{-# LANGUAGE FlexibleContexts #-} -- | -- Module : Simulation.Aivika.Trans.Processor -- Copyright : Copyright (c) 2009-2014, David Sorokin <david.sorokin@gmail.com> -- License : BSD3 -- Maintainer : David Sorokin <david.sorokin@gmail.com> -- Stability : experimental -- Tested with: GHC 7.8.3 -- -- The processor of simulation data. -- module Simulation.Aivika.Trans.Processor (-- * Processor Type Processor(..), -- * Processor Primitives emptyProcessor, arrProcessor, accumProcessor, -- * Specifying Identifier processorUsingId, -- * Prefetch and Delay Processors prefetchProcessor, delayProcessor, -- * Buffer Processor bufferProcessor, bufferProcessorLoop, -- * Processing Queues queueProcessor, queueProcessorLoopMerging, queueProcessorLoopSeq, queueProcessorLoopParallel, -- * Sequencing Processors processorSeq, -- * Parallelizing Processors processorParallel, processorQueuedParallel, processorPrioritisingOutputParallel, processorPrioritisingInputParallel, processorPrioritisingInputOutputParallel, -- * Arrival Processor arrivalProcessor, -- * Integrating with Signals signalProcessor, processorSignaling) where import qualified Control.Category as C import Control.Arrow import Simulation.Aivika.Trans.Comp import Simulation.Aivika.Trans.Simulation import Simulation.Aivika.Trans.Dynamics import Simulation.Aivika.Trans.Event import Simulation.Aivika.Trans.Cont import Simulation.Aivika.Trans.Process import Simulation.Aivika.Trans.Stream import Simulation.Aivika.Trans.QueueStrategy import Simulation.Aivika.Trans.Signal import Simulation.Aivika.Arrival (Arrival(..)) -- | Represents a processor of simulation data. newtype Processor m a b = Processor { runProcessor :: Stream m a -> Stream m b -- ^ Run the processor. } instance C.Category (Processor m) where id = Processor id Processor x . Processor y = Processor (x . y) -- The implementation is based on article -- A New Notation for Arrows by Ross Paterson, -- although my streams are different and they -- already depend on the Process monad, -- while the pure streams were considered in the -- mentioned article. instance MonadComp m => Arrow (Processor m) where arr = Processor . mapStream first (Processor f) = Processor $ \xys -> Cons $ do (xs, ys) <- liftSimulation $ unzipStream xys runStream $ zipStreamSeq (f xs) ys second (Processor f) = Processor $ \xys -> Cons $ do (xs, ys) <- liftSimulation $ unzipStream xys runStream $ zipStreamSeq xs (f ys) Processor f *** Processor g = Processor $ \xys -> Cons $ do (xs, ys) <- liftSimulation $ unzipStream xys runStream $ zipStreamSeq (f xs) (g ys) Processor f &&& Processor g = Processor $ \xs -> zipStreamSeq (f xs) (g xs) instance MonadComp m => ArrowChoice (Processor m) where left (Processor f) = Processor $ \xs -> Cons $ do ys <- liftSimulation $ memoStream xs runStream $ replaceLeftStream ys (f $ leftStream ys) right (Processor f) = Processor $ \xs -> Cons $ do ys <- liftSimulation $ memoStream xs runStream $ replaceRightStream ys (f $ rightStream ys) instance MonadComp m => ArrowZero (Processor m) where zeroArrow = emptyProcessor instance MonadComp m => ArrowPlus (Processor m) where (Processor f) <+> (Processor g) = Processor $ \xs -> Cons $ do [xs1, xs2] <- liftSimulation $ splitStream 2 xs runStream $ mergeStreams (f xs1) (g xs2) -- | A processor that never finishes its work producing an 'emptyStream'. emptyProcessor :: MonadComp m => Processor m a b emptyProcessor = Processor $ const emptyStream -- | Create a simple processor by the specified handling function -- that runs the discontinuous process for each input value to get the output. arrProcessor :: MonadComp m => (a -> Process m b) -> Processor m a b arrProcessor = Processor . mapStreamM -- | Accumulator that outputs a value determined by the supplied function. accumProcessor :: MonadComp m => (acc -> a -> Process m (acc, b)) -> acc -> Processor m a b accumProcessor f acc = Processor $ \xs -> Cons $ loop xs acc where loop xs acc = do (a, xs') <- runStream xs (acc', b) <- f acc a return (b, Cons $ loop xs' acc') -- | Create a processor that will use the specified process identifier. -- It can be useful to refer to the underlying 'Process' computation which -- can be passivated, interrupted, canceled and so on. See also the -- 'processUsingId' function for more details. processorUsingId :: MonadComp m => ProcessId m -> Processor m a b -> Processor m a b processorUsingId pid (Processor f) = Processor $ Cons . processUsingId pid . runStream . f -- | Launches the specified processors in parallel consuming the same input -- stream and producing a combined output stream. -- -- If you don't know what the enqueue strategies to apply, then -- you will probably need 'FCFS' for the both parameters, or -- function 'processorParallel' that does namely this. processorQueuedParallel :: (MonadComp m, EnqueueStrategy m si, EnqueueStrategy m so) => si -- ^ the strategy applied for enqueuing the input data -> so -- ^ the strategy applied for enqueuing the output data -> [Processor m a b] -- ^ the processors to parallelize -> Processor m a b -- ^ the parallelized processor processorQueuedParallel si so ps = Processor $ \xs -> Cons $ do let n = length ps input <- liftSimulation $ splitStreamQueueing si n xs let results = flip map (zip input ps) $ \(input, p) -> runProcessor p input output = concatQueuedStreams so results runStream output -- | Launches the specified processors in parallel using priorities for combining the output. processorPrioritisingOutputParallel :: (MonadComp m, EnqueueStrategy m si, PriorityQueueStrategy m so po) => si -- ^ the strategy applied for enqueuing the input data -> so -- ^ the strategy applied for enqueuing the output data -> [Processor m a (po, b)] -- ^ the processors to parallelize -> Processor m a b -- ^ the parallelized processor processorPrioritisingOutputParallel si so ps = Processor $ \xs -> Cons $ do let n = length ps input <- liftSimulation $ splitStreamQueueing si n xs let results = flip map (zip input ps) $ \(input, p) -> runProcessor p input output = concatPriorityStreams so results runStream output -- | Launches the specified processors in parallel using priorities for consuming the intput. processorPrioritisingInputParallel :: (MonadComp m, PriorityQueueStrategy m si pi, EnqueueStrategy m so) => si -- ^ the strategy applied for enqueuing the input data -> so -- ^ the strategy applied for enqueuing the output data -> [(Stream m pi, Processor m a b)] -- ^ the streams of input priorities and the processors -- to parallelize -> Processor m a b -- ^ the parallelized processor processorPrioritisingInputParallel si so ps = Processor $ \xs -> Cons $ do input <- liftSimulation $ splitStreamPrioritising si (map fst ps) xs let results = flip map (zip input ps) $ \(input, (_, p)) -> runProcessor p input output = concatQueuedStreams so results runStream output -- | Launches the specified processors in parallel using priorities for consuming -- the input and combining the output. processorPrioritisingInputOutputParallel :: (MonadComp m, PriorityQueueStrategy m si pi, PriorityQueueStrategy m so po) => si -- ^ the strategy applied for enqueuing the input data -> so -- ^ the strategy applied for enqueuing the output data -> [(Stream m pi, Processor m a (po, b))] -- ^ the streams of input priorities and the processors -- to parallelize -> Processor m a b -- ^ the parallelized processor processorPrioritisingInputOutputParallel si so ps = Processor $ \xs -> Cons $ do input <- liftSimulation $ splitStreamPrioritising si (map fst ps) xs let results = flip map (zip input ps) $ \(input, (_, p)) -> runProcessor p input output = concatPriorityStreams so results runStream output -- | Launches the processors in parallel consuming the same input stream and producing -- a combined output stream. This version applies the 'FCFS' strategy both for input -- and output, which suits the most part of uses cases. processorParallel :: MonadComp m => [Processor m a b] -> Processor m a b processorParallel = processorQueuedParallel FCFS FCFS -- | Launches the processors sequentially using the 'prefetchProcessor' between them -- to model an autonomous work of each of the processors specified. processorSeq :: MonadComp m => [Processor m a a] -> Processor m a a processorSeq [] = emptyProcessor processorSeq [p] = p processorSeq (p : ps) = p >>> prefetchProcessor >>> processorSeq ps -- | Create a buffer processor, where the process from the first argument -- consumes the input stream but the stream passed in as the second argument -- and produced usually by some other process is returned as an output. -- This kind of processor is very useful for modeling the queues. bufferProcessor :: MonadComp m => (Stream m a -> Process m ()) -- ^ a separate process to consume the input -> Stream m b -- ^ the resulting stream of data -> Processor m a b bufferProcessor consume output = Processor $ \xs -> Cons $ do spawnProcess (consume xs) runStream output -- | Like 'bufferProcessor' but allows creating a loop when some items -- can be processed repeatedly. It is very useful for modeling the processors -- with queues and loop-backs. bufferProcessorLoop :: MonadComp m => (Stream m a -> Stream m c -> Process m ()) -- ^ consume two streams: the input values of type @a@ -- and the values of type @c@ returned by the loop -> Stream m d -- ^ the stream of data that may become results -> Processor m d (Either e b) -- ^ process and then decide what values of type @e@ -- should be processed in the loop (this is a condition) -> Processor m e c -- ^ process in the loop and then return a value -- of type @c@ to the input again (this is a loop body) -> Processor m a b bufferProcessorLoop consume preoutput cond body = Processor $ \xs -> Cons $ do (reverted, output) <- liftSimulation $ partitionEitherStream $ runProcessor cond preoutput spawnProcess (consume xs $ runProcessor body reverted) runStream output -- | Return a processor with help of which we can model the queue. -- -- Although the function doesn't refer to the queue directly, its main use case -- is namely a processing of the queue. The first argument should be the enqueueing -- operation, while the second argument should be the opposite dequeueing operation. -- -- The reason is as follows. There are many possible combinations how the queues -- can be modeled. There is no sense to enumerate all them creating a separate function -- for each case. We can just use combinators to define exactly what we need. -- -- So, the queue can lose the input items if the queue is full, or the input process -- can suspend while the queue is full, or we can use priorities for enqueueing, -- storing and dequeueing the items in different combinations. There are so many use -- cases! -- -- There is a hope that this function along with other similar functions from this -- module is sufficient to cover the most important cases. Even if it is not sufficient -- then you can use a more generic function 'bufferProcessor' which this function is -- based on. In case of need, you can even write your own function from scratch. It is -- quite easy actually. queueProcessor :: MonadComp m => (a -> Process m ()) -- ^ enqueue the input item and wait -- while the queue is full if required -- so that there were no hanging items -> Process m b -- ^ dequeue an output item -> Processor m a b -- ^ the buffering processor queueProcessor enqueue dequeue = bufferProcessor (consumeStream enqueue) (repeatProcess dequeue) -- | Like 'queueProcessor' creates a queue processor but with a loop when some items -- can be processed and then added to the queue again. Also it allows specifying -- how two input streams of data can be merged. queueProcessorLoopMerging :: MonadComp m => (Stream m a -> Stream m d -> Stream m e) -- ^ merge two streams: the input values of type @a@ -- and the values of type @d@ returned by the loop -> (e -> Process m ()) -- ^ enqueue the input item and wait -- while the queue is full if required -- so that there were no hanging items -> Process m c -- ^ dequeue an item for the further processing -> Processor m c (Either f b) -- ^ process and then decide what values of type @f@ -- should be processed in the loop (this is a condition) -> Processor m f d -- ^ process in the loop and then return a value -- of type @d@ to the queue again (this is a loop body) -> Processor m a b -- ^ the buffering processor queueProcessorLoopMerging merge enqueue dequeue = bufferProcessorLoop (\bs cs -> consumeStream enqueue $ merge bs cs) (repeatProcess dequeue) -- | Like 'queueProcessorLoopMerging' creates a queue processor with a loop when -- some items can be processed and then added to the queue again. Only it sequentially -- merges two input streams of data: one stream that come from the external source and -- another stream of data returned by the loop. The first stream has a priority over -- the second one. queueProcessorLoopSeq :: MonadComp m => (a -> Process m ()) -- ^ enqueue the input item and wait -- while the queue is full if required -- so that there were no hanging items -> Process m c -- ^ dequeue an item for the further processing -> Processor m c (Either e b) -- ^ process and then decide what values of type @e@ -- should be processed in the loop (this is a condition) -> Processor m e a -- ^ process in the loop and then return a value -- of type @a@ to the queue again (this is a loop body) -> Processor m a b -- ^ the buffering processor queueProcessorLoopSeq = queueProcessorLoopMerging mergeStreams -- | Like 'queueProcessorLoopMerging' creates a queue processor with a loop when -- some items can be processed and then added to the queue again. Only it runs two -- simultaneous processes to enqueue the input streams of data: one stream that come -- from the external source and another stream of data returned by the loop. queueProcessorLoopParallel :: MonadComp m => (a -> Process m ()) -- ^ enqueue the input item and wait -- while the queue is full if required -- so that there were no hanging items -> Process m c -- ^ dequeue an item for the further processing -> Processor m c (Either e b) -- ^ process and then decide what values of type @e@ -- should be processed in the loop (this is a condition) -> Processor m e a -- ^ process in the loop and then return a value -- of type @a@ to the queue again (this is a loop body) -> Processor m a b -- ^ the buffering processor queueProcessorLoopParallel enqueue dequeue = bufferProcessorLoop (\bs cs -> do spawnProcess $ consumeStream enqueue bs spawnProcess $ consumeStream enqueue cs) (repeatProcess dequeue) -- | This is a prefetch processor that requests for one more data item from -- the input in advance while the latest item is not yet fully processed in -- the chain of streams, usually by other processors. -- -- You can think of this as the prefetched processor could place its latest -- data item in some temporary space for later use, which is very useful -- for modeling a sequence of separate and independent work places. prefetchProcessor :: MonadComp m => Processor m a a prefetchProcessor = Processor prefetchStream -- | Convert the specified signal transform to a processor. -- -- The processor may return data with delay as the values are requested by demand. -- Consider using the 'arrivalSignal' function to provide with the information -- about the time points at which the signal was actually triggered. -- -- The point is that the 'Stream' used in the 'Processor' is requested outside, -- while the 'Signal' is triggered inside. They are different by nature. -- The former is passive, while the latter is active. -- -- Cancel the processor's process to unsubscribe from the signals provided. signalProcessor :: MonadComp m => (Signal m a -> Signal m b) -> Processor m a b signalProcessor f = Processor $ \xs -> Cons $ do sa <- streamSignal xs sb <- signalStream (f sa) runStream sb -- | Convert the specified processor to a signal transform. -- -- The processor may return data with delay as the values are requested by demand. -- Consider using the 'arrivalSignal' function to provide with the information -- about the time points at which the signal was actually triggered. -- -- The point is that the 'Stream' used in the 'Processor' is requested outside, -- while the 'Signal' is triggered inside. They are different by nature. -- The former is passive, while the latter is active. -- -- Cancel the returned process to unsubscribe from the signal specified. processorSignaling :: MonadComp m => Processor m a b -> Signal m a -> Process m (Signal m b) processorSignaling (Processor f) sa = do xs <- signalStream sa let ys = f xs streamSignal ys -- | A processor that adds the information about the time points at which -- the original stream items were received by demand. arrivalProcessor :: MonadComp m => Processor m a (Arrival a) arrivalProcessor = Processor arrivalStream -- | A processor that delays the input stream by one step using the specified initial value. delayProcessor :: MonadComp m => a -> Processor m a a delayProcessor a0 = Processor $ delayStream a0