Haskell'98 supports just one array constructor type, namely Array, which gives you immutable boxed arrays. "Immutable" means that these arrays, like any other pure functional data structure, have contents fixed at construction time. You can't modify them, only query. There are "modification" operations, but they just return new arrays and don't modify the original one. This makes it possible to use Arrays in pure functional code along with lists. "Boxed" means that array elements are just ordinary Haskell (lazy) values, which are evaluated on demand, and can even contain bottom (undefined) value. You can learn how to use these arrays at http://haskell.org/tutorial/arrays.html and I'd recommend that you read this before proceeding to the rest of this page.

Nowadays the main Haskell compilers, GHC and Hugs, ship with the same set of Hierarchical Libraries, and these libraries contain a new implementation of arrays which is backward compatible with the Haskell'98 one, but which has far more features. Suffice it to say that these libraries support 9 types of array constructors: Array, UArray, IOArray, IOUArray, STArray, STUArray, DiffArray, DiffUArray and StorableArray. It is no wonder that the array libraries are a source of so much confusion for new Haskellers. However, they are actually very simple - each provides just one of two interfaces, and one of these you already know.

Quick reference [ edit ]

Immutable

instance IArray a e IO monad

instance MArray a e IO ST monad

instance MArray a e ST Standard Array

DiffArray IOArray STArray Unboxed UArray

DiffUArray IOUArray

StorableArray STUArray

Immutable arrays [ edit ]

The first interface provided by the new array library, is defined by the typeclass IArray (which stands for "immutable array" and defined in the module Data.Array.IArray) and defines the same operations that were defined for Array in Haskell '98. Here's a simple example of its use that prints (37,64) :

import Data.Array buildPair :: ( Int , Int ) buildPair = let arr = listArray ( 1 , 10 ) ( repeat 37 ) :: Array Int Int arr' = arr // [( 1 , 64 )] in ( arr ! 1 , arr' ! 1 ) main = print buildPair

The big difference is that it is now a typeclass and there are 4 array type constructors, each of which implements this interface: Array, UArray, DiffArray, and DiffUArray. We will later describe the differences between them and the cases when these other types are preferable to use instead of the good old Array. Also note that to use Array type constructor together with other new array types, you need to import Data.Array.IArray module instead of Data.Array.

Mutable IO arrays [ edit ]

The second interface is defined by the type class MArray (which stands for "mutable array" and is defined in the module Data.Array.MArray) and contains operations to update array elements in-place. Mutable arrays are very similar to IORefs, only they contain multiple values. Type constructors for mutable arrays are IOArray and IOUArray (in Data.Array.IO) and operations which create, update and query these arrays all belong to the IO monad:

import Data.Array.IO main = do arr <- newArray ( 1 , 10 ) 37 :: IO ( IOArray Int Int ) a <- readArray arr 1 writeArray arr 1 64 b <- readArray arr 1 print ( a , b )

This program creates an array of 10 elements with all values initially set to 37. Then it reads the first element of the array. After that, the program modifies the first element of the array and then reads it again. The type declaration in the second line is necessary because our little program doesn't provide enough context to allow the compiler to determine the concrete type of arr .

Mutable arrays in ST monad [ edit ]

In the same way that IORef has its more general cousin STRef, IOArray has a more general version STArray (and similarly, IOUArray is parodied by STUArray; both defined in Data.Array.ST). These array types allow one to work with mutable arrays in the ST monad:

import Control.Monad.ST import Data.Array.ST buildPair = do arr <- newArray ( 1 , 10 ) 37 :: ST s ( STArray s Int Int ) a <- readArray arr 1 writeArray arr 1 64 b <- readArray arr 1 return ( a , b ) main = print $ runST buildPair

Believe it or not, now you know all that is needed to use any array type. Unless you are interested in speed issues, just use Array, IOArray and STArray where appropriate. The following topics are almost exclusively about selecting the proper array type to make programs run faster.

Freezing and thawing [ edit ]

Haskell allows conversion between immutable and mutable arrays with the freeze and thaw functions:

freeze :: ( Ix i , MArray a e m , IArray b e ) => a i e -> m ( b i e ) thaw :: ( Ix i , IArray a e , MArray b e m ) => a i e -> m ( b i e )

For instance, the following converts an Array into an STArray, alters it, and then converts it back:

import Data.Array import Control.Monad.ST import Data.Array.ST buildPair :: ( Int , Int ) buildPair = let arr = listArray ( 1 , 10 ) ( repeat 37 ) :: Array Int Int arr' = modifyAsST arr in ( arr ! 1 , arr' ! 1 ) modifyAsST :: Array Int Int -> Array Int Int modifyAsST arr = runST $ do starr <- thaw arr compute starr newarr <- freeze starr return newarr compute :: STArray s Int Int -> ST s () compute arr = do writeArray arr 1 64 main = print buildPair

Freezing and thawing both copy the entire array. If you want to use the same memory locations before and after freezing or thawing but can allow some access restrictions, see the section Unsafe operations and running over array elements.

DiffArray [ edit ]

As we already stated, the update operation on immutable arrays (IArray) just creates a new copy of the array, which is very inefficient, but it is a pure operation which can be used in pure functions. On the other hand, updates on mutable arrays (MArray) are efficient but can be done only in monadic code. DiffArray (defined in Data.Array.Diff) combines the best of both worlds - it supports the IArray interface and therefore can be used in a purely functional way, but internally it uses the efficient update of MArrays.

How does this trick work? DiffArray has a pure external interface, but internally it is represented as a reference to an IOArray.

When the '//' operator is applied to a diff array, its contents are physically updated in place. The old array silently changes its representation without changing the visible behavior: it stores a link to the new current array along with the difference to be applied to get the old contents.

So if a diff array is used in a single-threaded style, that is, after '//' application the old version is no longer used, then a ! i takes O(1) time and a // d takes O(n) time. Accessing elements of older versions gradually becomes slower.

Updating an array which is not current makes a physical copy. The resulting array is unlinked from the old family. So you can obtain a version which is guaranteed to be current and thus has fast element access by performing the "identity update", old // [] .

The library provides two "differential" array constructors - DiffArray, made internally from IOArray, and DiffUArray, based on IOUArray. If you really need to, you can construct new "differential" array types from any 'MArray' types living in the 'IO' monad. See the module documentation for further details.

Usage of DiffArray doesn't differ from that of Array, the only difference is memory consumption and speed:

import Data.Array.Diff main = do let arr = listArray ( 1 , 1000 ) [ 1 .. 1000 ] :: DiffArray Int Int a = arr ! 1 arr2 = arr // [( 1 , 37 )] b = arr2 ! 1 print ( a , b )

You can use 'seq' to force evaluation of array elements prior to updating an array:

import Data.Array.Diff main = do let arr = listArray ( 1 , 1000 ) [ 1 .. 1000 ] :: DiffArray Int Int a = arr ! 1 b = arr ! 2 arr2 = a ` seq ` b ` seq ` ( arr // [( 1 , 37 ),( 2 , 64 )]) c = arr2 ! 1 print ( a , b , c )

Unboxed arrays [ edit ]

In most implementations of lazy evaluation, values are represented at runtime as pointers to either their value, or code for computing their value. This extra level of indirection, together with any extra tags needed by the runtime, is known as a box. The default "boxed" arrays consist of many of these boxes, each of which may compute its value separately. This allows for many neat tricks, like recursively defining an array's elements in terms of one another, or only computing the specific elements of the array which are ever needed. However, for large arrays, it costs a lot in terms of overhead, and if the entire array is always needed, it can be a waste.

Unboxed arrays (defined in Data.Array.Unboxed) are more like arrays in C - they contain just the plain values without this extra level of indirection, so that, for example, an array of 1024 values of type Int32 will use only 4 kb of memory. Moreover, indexing of such arrays can be significantly faster.

Of course, unboxed arrays have their own disadvantages. First, unboxed arrays can be made only of plain values having a fixed size -- Int, Word, Char, Bool, Ptr, Double and others (listed in the UArray class definition). You can even implement unboxed arrays yourself for other simple types, including enumerations. But Integer, String and any other types defined with variable size cannot be elements of unboxed arrays. Second, without that extra level of indirection, all of the elements in an unboxed array must be evaluated when the array is evaluated, so you lose the benefits of lazy evaluation. Indexing the array to read just one element will construct the entire array. This is not much of a loss if you will eventually need the whole array, but it does prevent recursively defining the array elements in terms of each other, and may be too expensive if you only ever need specific values. Nevertheless, unboxed arrays are a very useful optimization instrument, and I recommend using them as much as possible.

All main array types in the library have unboxed counterparts:

Array - UArray (module Data.Array.Unboxed) IOArray - IOUArray (module Data.Array.IO) STArray - STUArray (module Data.Array.ST) DiffArray - DiffUArray (module Data.Array.Diff)

So, basically replacing boxed arrays in your program with unboxed ones is very simple - just add 'U' to the type signatures, and you are done! Of course, if you change Array to UArray, you also need to add "Data.Array.Unboxed" to your imports list.

StorableArray [ edit ]

A storable array (Data.Array.Storable) is an IO-mutable array which stores its contents in a contiguous memory block living in the C heap. Elements are stored according to the class 'Storable'. You can obtain the pointer to the array contents to manipulate elements from languages like C.

It is similar to 'IOUArray' (in particular, it implements the same MArray interface) but slower. The advantage is that it's compatible with C through the foreign function interface. The memory addresses of storable arrays are fixed, so you can pass them to C routines.

The pointer to the array contents is obtained by 'withStorableArray'. The idea is similar to 'ForeignPtr' (used internally here). The pointer should be used only during execution of the 'IO' action returned by the function passed as argument to 'withStorableArray'.

{-# OPTIONS_GHC -fglasgow-exts #-} import Data.Array.Storable import Foreign.Ptr import Foreign.C.Types main = do arr <- newArray ( 1 , 10 ) 37 :: IO ( StorableArray Int Int ) a <- readArray arr 1 withStorableArray arr ( \ ptr -> memset ptr 0 40 ) b <- readArray arr 1 print ( a , b ) foreign import ccall unsafe "string.h" memset :: Ptr a -> CInt -> CSize -> IO ()

If you want to use this pointer afterwards, ensure that you call 'touchStorableArray' AFTER the last use of the pointer, so that the array will be not freed too early.



Additional comments: GHC 6.6 should make access to a 'StorableArray' as fast as to any other unboxed array. The only difference between 'StorableArray' and 'UArray' will be that UArray lies in relocatable part of GHC heap while 'StorableArray' lies in non-relocatable part and therefore keep the fixed address, what allow to pass this address to the C routines and save it in the C data structures.

GHC 6.6 also adds 'unsafeForeignPtrToStorableArray' operation that allows to use any Ptr as address of 'StorableArray' and in particular work with arrays returned by C routines. Example of using this operation:

import Data.Array.Storable import Foreign.Marshal.Alloc import Foreign.Marshal.Array import Foreign.ForeignPtr main = do ptr <- mallocArray 10 fptr <- newForeignPtr_ ptr arr <- unsafeForeignPtrToStorableArray ( 1 , 10 ) fptr :: IO ( StorableArray Int Int ) writeArray arr 1 64 a <- readArray arr 1 print a free ptr

This example allocs memory for 10 Ints (which emulates array returned by some C function), then converts returned 'Ptr Int' to 'ForeignPtr Int' and 'ForeignPtr Int' to 'StorableArray Int Int'. It then writes and reads first element of array. At the end, memory used by array is deallocated by 'free' which again emulates deallocation by C routines. We can also enable automatic freeing of allocated block by replacing "newForeignPtr_ ptr" with "newForeignPtr finalizerFree ptr". In this case memory will be automatically freed after last array usage, as for any other Haskell objects.

The Haskell Array Preprocessor (STPP) [ edit ]

Using mutable arrays in Haskell (IO and ST ones) is not very handy. But there is one tool which adds syntax sugar and makes using of such arrays very close to that in imperative languages. It is written by Hal Daume III and you can get it as http://hal3.name/STPP/stpp.tar.gz

Using this tool, you can index array elements in arbitrary complex expressions with just "arr[|i|]" notation and this preprocessor will automatically convert such syntax forms to appropriate calls to 'readArray' and 'writeArray'. Multi-dimensional arrays are also supported, with indexing in the form "arr[|i|][|j|]". See further descriptions at http://hal3.name/STPP/





ArrayRef library [ edit ]

The ArrayRef library reimplements array libraries with the following extensions:

dynamic (resizable) arrays

polymorphic unboxed arrays

It also adds syntax sugar what simplifies arrays usage. Although not so elegant as with STPP, it's on other hand implemented entirely inside Haskell language, without any preprocessors.





Unsafe operations and running over array elements [ edit ]

As mentioned above, there are operations that converts between mutable and immutable arrays of the same type, namely 'freeze' (mutable → immutable) and 'thaw' (immutable → mutable). These copy the entire array. If you are sure that a mutable array will not be modified or that an immutable array will not be used after the conversion, you can use unsafeFreeze/unsafeThaw instead - these operations convert array in-place if input and resulting arrays have the same memory representation (i.e. the same type and boxing). Please note that "unsafe*" operations modifies memory - they sets/clears flag in array header which tells about array mutability. So these operations can't be used together with multi-threaded access to array (using threads or some form of coroutines).

There are also operations that converts unboxed arrays to another element type, namely castIOUArray and castSTUArray. These operations rely on actual type representation in memory and therefore there is no any guarantees on their results. In particular, these operations can be used to convert any unboxable value to the sequence of bytes and vice versa, f.e. it's used in AltBinary library to serialize floating-point values. Please note that these operations don't recompute array bounds according to changed size of elements. You should do it yourself using 'sizeOf' operation!

While arrays can have any type of indexes, internally any index after bounds checking is converted to plain Int value (position) and then one of low-level operations, unsafeAt, unsafeRead, unsafeWrite, is used. You can use these operations yourself in order to outpass bounds checking and make your program faster. These operations are especially useful if you need to walk through entire array:

-- | Returns a list of all the elements of an array, in the same order -- as their indices. elems arr = [ unsafeAt arr i | i <- [ 0 .. rangeSize ( bounds arr ) - 1 ] ]

"unsafe*" operations in such loops are really safe because 'i' loops only through positions of existing array elements.

GHC-specific topics [ edit ]

Parallel arrays (module GHC.PArr) [ edit ]

As we already mentioned, array library supports two array varieties - lazy boxed arrays and strict unboxed ones. Parallel array implements something intervening - it's a strict boxed immutable array. This keeps flexibility of using any data type as array element while makes both creation and access to such arrays much faster. Array creation implemented as one imperative loop that fills all array elements, while access to array elements don't need to check "box". It should be obvious that parallel arrays are not efficient in cases where calculation of array elements are rather complex and you will not use most of array elements. One more drawback of practical usage is that parallel arrays don't support IArray interface which means that you can't write generic algorithms which work both with Array and parallel array constructor.

Like many GHC extensions, this is described in a paper: An Approach to Fast Arrays in Haskell, by Manuel M. T. Chakravarty and Gabriele Keller.

You can also look at sources of GHC.PArr module, which contains a lot of comments.

Special syntax for parallel arrays is enabled by "ghc -fparr" or "ghci -fparr" which is undocumented in user manual of GHC 6.4.1.





Welcome to the machine [ edit ]

Array#, MutableArray#, ByteArray#, MutableByteArray#, pinned and moveable byte arrays

GHC heap contains two kinds of objects - some are just byte sequences, other contains pointers to other objects (so called "boxes"). These segregation allows to find chains of references when performing garbage collection and update these pointers when memory used by heap is compacted and objects are moved to new places. Internal (raw) GHC's type Array# represents a sequence of object pointers (boxes). There is low-level operation in ST monad which allocates array of specified size in heap, its type is something like (Int -> ST s Array#). The Array# type is used inside Array type which represents boxed immutable arrays.

There is a different type for mutable boxed arrays (IOArray/STArray), namely MutableArray#. Separate type for mutable arrays required because of 2-stage garbage collection mechanism. Internal representation of Array# and MutableArray# are the same excluding for some flags in header and this make possible to on-place convert MutableArray# to Array# and vice versa (this is that unsafeFreeze and unsafeThaw operations do).

Unboxed arrays are represented by the ByteArray# type. It's just a plain memory area in the heap, like the C's array. There are two primitive operations that creates a ByteArray# of specified size - one allocates memory in normal heap and so this byte array can be moved each time when garbage collection occurs. This prevents converting of ByteArray# to plain memory pointer that can be used in C procedures (although it's still possible to pass current ByteArray# pointer to "unsafe foreign" procedure if it don't try to store this pointer somewhere). The second primitive allocates ByteArray# of specified size in "pinned" heap area, which contains objects with fixed place. Such byte array will never be moved by GC so it's address can be used as plain Ptr and shared with C world. First way to create ByteArray# used inside implementation of all UArray types, second way used in StorableArray (although StorableArray can also point to data, allocated by C malloc).

There is also MutableByteArray# type what don't had much difference with ByteArray#, but GHC's primitives support only monadic read/write operations for MutableByteArray#, and only pure reads for ByteArray#, plus unsafeFreeze/unsafeThaw operations that changes appropriate fields in headers of this arrays. This differentiation don't make much sense except for additional safety checks.

So, pinned MutableByteArray# or C malloced memory used inside StorableArray, unpinned MutableByteArray# used inside IOUArray and STUArray, and unpinned ByteArray# are used inside UArray.

Both boxed and unboxed arrays API are almost the same:

marr <- alloc n - allocates mutable array with given size arr <- unsafeFreeze marr - converts mutable array to immutable one marr <- unsafeThaw arr - converts immutable array to mutable one x <- unsafeRead marr i - monadic reading of value with given index from mutable array unsafeWrite marr i x - monadic writing of value with given index from mutable array let x = unsafeAt arr i - pure reading of value with given index from immutable array (all indexes are counted from 0)

Based on these primitive operations, the arrays library implements indexing with any type and with any lower bound, bounds checking and all other high-level operations. Operations that creates/updates immutable arrays just creates them as mutable arrays in ST monad, make all required updates on this array and then use unsafeFreeze before returning array from runST. Operations on IO arrays are implemented via operations on ST arrays using stToIO operation.

Mutable arrays and GC [ edit ]

GHC implements 2-stage GC which is very fast - minor GC occurs after each 256 kb allocated and scans only this area (plus recent stack frames) when searching for a "live" data. This solution uses the fact that usual Haskell data are immutable and therefore any data structures that was created before previous minor GC can't point to the data structures created after this GC (due to immutability data can contain only "backward" references).

But this simplicity breaks when we add to the language mutable boxed references (IORef/STRef) and arrays (IOArray/STArray). On each GC, including minor ones, each element in mutable data structures should be scanned because it may be updated since last GC and now point to the data allocated since last GC.

For programs that contains a lot of data in mutable boxed arrays/references GC times may easily outweigh useful computation time. Ironically, one of such programs is GHC itself. Solution for such programs is to add to cmdline option like "+RTS -A10m" which increases size of minor GC chunks from 256 kb to 10 mb, i.e. makes minor GC 40 times less frequent. You can see effect of this change using "+RTS -sstderr" option - "%GC time" should significantly decrease.

There is a way to include this option into your executable so it will be used automatically on each execution - you should just add to your project C source file that contains the following line:

char *ghc_rts_opts = "-A10m";

Of course, you can increase or decrease this value according to your needs.

Increasing "-A" value don't comes for free - aside from obvious increasing of memory usage, execution times (of useful code) will also grow. Default "-A" value tuned to be close to modern CPU cache sizes that means that most of memory references are fall inside the cache. When 10 mb of memory are allocated before doing GC, this data locality is no more holds. So, increasing "-A" can either increase or decrease program speed, depending on its nature. Try various settings between 64 kb and 16 mb while running program with "typical" parameters and try to select a best setting for your concrete program and cpu combination.

There is also another way to avoid increasing GC times - use either unboxed or immutable arrays. Also note that immutable arrays are build as mutable ones and then "freezed", so during the construction time GC will also scan their contents.

Hopefully, GHC 6.6 fixed the problem - it remembers what references/arrays was updated since last GC and scans only them. You can suffer from the old problems only in the case when you use very large arrays.

Further information:



