Copyright | David Johnson (c) 2019-2020 |
---|---|
License | BSD3 |
Maintainer | David Johnson <djohnson.m@gmail.com> |
Stability | Experimental |
Portability | GHC |
Safe Haskell | None |
Language | Haskell2010 |
Synopsis
- 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
Tutorial
ArrayFire 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
>>>
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
>>>
scalar @Double 1 [10] == scalar @Double 1 [10]
True>>>
scalar @Double 1 [10] /= scalar @Double 1 [10]
False
>>>
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"
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
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