{-|
The @monad-par@ package provides a family of @Par@ monads, for
speeding up pure computations using parallel processors. (for a similar
programming model for use with @IO@, see "Control.Monad.Par.IO".)
The result of a given @Par@ computation is always the same - i.e. it
is deterministic, but the computation may be performed more quickly
if there are processors available to share the work.
For example, the following program fragment computes the values of
@(f x)@ and @(g x)@ in parallel, and returns a pair of their results:
> runPar $ do
> fx <- spawn (return (f x)) -- start evaluating (f x)
> gx <- spawn (return (g x)) -- start evaluating (g x)
> a <- get fx -- wait for fx
> b <- get gx -- wait for gx
> return (a,b) -- return results
@Par@ can be used for specifying pure parallel computations in
which the order of the computation is not known beforehand.
The programmer specifies how information flows from one
part of the computation to another, but not the order in which
computations will be evaluated at runtime. Information flow is
described using "variables" called @IVar@s, which support 'put' and
'get' operations. For example, suppose you have a problem that
can be expressed as a network with four nodes, where @b@ and @c@
require the value of @a@, and @d@ requires the value of @b@ and @c@:
> a
> / \
> b c
> \ /
> d
Then you could express this in the @Par@ monad like this:
> runPar $ do
> [a,b,c,d] <- sequence [new,new,new,new]
> fork $ do x <- get a; put b (x+1)
> fork $ do x <- get a; put c (x+2)
> fork $ do x <- get b; y <- get c; put d (x+y)
> fork $ do put a (3 :: Int)
> get d
The result of the above computation is always 9. The 'get' operation
waits until its input is available; multiple 'put's to the same
@IVar@ are not allowed, and result in a runtime error. Values
stored in @IVar@s are usually fully evaluated (although there are
ways provided to pass lazy values if necessary).
In the above example, @b@ and @c@ will be evaluated in parallel.
In practice the work involved at each node is too small here to see
the benefits of parallelism though: typically each node should
involve much more work. The granularity is completely under your
control - too small and the overhead of the @Par@ monad will
outweigh any parallelism benefits, whereas if the nodes are too
large then there might not be enough parallelism to use all the
available processors.
Unlike @Control.Parallel@, in @Control.Monad.Par@ parallelism is
not combined with laziness, so sharing and granulairty are
completely under the control of the programmer. New units of
parallel work are only created by @fork@ and a few other
combinators.
The default implementation is based on a work-stealing scheduler
that divides the work as evenly as possible between the available
processors at runtime. Other schedulers are available that are
based on different policies and have different performance
characteristics. To use one of these other schedulers, just import
its module instead of "Control.Monad.Par":
* "Control.Monad.Par.Scheds.Trace"
* "Control.Monad.Par.Scheds.Sparks"
For more information on the programming model, please see these sources:
* The wiki\/tutorial ()
* The original paper ()
* Tutorial slides ()
* Other slides: (,
)
-}
module Control.Monad.Par
(
-- * The Par Monad
Par,
runPar, runParIO,
fork,
-- | forks a computation to happen in parallel. The forked
-- computation may exchange values with other computations using
-- @IVar@s.
-- * Communication: IVars
IVar,
new,
-- | creates a new @IVar@
newFull,
-- | creates a new @IVar@ that contains a value
newFull_,
-- | creates a new @IVar@ that contains a value (head-strict only)
get,
-- | read the value in an @IVar@. 'get' can only return when the
-- value has been written by a prior or parallel @put@ to the same
-- @IVar@.
put,
-- | put a value into a @IVar@. Multiple 'put's to the same @IVar@
-- are not allowed, and result in a runtime error.
--
-- 'put' fully evaluates its argument, which therefore must be an
-- instance of 'NFData'. The idea is that this forces the work to
-- happen when we expect it, rather than being passed to the consumer
-- of the @IVar@ and performed later, which often results in less
-- parallelism than expected.
--
-- Sometimes partial strictness is more appropriate: see 'put_'.
--
put_,
-- | like 'put', but only head-strict rather than fully-strict.
-- * Operations
spawn,
-- | Like 'fork', but returns a @IVar@ that can be used to query the
-- result of the forked computataion. Therefore @spawn@ provides /futures/ or /promises/.
--
-- > spawn p = do
-- > r <- new
-- > fork (p >>= put r)
-- > return r
--
spawn_,
-- | Like 'spawn', but the result is only head-strict, not fully-strict.
spawnP,
-- | Spawn a pure (rather than monadic) computation. Fully-strict.
--
-- > spawnP = spawn . return
module Control.Monad.Par.Combinator,
-- | This module also reexports the Combinator library for backwards
-- compatibility with version 0.1.
NFData()
-- | /(0.3)/ Reexport 'NFData' for fully-strict operators.
)
where
import Control.Monad.Par.Class hiding ( spawn, spawn_, spawnP, put, put_
, get, newFull, new, fork, newFull_ )
import Control.Monad.Par.Scheds.Trace
import Control.Monad.Par.Combinator