Safe Haskell | None |
---|---|
Language | Haskell98 |
- class (Positive (Size v), Phi v, Undefined v) => Simple v where
- type Element v :: *
- type Size v :: *
- shuffleMatch :: ConstValue (Vector (Size v) Word32) -> v -> CodeGenFunction r v
- extract :: Value Word32 -> v -> CodeGenFunction r (Element v)
- class Simple v => C v where
- insert :: Value Word32 -> Element v -> v -> CodeGenFunction r v
- class (n ~ Size (Construct n a), a ~ Element (Construct n a), C (Construct n a)) => Canonical n a where
- type Construct n a :: *
- size :: Positive n => Value (Vector n a) -> Int
- sizeInTuple :: Simple v => v -> Int
- replicate :: C v => Element v -> CodeGenFunction r v
- iterate :: C v => (Element v -> CodeGenFunction r (Element v)) -> Element v -> CodeGenFunction r v
- assemble :: C v => [Element v] -> CodeGenFunction r v
- shuffle :: (C v, C w, Element v ~ Element w) => v -> ConstValue (Vector (Size w) Word32) -> CodeGenFunction r w
- rotateUp :: Simple v => v -> CodeGenFunction r v
- rotateDown :: Simple v => v -> CodeGenFunction r v
- reverse :: Simple v => v -> CodeGenFunction r v
- shiftUp :: C v => Element v -> v -> CodeGenFunction r (Element v, v)
- shiftDown :: C v => Element v -> v -> CodeGenFunction r (Element v, v)
- shiftUpMultiZero :: (C v, Zero (Element v)) => Int -> v -> CodeGenFunction r v
- shiftDownMultiZero :: (C v, Zero (Element v)) => Int -> v -> CodeGenFunction r v
- shuffleMatchTraversable :: (Simple v, Traversable f) => ConstValue (Vector (Size v) Word32) -> f v -> CodeGenFunction r (f v)
- shuffleMatchAccess :: C v => ConstValue (Vector (Size v) Word32) -> v -> CodeGenFunction r v
- shuffleMatchPlain1 :: (Positive n, IsPrimitive a) => Value (Vector n a) -> ConstValue (Vector n Word32) -> CodeGenFunction r (Value (Vector n a))
- shuffleMatchPlain2 :: (Positive n, IsPrimitive a) => Value (Vector n a) -> Value (Vector n a) -> ConstValue (Vector n Word32) -> CodeGenFunction r (Value (Vector n a))
- insertTraversable :: (C v, Traversable f, Applicative f) => Value Word32 -> f (Element v) -> f v -> CodeGenFunction r (f v)
- extractTraversable :: (Simple v, Traversable f) => Value Word32 -> f v -> CodeGenFunction r (f (Element v))
- extractAll :: Simple v => v -> CodeGenFunction r [Element v]
- data Constant n a
- constant :: Positive n => a -> Constant n a
- insertChunk :: (C c, C v, Element c ~ Element v) => Int -> c -> v -> CodeGenFunction r v
- modify :: C v => Value Word32 -> (Element v -> CodeGenFunction r (Element v)) -> v -> CodeGenFunction r v
- map :: (C v, C w, Size v ~ Size w) => (Element v -> CodeGenFunction r (Element w)) -> v -> CodeGenFunction r w
- mapChunks :: (C ca, C cb, Size ca ~ Size cb, C va, C vb, Size va ~ Size vb, Element ca ~ Element va, Element cb ~ Element vb) => (ca -> CodeGenFunction r cb) -> va -> CodeGenFunction r vb
- zipChunksWith :: (C ca, C cb, C cc, Size ca ~ Size cb, Size cb ~ Size cc, C va, C vb, C vc, Size va ~ Size vb, Size vb ~ Size vc, Element ca ~ Element va, Element cb ~ Element vb, Element cc ~ Element vc) => (ca -> cb -> CodeGenFunction r cc) -> va -> vb -> CodeGenFunction r vc
- chop :: (C c, C v, Element c ~ Element v) => v -> [CodeGenFunction r c]
- concat :: (C c, C v, Element c ~ Element v) => [c] -> CodeGenFunction r v
- select :: (IsFirstClass a, IsPrimitive a, Positive n, CmpRet a, CmpResult a ~ Bool) => Value (Vector n Bool) -> Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- signedFraction :: (IsFloating a, IsConst a, Real a, Positive n) => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- cumulate1 :: (IsArithmetic a, IsPrimitive a, Positive n) => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- umul32to64 :: Positive n => Value (Vector n Word32) -> Value (Vector n Word32) -> CodeGenFunction r (Value (Vector n Word64))
- class (IsArithmetic a, IsPrimitive a) => Arithmetic a where
- sum :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value a)
- sumToPair :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value a, Value a)
- sumInterleavedToPair :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value a, Value a)
- cumulate :: Positive n => Value a -> Value (Vector n a) -> CodeGenFunction r (Value a, Value (Vector n a))
- dotProduct :: Positive n => Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value a)
- mul :: Positive n => Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- class (Arithmetic a, CmpRet a, CmpResult a ~ Bool, IsConst a) => Real a where
- min, max :: Positive n => Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- abs :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- signum :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
- truncate, fraction, floor :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a))
Documentation
class (Positive (Size v), Phi v, Undefined v) => Simple v where Source
shuffleMatch :: ConstValue (Vector (Size v) Word32) -> v -> CodeGenFunction r v Source
extract :: Value Word32 -> v -> CodeGenFunction r (Element v) Source
class Simple v => C v where Source
Allow to work on records of vectors as if they are vectors of records.
This is a reasonable approach for records of different element types
since processor vectors can only be built from elements of the same type.
But also, say, for chunked stereo signal this makes sense.
In this case we would work on Stereo (Value a)
.
Formerly we used a two-way dependency Vector - (Element, Size). Now we have only the dependency Vector -> (Element, Size). This means that we need some more type annotations as in umul32to64/assemble, on the other hand we can allow multiple vector types with respect to the same element type. E.g. we can provide a vector type with pair elements where the pair elements are interleaved in the vector.
class (n ~ Size (Construct n a), a ~ Element (Construct n a), C (Construct n a)) => Canonical n a Source
sizeInTuple :: Simple v => v -> Int Source
replicate :: C v => Element v -> CodeGenFunction r v Source
Manually assemble a vector of equal values. Better use ScalarOrVector.replicate.
iterate :: C v => (Element v -> CodeGenFunction r (Element v)) -> Element v -> CodeGenFunction r v Source
assemble :: C v => [Element v] -> CodeGenFunction r v Source
construct a vector out of single elements
You must assert that the length of the list matches the vector size.
This can be considered the inverse of extractAll
.
shuffle :: (C v, C w, Element v ~ Element w) => v -> ConstValue (Vector (Size w) Word32) -> CodeGenFunction r w Source
Manually implement vector shuffling using insertelement and extractelement.
In contrast to LLVM's built-in instruction it supports distinct vector sizes,
but it allows only one input vector
(or a tuple of vectors, but we cannot shuffle between them).
For more complex shuffling we recommend extractAll
and assemble
.
rotateUp :: Simple v => v -> CodeGenFunction r v Source
Rotate one element towards the higher elements.
I don't want to call it rotateLeft or rotateRight, because there is no prefered layout for the vector elements. In Intel's instruction manual vector elements are indexed like the bits, that is from right to left. However, when working with Haskell list and enumeration syntax, the start index is left.
rotateDown :: Simple v => v -> CodeGenFunction r v Source
reverse :: Simple v => v -> CodeGenFunction r v Source
shiftUpMultiZero :: (C v, Zero (Element v)) => Int -> v -> CodeGenFunction r v Source
shiftDownMultiZero :: (C v, Zero (Element v)) => Int -> v -> CodeGenFunction r v Source
shuffleMatchTraversable :: (Simple v, Traversable f) => ConstValue (Vector (Size v) Word32) -> f v -> CodeGenFunction r (f v) Source
shuffleMatchAccess :: C v => ConstValue (Vector (Size v) Word32) -> v -> CodeGenFunction r v Source
Implement the shuffleMatch
method using the methods of the C
class.
shuffleMatchPlain1 :: (Positive n, IsPrimitive a) => Value (Vector n a) -> ConstValue (Vector n Word32) -> CodeGenFunction r (Value (Vector n a)) Source
shuffleMatchPlain2 :: (Positive n, IsPrimitive a) => Value (Vector n a) -> Value (Vector n a) -> ConstValue (Vector n Word32) -> CodeGenFunction r (Value (Vector n a)) Source
insertTraversable :: (C v, Traversable f, Applicative f) => Value Word32 -> f (Element v) -> f v -> CodeGenFunction r (f v) Source
extractTraversable :: (Simple v, Traversable f) => Value Word32 -> f v -> CodeGenFunction r (f (Element v)) Source
extractAll :: Simple v => v -> CodeGenFunction r [Element v] Source
provide the elements of a vector as a list of individual virtual registers
This can be considered the inverse of assemble
.
insertChunk :: (C c, C v, Element c ~ Element v) => Int -> c -> v -> CodeGenFunction r v Source
modify :: C v => Value Word32 -> (Element v -> CodeGenFunction r (Element v)) -> v -> CodeGenFunction r v Source
map :: (C v, C w, Size v ~ Size w) => (Element v -> CodeGenFunction r (Element w)) -> v -> CodeGenFunction r w Source
Like LLVM.Util.Loop.mapVector but the loop is unrolled, which is faster since it can be packed by the code generator.
mapChunks :: (C ca, C cb, Size ca ~ Size cb, C va, C vb, Size va ~ Size vb, Element ca ~ Element va, Element cb ~ Element vb) => (ca -> CodeGenFunction r cb) -> va -> CodeGenFunction r vb Source
zipChunksWith :: (C ca, C cb, C cc, Size ca ~ Size cb, Size cb ~ Size cc, C va, C vb, C vc, Size va ~ Size vb, Size vb ~ Size vc, Element ca ~ Element va, Element cb ~ Element vb, Element cc ~ Element vc) => (ca -> cb -> CodeGenFunction r cc) -> va -> vb -> CodeGenFunction r vc Source
chop :: (C c, C v, Element c ~ Element v) => v -> [CodeGenFunction r c] Source
If the target vector type is a native type then the chop operation produces no actual machine instruction. (nop) If the vector cannot be evenly divided into chunks the last chunk will be padded with undefined values.
concat :: (C c, C v, Element c ~ Element v) => [c] -> CodeGenFunction r v Source
The target size is determined by the type. If the chunk list provides more data, the exceeding data is dropped. If the chunk list provides too few data, the target vector is filled with undefined elements.
select :: (IsFirstClass a, IsPrimitive a, Positive n, CmpRet a, CmpResult a ~ Bool) => Value (Vector n Bool) -> Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
LLVM.select on boolean vectors cannot be translated to X86 code in LLVM-2.6, thus I code my own version that calls select on all elements. This is slow but works. When this issue is fixed, this function will be replaced by LLVM.select.
signedFraction :: (IsFloating a, IsConst a, Real a, Positive n) => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
cumulate1 :: (IsArithmetic a, IsPrimitive a, Positive n) => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
Needs (log n) vector additions
umul32to64 :: Positive n => Value (Vector n Word32) -> Value (Vector n Word32) -> CodeGenFunction r (Value (Vector n Word64)) Source
class (IsArithmetic a, IsPrimitive a) => Arithmetic a where Source
The order of addition is chosen for maximum efficiency. We do not try to prevent cancelations.
Nothing
sum :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value a) Source
sumToPair :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value a, Value a) Source
The first result value is the sum of all vector elements from 0 to div n 2 + 1
and the second result value is the sum of vector elements from div n 2
to n-1
.
n must be at least D2.
sumInterleavedToPair :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value a, Value a) Source
Treat the vector as concatenation of pairs and all these pairs are added. Useful for stereo signal processing. n must be at least D2.
cumulate :: Positive n => Value a -> Value (Vector n a) -> CodeGenFunction r (Value a, Value (Vector n a)) Source
dotProduct :: Positive n => Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value a) Source
mul :: Positive n => Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
class (Arithmetic a, CmpRet a, CmpResult a ~ Bool, IsConst a) => Real a where Source
Attention:
The rounding and fraction functions only work
for floating point values with maximum magnitude of maxBound :: Int32
.
This way we save expensive handling of possibly seldom cases.
min, max :: Positive n => Value (Vector n a) -> Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
abs :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
signum :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source
truncate, fraction, floor :: Positive n => Value (Vector n a) -> CodeGenFunction r (Value (Vector n a)) Source