 867: 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 === + == 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 806: Line 876: 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. 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 === + == INLINE worker functions == Consider the following example: Consider the following example: Line 824: Line 894: 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'' - - === Programming with stencils === - - ''Discuss the stencil types model''

## Current revision

Repa is a Haskell library for high performance, regular, multi-dimensional 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 and was based on version 3.2 of the library. Repa versions earlier than 3.0 use different API and are not compatible with this tutorial.

Note: a companion tutorial to this is provided as the vector tutorial, and is based on the NumPy tutorial.

Authors: Don Stewart (original version), Jan Stolarek (update from Repa 2 to Repa 3).

# 1 Quick Tour

Repa (REgular PArallel arrays) is an advanced, multi-dimensional 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:

# 2 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 non-core functionality, a number of related packages are available: and example algorithms in: # 3 Type indexes Repa 3 introduced a notion of type index: data family Array rep sh e  In the above definition rep is the type index, which is represented with a data type name and defines the type of underlying array representation. This allows to explicitly specify representation of an array on type system level. Repa distinguishes ten different types of representation, which fall into three categories: manifest, delayed and meta. Manifest arrays are represented by concrete values stored in memory using e.g. unboxed vectors (represented with data type U) or strict bytestrings (B). Delayed array (D) is a function from index to a value. Every time an element is requested from a delayed array it is calculated anew, which means that delayed arrays are inefficient when the data is needed multiple times. On the other hand delaying an array allows to perform optimisation by fusion. Meta arrays provide additional information about underlying array data, which can be used for load balancing or speeding up calculation of border effects. See Guiding Parallel Array Fusion with Indexed Types paper for a detailed explanation of each representation. # 4 Array 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, repa-based 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 multi-dimensional 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 two-dimensional array of unboxed doubles, indexed via Int keys, while Array U Z Double is a zero-dimension 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. ## 4.1 Building shapes To build values of shape type, we can use the Z and :. constructors. Open the ghci and import Repa: ghci> :m +Data.Array.Repa ghci> Z -- the zero-dimension Z For arrays of non-zero dimension, we must give a size. Note: a common error is to leave off the type of the size. ghci> :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: ghci> :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. ## 4.2 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 ghci> let x :: Array U DIM3 Int; x = fromListUnboxed (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27] ghci> :t x x :: Array U DIM3 Int -- query the extent ghci> extent x ((Z :. 3) :. 3) :. 3 -- compute the rank (number of dimensions) ghci> let sh = extent x ghci> rank sh 3 -- compute the size (total number of elements) > size sh 27 -- extract the elements of the array as a flat vector ghci> 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 # 5 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 ghc-prim ... linking ... done. Loading package integer-gmp ... linking ... done. Loading package base ... linking ... done. ghci> :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):

ghci> let inputs = [1..10] :: [Double]
ghci> let x = fromListUnboxed (Z :. (10::Int)) inputs
ghci> 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:

ghci> :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:

ghci> let x' = fromListUnboxed (Z :. 10 :: DIM1) inputs
ghci> :t x'
x' :: Array U DIM1 Double

The same data may also be treated as a two dimensional array, by changing the shape parameter:

ghci> let x2 = fromListUnboxed (Z :. (5::Int) :. (2::Int)) inputs
ghci> 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:

ghci> :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:

ghci> let x2' = fromListUnboxed (Z :. 5 :. 2 :: DIM2) inputs
ghci> 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])
ghci> :t x2'
x2' :: Array U DIM2 Double

## 5.1 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:

ghci> :m + Data.Vector.Unboxed
ghci> let x = fromUnboxed (Z :. (10::Int)) (enumFromN 0 10)
ghci> 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 one-dimensional array of doubles. As usual, we can also impose other shapes:

ghci> let x = fromUnboxed (Z :. (3::Int) :. (3::Int)) (enumFromN 0 9)
ghci> x
AUnboxed ((Z :. 3) :. 3) (fromList [0.0,1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0])
ghci> :t x
x :: Array U ((Z :. Int) :. Int) Double

to create a 3x3 array.

## 5.2 Generating random arrays

The repa-algorithms package lets us generate new arrays with random data:

-- 3d array of Ints, bounded between 0 and 255.
ghci> 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])

## 5.3 Reading arrays from files

Using the repa-io 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:

ghci> :m +Data.Array.Repa.IO.Matrix
ghci> let x = fromList (Z :. 5 :. 2 :: DIM2) [1..10]
ghci> 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.

ghci> xx <- readMatrixFromTextFile "test.dat"
ghci> 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])
ghci> :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 repa-devil library.

## 5.4 Copying arrays from pointers

You can also generate new repa arrays by copying data from a pointer. Here is an example, using fromForeignPtr:

import Data.Word
import Foreign.Ptr
import qualified Foreign.ForeignPtr.Safe         as FPS

import qualified Data.Vector.Storable            as V
import qualified Data.Array.Repa                 as R
import qualified Data.Array.Repa.Repr.ForeignPtr as RFP
import Data.Array.Repa

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 $concat [ [ r, g, b, 255 ] | r <- [0 .. 255] , g <- [0 .. 255] , b <- [0 .. 255] ] ptr2repa :: Ptr Word8 -> IO (R.Array RFP.F R.DIM3 Word8) ptr2repa p = do fp <- FPS.newForeignPtr_ p return$ RFP.fromForeignPtr (Z :. i :. j :. k) fp

main = do
-- copy our 1d vector to a repa 3d array, via a pointer
r <- V.unsafeWith v ptr2repa
runIL $writeImage "test.png" (RGBA r) return () The v function creates a vector filled with pixel data (RGBA values); ptr2repa takes a Ptr, converts it to ForeignPtr which is then used to copy data into a 3D REPA array (first two dimensions correspond to pixel location, the third dimension corresponds to colour channel). In the main function we use unsafeWith function operating on storable vectors. It passes Ptr to vector's internal data to a function that operates on that Ptr (it may not modify the data). In our case this is the ptr2repa array, which returns image data wrapped in an array. That array is then passed as data to writeImage function. RGBA is a data constructor for Image type. This allows writeImage to interpret array data correctly. runIL is a wrapper around IO monad that guarantees that IL library will be properly initialized. The result is written to disk as this image: # 6 Indexing arrays To access elements in repa arrays, you provide an array and a shape, to access the element: (!) :: (Shape sh, Source r e) => Array r sh e -> sh -> e Indices start with 0. So: ghci> let x = fromListUnboxed (Z :. (10::Int)) [1..10] ghci> x ! (Z :. 2) 3.0 Note that we can't give just a bare literal as the shape, even for one-dimensional 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:244 ((!)): index out of bounds (11,10) An exception is thrown. Older versions of repa offered safe indexing operator (!?) that returned Maybe, but it is no longer available in repa 3.2. # 7 Operations on arrays Besides indexing, there are many regular, list-like operations on arrays. Since many of the names parallel those in the Prelude, we import Repa qualified: ghci> import qualified Data.Array.Repa as Repa  ## 7.1 Maps, zips, filters and folds Let us define an unboxed array: ghci> let x = fromListUnboxed (Z :. (3::Int) :. (3::Int)) [1..9] ghci> x AUnboxed ((Z :. 3) :. 3) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0]) We can map over multi-dimensional arrays with map, but since it conflicts with the definition in the Prelude, we have to use it with the qualifier we requested: ghci> let y = Repa.map (^2) x The type of Repa.map is: map :: (Shape sh, Source r a) => (a -> b) -> Array r sh a -> Array D sh b This means that result is a delayed array (represented by letter D in the return type). We can index into delayed array (remember that indices are 0-based): ghci> y Repa.! (Z :. 0 :. 2) 9.0 ghci> y Repa.! (Z :. 2 :. 0) 49.0 Passing an incorrect index will NOT cause an exception: ghci> y Repa.! (Z :. 0 :. 3) 16.0 ghci> y Repa.! (Z :. 0 :. -4) 0.0 ghci> y Repa.! (Z :. 10 :. 10) 0.0 Since delayed arrays are not instances of Show type class, we cannot display the whole array directly: ghci> y No instance for (Show (Array D ((Z :. Int) :. Int) Double)) arising from a use of print' To display y array it must be turned into manifest array by computing all its values. This can be done either with computeP, which evaluates array in parallel, or computeS, which evaluates values sequentially. Looking at the type of computeP: ghci> :t computeP computeP :: (Monad m, Source r2 e, Load r1 sh e, Target r2 e) => Array r1 sh e -> m (Array r2 sh e) we can see that the result is enclosed within a monad. The reason for this is that monads give a well defined notion of sequence and thus computeP enforces completion of parallel evaluation in a particular point of monadic computations. This prevents nested data parallelism, which is not supported by Repa. We don't really care here about monadic effect, so we can take the manifest array out of a monad after computeP is done: ghci> computeP y :: IO (Array U DIM2 Double) AUnboxed ((Z :. 3) :. 3) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]) ghci> z <- computeP y :: IO (Array U DIM2 Double) ghci> z AUnboxed ((Z :. 3) :. 3) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]) ghci> let [w] = computeP y :: [Array U DIM2 Double] ghci> w AUnboxed ((Z :. 3) :. 3) (fromList [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: ghci> extent x (Z :. 3) :. 3 ghci> extent y (Z :. 3) :. 3 There are four fold operations in Repa library: foldP, foldS, foldAllP and foldAllS. First two reduce the the inner dimension of the array, the last two reduce whole array to one value. The P and S suffixes stand for parallel and sequential evaluation. First parameter of parallel folds must be an associative sequential operator and the starting element must be a neutral element of that operator. This is required in order to ensure correctness of parallel evaluation. Type of foldP is given as: foldP :: (Monad m, Shape sh, Source r a, Unbox a, Elt a) => (a -> a -> a) -> a -> Array r (sh :. Int) a -> m (Array U sh a) The x defined above was a 2D array: ghci> extent x (Z :. 3) :. 3 but if we sum each row: ghci> Repa.foldP (+) 0 x AUnboxed (Z :. 3) (fromList [6.0,15.0,24.0]) we get a 1D array instead (liftM is used to run extent function within a monad returned by foldP): ghci> liftM extent (Repa.foldP (+) 0 x) Z :. 3 Similarly, if y is a (3 x 3 x 2) 3D array: ghci> let y = fromListUnboxed ((Z :. 3 :. 3 :. 2) :: DIM3) [1..18] ghci> y AUnboxed (((Z :. 3) :. 3) :. 2) (fromList [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]) we can fold over the inner dimension: ghci> Repa.foldP (+) 0 y AUnboxed ((Z :. 3) :. 3) (fromList [3.0,7.0,11.0,15.0,19.0,23.0,27.0,31.0,35.0]) yielding a 2D (3 x 3) array in place of our 3D (3 x 3 x 3) array. Two arrays may be combined via zipWith: zipWith :: (Shape sh, Source r2 b, Source r1 a) => (a -> b -> c) -> Array r1 sh a -> Array r2 sh b -> Array D sh c an example: ghci> let y = Repa.zipWith (*) x x ghci> computeP y :: IO (Array U DIM2 Double) AUnboxed ((Z :. 3) :. 3) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0]) ghci> extent y (Z :. 3) :. 3 If the extent of the two array arguments differ, then the resulting array's extent is their intersection. ## 7.2 Mapping, with indices A very powerful operator is traverse, an array transformation which also supplies the current index: traverse :: (Shape sh', Shape sh, Source r a) => Array r 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 D sh' b This is quite a complicated 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. It is a function (often anonymous one) that takes two parameters: a function that looks up elements of an original array and a location of the currently processed element in the new array. Based on these two parameters a new value is determined for the resulting array. Note that traverse goes through all elements of the resulting array and it is up to you how to get values of original array based on an index of the new array. traverse returns a delayed array, which means we will use computeP to evaluate its values and display them on the screen. So we see this generalizes map to support indexes, and optional inspection of the current element. Let's try some examples: ghci> let x = fromListUnboxed (Z :. (3::Int) :. (3::Int) :. (3::Int)) [1..27] :: Array U DIM3 Int ghci> x AUnboxed (((Z :. 3) :. 3) :. 3) (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]) -- Keeping the shape the same, and just overwriting elements -- Use traverse to set all elements to their x axis: ghci> computeP$ traverse x id (\_ (Z :. i :. j :. k) -> i) :: IO (Array U DIM3 Int)
AUnboxed (((Z :. 3) :. 3) :. 3) (fromList [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:
ghci> computeP $traverse x id (\f (Z :. i :. j :. k) -> f (Z :. j :. k :. i)) :: IO (Array U DIM3 Int) AUnboxed (((Z :. 3) :. 3) :. 3) (fromList [1,4,7,10,13,16,19,22,25, 2,5,8,11,14,17,20,23,26, 3,6,9,12,15,18,21,24,27]) -- Take a slice of one dimension. The resulting shape of the array changes ghci> computeP$ traverse x (\(e :. _) -> e) (\f (Z :. i :. j) -> f (Z :. i :. j :. 0)) :: IO (Array U DIM2 Int)
AUnboxed ((Z :. 3) :. 3) (fromList [1,4,7,10,13,16,19,22,25])

The documentation on traverse provides further information.

## 7.3 Numeric operations: negation, addition, subtraction, multiplication

To perform basic numeric operations on Repa arrays you must use specialized operators: (+^) (addition), (-^) (subtraction), (*^) (multiplication) and (/^) (division). All these operators work element-wise and produce delayed arrays (they have to be computed if you want to have results as unboxed arrays).

ghci> let x = fromListUnboxed (Z :. (10 :: Int)) [1..10]
ghci> computeP $x +^ x :: IO (Array U DIM1 Double) AUnboxed (Z :. 10) (fromList [2.0,4.0,6.0,8.0,10.0,12.0,14.0,16.0,18.0,20.0]) ghci> computeP$ x -^ x :: IO (Array U DIM1 Double)
AUnboxed (Z :. 10) (fromList [0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0])
ghci> computeP $x *^ x :: IO (Array U DIM1 Double) AUnboxed (Z :. 10) (fromList [1.0,4.0,9.0,16.0,25.0,36.0,49.0,64.0,81.0,100.0]) ghci> computeP$ x /^ x :: IO (Array U DIM1 Double)
AUnboxed (Z :. 10) (fromList [1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0])

# 8 Changing the shape of an array

One of the main advantages of repa-style arrays over other arrays in Haskell is the ability to reshape data without copying. This is achieved via *index-space transformations*.

An example: transposing a 2D array. First, the type of the transformation:

transpose2D :: (Source r e) => Array r DIM2 e -> Array D 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 sh2, Shape sh1, Source r e) =>
sh2 -> (sh2 -> sh1) -> Array r sh1 e -> Array D sh2 e

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, Source r e) =>
Array r ((sh :. Int) :. Int) e -> Array D ((sh :. Int) :. Int) e

The types indicate that it works on the lowest two dimensions of the array.

Arrays shape can be changed without altering the underlying data by using reshape:

ghci> let x = fromListUnboxed (Z :. (5 :: Int) :. (2 :: Int)) [1..10]
ghci> x
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])
ghci> computeP $reshape (Z :. (2::Int) :. (5::Int)) x :: IO (Array U DIM2 Double) AUnboxed ((Z :. 2) :. 5) (fromList [1.0,2.0,3.0,4.0,5.0,6.0,7.0,8.0,9.0,10.0]) # 9 Examples Following are some examples of useful functions that exercise the API. ## 9.1 Example: Rotating an image with backpermute Flip an image upside down: import Foreign.Ptr import System.Environment import Data.Word import Data.Array.Repa hiding ((++)) import Data.Array.Repa.IO.DevIL import Data.Array.Repa.Repr.ForeignPtr main :: IO () main = do [f] <- getArgs (RGB v) <- runIL$ readImage f
rotated <- (computeP $rot180 v) :: IO (Array F DIM3 Word8) runIL$ writeImage ("flip-"++f) (RGB rotated)

rot180 :: (Source r e) => Array r DIM3 e -> Array D DIM3 e
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:

   $ghc -O2 --make -threaded -rtsopts repa.hs$ ./repa "haskell.jpg" +RTS -N2


Results in:

## 9.2 Example: matrix-matrix multiplication

A more advanced example from the Repa paper is matrix-matrix 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 $A=\begin{bmatrix}a&b\\c&d\end{bmatrix}$ and $B=\begin{bmatrix}e&f\\g&h\end{bmatrix}$

then

$AB=\begin{bmatrix}a&b\\c&d\end{bmatrix}\begin{bmatrix}e&f\\g&h\end{bmatrix}=\begin{bmatrix}ae+bg&af+bh\\ce+dg&cf+dh\end{bmatrix}$

So we take two, 2D arrays and generate a new array, using our transpose function from earlier:

mmMult  :: Monad m
=> Array U DIM2 Double
-> Array U DIM2 Double
-> m (Array U DIM2 Double)
mmMult a b = sumP (Repa.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 using extend function. 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 repa-algorithms package.

## 9.3 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 repa-devil image library:

import System.Environment
import Data.Word
import Data.Array.Repa hiding ((++))
import Data.Array.Repa.IO.DevIL
import Data.Array.Repa.Repr.ForeignPtr

--
-- Read an image, desaturate, write out with new name.
--
main = do
[f] <- getArgs
runIL $do (RGB a) <- readImage f b <- (computeP$ traverse a id luminosity) :: IL (Array F DIM3 Word8)
writeImage ("grey-" ++ f) (RGB 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 repa.hs  which we can run, to use two cores: $ time ./repa sunflower.png +RTS -N2 -H

real    0m0.048s
user    0m0.052s
sys     0m0.016s


We can also see that program takes a bit longer when run on a single core:

$time ./repa sunflower.png +RTS -N1 -H real 0m0.064s user 0m0.060s sys 0m0.008s  Given an image like this: The desaturated result from Haskell: ## 9.4 Example: Partial Differential equations, idiomatic option pricing and risk # 10 Optimising Repa programs Note: This section is based on older version of Repa (2.x). It seems that it is not entirely accurate for Repa 3. For example there is no force function and array type (manifest or delayed) is now explicitly defined using type index. I do not feel sufficiently competent to update this section so I am leaving it "as is" for the time being. --killy9999 09:12, 11 October 2012 (UTC) ## 10.1 Fusion, and why you need it Repa depends critically on array fusion to achieve fast code. Fusion is a fancy name for the combination of inlining and code transformations performed by GHC when it compiles your program. The fusion process merges the array filling loops defined in the Repa library, with the "worker" functions that you write in your own module. If the fusion process fails, then the resulting program will be much slower than it needs to be, often 10x slower an equivalent program using plain Haskell lists. On the other hand, provided fusion works, the resulting code will run as fast as an equivalent cleanly written C program. Making fusion work is not hard once you understand what's going on. ## 10.2 The force function has the loops Suppose we have the following binding:  arr' = R.force$ 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.

## 10.3 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.

## 10.4 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.

## 10.5 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)
`