-------------------------------------------------------------------------------- -- | -- Module : ArrayFire -- Copyright : David Johnson (c) 2019-2020 -- License : BSD3 -- Maintainer : David Johnson <djohnson.m@gmail.com> -- Stability : Experimental -- Portability : GHC -- -- <<https://user-images.githubusercontent.com/875324/59819703-0fbaf980-92f7-11e9-8f53-adebea590bfb.png>> -- -------------------------------------------------------------------------------- module ArrayFire ( -- * Tutorial -- $tutorial -- ** Modules -- $modules -- ** Exceptions -- $exceptions -- ** Construction -- $construction -- ** Laws -- $laws -- ** Conversion -- $conversion -- ** Serialization -- $serialization -- ** Device -- $device module ArrayFire.Algorithm , module ArrayFire.Arith , module ArrayFire.Array , module ArrayFire.Backend , module ArrayFire.BLAS , module ArrayFire.Data , module ArrayFire.Device , module ArrayFire.Features , module ArrayFire.Graphics , module ArrayFire.Image , module ArrayFire.Index , module ArrayFire.LAPACK , module ArrayFire.Random , module ArrayFire.Signal , module ArrayFire.Sparse , module ArrayFire.Statistics , module ArrayFire.Types , module ArrayFire.Util , module ArrayFire.Vision , module Foreign.C.Types , module Data.Int , module Data.Word , module Data.Complex , module Foreign.Storable ) where import ArrayFire.Algorithm import ArrayFire.Arith import ArrayFire.Array import ArrayFire.Backend import ArrayFire.BLAS import ArrayFire.Data import ArrayFire.Device import ArrayFire.Features import ArrayFire.Graphics import ArrayFire.Image import ArrayFire.Index import ArrayFire.LAPACK import ArrayFire.Random import ArrayFire.Signal import ArrayFire.Sparse import ArrayFire.Statistics import ArrayFire.Types import ArrayFire.Util import ArrayFire.Vision import ArrayFire.Orphans () import Foreign.Storable import Foreign.C.Types import Data.Int import Data.Complex import Data.Word -- $tutorial -- -- [ArrayFire](http://arrayfire.org/docs/gettingstarted.htm) is a high performance parallel computing library that features modules for statistical and numerical methods. -- Example usage is depicted below. -- -- @ -- module Main where -- -- import qualified ArrayFire as A -- -- main :: IO () -- main = print $ A.matrix @Double (2,2) [[1,2],[3,4]] -- @ -- -- @ -- ArrayFire Array -- [2 2 1 1] -- 1.0000 2.0000 -- 3.0000 4.0000 -- @ -- $modules -- -- All child modules are re-exported top-level in the "ArrayFire" module. -- We recommend importing "ArrayFire" qualified so as to avoid naming collisions. -- -- >>> import qualified ArrayFire as A -- -- $exceptions -- -- @ -- {\-\# LANGUAGE TypeApplications \#\-} -- module Main where -- -- import qualified ArrayFire as A -- import Control.Exception ( catch ) -- -- main :: IO () -- main = A.printArray action \`catch\` (\\(e :: A.AFException) -> print e) -- where -- action = -- A.matrix \@Double (3,3) [[[1..],[1..],[1..]]] -- \`A.mul\` A.matrix \@Double (2,2) [[1..],[1..]] -- @ -- -- The above operation is invalid since the matrix multiply has improper dimensions. The caught exception produces the following error: -- -- > AFException {afExceptionType = SizeError, afExceptionCode = 203, afExceptionMsg = "Invalid input size"} -- -- $construction -- An 'Array' can be constructed using the following smart constructors: -- -- @ -- >>> scalar \@Double 2.0 -- ArrayFire Array -- [1 1 1 1] -- 2.0000 -- @ -- -- @ -- >>> vector \@Double 10 [1..] -- ArrayFire Array -- [10 1 1 1] -- 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 -- @ -- -- @ -- >>> matrix \@Double (2,2) [[1,2],[3,4]] -- ArrayFire Array -- [2 2 1 1] -- 1.0000 2.0000 -- 3.0000 4.0000 -- @ -- -- @ -- >>> cube \@Double (2,2,2) [[[2,2],[2,2]],[[2,2],[2,2]]] -- ArrayFire Array -- [2 2 2 1] -- 2.0000 2.0000 -- 2.0000 2.0000 -- -- 2.0000 2.0000 -- 2.0000 2.0000 -- @ -- -- @ -- >>> tensor \@Double (2,2,2,2) [[[[2,2],[2,2]],[[2,2],[2,2]]], [[[2,2],[2,2]],[[2,2],[2,2]]]] -- ArrayFire Array -- [2 2 2 2] -- 2.0000 2.0000 -- 2.0000 2.0000 -- -- 2.0000 2.0000 -- 2.0000 2.0000 -- -- -- 2.0000 2.0000 -- 2.0000 2.0000 -- -- 2.0000 2.0000 -- 2.0000 2.0000 -- @ -- -- Array construction can use Haskell's lazy lists, since 'take' is called on each dimension before sending to the C API. -- -- >>> mkArray @Double [2,2] [ [1..], [1..] ] -- ArrayFire Array -- [10 1 1 1] -- 1.0000 2.0000 3.0000 4.0000 5.0000 6.0000 7.0000 8.0000 9.0000 10.0000 -- -- Specifying up to 4 dimensions is allowed (anything high is ignored). -- $laws -- Every 'Array' is an instance of 'Eq', 'Num', 'Fractional', 'Floating' -- -- 'Num' -- -- >>> scalar @Int 1 + scalar @Int 1 -- ArrayFire Array -- [1 1 1 1] -- 2 -- -- >>> scalar @Int 1 - scalar @Int 1 -- ArrayFire Array -- [1 1 1 1] -- 0 -- -- >>> scalar @Double 10 / scalar @Double 10 -- ArrayFire Array -- [1 1 1 1] -- 1.0000 -- -- >>> abs $ scalar @Double (-10) -- ArrayFire Array -- [1 1 1 1] -- 10.0000 -- -- >>> negate (scalar @Double 1 [10]) -- -10.0 -- -- >>> fromInteger 1.0 :: Array Double -- ArrayFire Array -- [1 1 1 1] -- 1.0000 -- -- 'Eq' -- -- >>> scalar @Double 1 [10] == scalar @Double 1 [10] -- True -- >>> scalar @Double 1 [10] /= scalar @Double 1 [10] -- False -- -- -- 'Floating' -- -- >>> pi :: Array Double -- ArrayFire Array -- [1 1 1 1] -- 3.1416 -- -- $conversion -- 'Array' can be exported into Haskell using `toVector'. This will create a Storable vector suitable for use in other C programs. -- -- >>> vector :: Vector Double <- toVector <$> randu @Double [10,10] -- -- $serialization -- Each 'Array' can be serialized to disk and deserialized from disk efficiently. -- -- @ -- import qualified ArrayFire as A -- import Control.Monad -- -- main :: IO () -- main = do -- let arr = A.'constant' [1,1,1,1] 10 -- idx <- A.'saveArray' "key" arr "file.array" False -- foundIndex <- A.'readArrayKeyCheck' "file.array" "key" -- when (idx == foundIndex) $ do -- array <- A.'readArrayKey' "file.array" "key" -- 'print' array -- -- ArrayFire Array -- [ 1 1 1 1 ] -- 10 -- @ -- -- $device -- The ArrayFire API is able to see which devices are present, and will by default use the GPU if available. -- -- >>> afInfo -- ArrayFire v3.6.4 (OpenCL, 64-bit Mac OSX, build 1b8030c5) -- [0] APPLE: AMD Radeon Pro 555X Compute Engine, 4096 MB <-- brackets [] signify device being used. -- -1- APPLE: Intel(R) UHD Graphics 630, 1536 MB -- -- $visualization -- The ArrayFire API is able to visualize -- >>> window <- createWindow 800 600 "Histogram" --