Main type for scheduling work. See withScheduler or withScheduler_ for ways to construct and use this data type.
Modulescheduler-2.0.1.0Haskell2010
Control.Scheduler
- 8 types
- 38 values
- Packagescheduler-2.0.1.0
- Exports48
- LanguageHaskell2010
- LicenceBSD-3-Clause
- SourceTypes.hs
Scheduler
3 declarationsThis is a wrapper around Scheduler, but it also keeps a separate state for each individual worker. See withSchedulerWS or withSchedulerWS_ for ways to construct and use this data type.
Computed results of scheduled jobs.
Constructors
Finished [a]Finished normally with all scheduled jobs completed
FinishedEarly [a] !aFinished early by the means of cancelBatch or terminate.
FinishedEarlyWith !aFinished early by the means of cancelBatchWith or terminateWith.
Instances8Functor, Foldable, Traversable, Eq1, Show1, Eq, …
Functor ResultsDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesFoldable ResultsDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesTraversable ResultsDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesEq1 ResultsDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesShow1 ResultsDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesEq a => Eq (Results a)Defined in scheduler-2.0.1.0 · Control.Scheduler.TypesRead a => Read (Results a)Defined in scheduler-2.0.1.0 · Control.Scheduler.TypesShow a => Show (Results a)Defined in scheduler-2.0.1.0 · Control.Scheduler.Types
Regular
withScheduler :: MonadUnliftIO m=> CompComputation strategy
-> (Scheduler RealWorld a -> m b)Action that will be scheduling all the work.
-> m [a]
Initialize a scheduler and submit jobs that will be computed sequentially or in parallelel, which is determined by the Computation strategy.
Here are some cool properties about the withScheduler:
This function will block until all of the submitted jobs have finished or at least one of them resulted in an exception, which will be re-thrown at the callsite.
It is totally fine for nested jobs to submit more jobs for the same or other scheduler
It is ok to initialize multiple schedulers at the same time, although that will likely result in suboptimal performance, unless workers are pinned to different capabilities.
Warning It is pretty dangerous to schedule jobs that can block, because it might lead to a potential deadlock, if you are not careful. Consider this example. First execution works fine, since there are two scheduled workers, and one can unblock the other, but the second scenario immediately results in a deadlock.
withScheduler (ParOn [1,2]) $ \s -> newEmptyMVar >>= (\ mv -> scheduleWork s (readMVar mv) >> scheduleWork s (putMVar mv ()))[(),()]import System.Timeouttimeout 1000000 $ withScheduler (ParOn [1]) $ \s -> newEmptyMVar >>= (\ mv -> scheduleWork s (readMVar mv) >> scheduleWork s (putMVar mv ()))Nothing
Important: In order to get work done truly in parallel, program needs to be compiled with
-threaded GHC flag and executed with +RTS -N -RTS to use all available cores.
withScheduler_ :: MonadUnliftIO m=> CompComputation strategy
-> (Scheduler RealWorld a -> m b)Action that will be scheduling all the work.
-> m ()
Same as withScheduler, but discards results of submitted jobs.
withSchedulerR :: MonadUnliftIO m=> CompComputation strategy
-> (Scheduler RealWorld a -> m b)Action that will be scheduling all the work.
-> m (Results a)
Same as withScheduler, except instead of a list it produces Results, which allows for distinguishing between the ways computation was terminated.
Stateful workers
Run a scheduler with stateful workers. Throws MutexException if an attempt is made to concurrently use the same WorkerStates with another SchedulerWS.
Examples
A good example of using stateful workers would be generation of random number in parallel. A lof of times random number generators are not thread safe, so we can work around this problem with a separate stateful generator for each of the workers.
import Control.Monad as M ((>=>), replicateM)import Control.Concurrent (yield, threadDelay)import Data.List (sort)-- ^ Above imports are used to make sure output is deterministic, which is needed for doctestimport System.Random.MWC as MWCimport Data.Vector.Unboxed as V (singleton)states <- initWorkerStates (ParN 4) (MWC.initialize . V.singleton . fromIntegral . getWorkerId)let scheduleGen scheduler = scheduleWorkState scheduler (MWC.uniform >=> \r -> yield >> threadDelay 200000 >> pure r)sort <$> withSchedulerWS states (M.replicateM 4 . scheduleGen) :: IO [Double][0.21734983682025255,0.5000843862105709,0.5759825622603018,0.8587171114177893]sort <$> withSchedulerWS states (M.replicateM 4 . scheduleGen) :: IO [Double][2.3598617298033475e-2,9.949679290089553e-2,0.38223134248645885,0.7408640677124702]
In the above example we use four different random number generators from
`mwc-random` in order to generate 4
numbers, all in separate threads. The subsequent call to the withSchedulerWS function
with the same states is allowed to reuse the same generators, thus avoiding expensive
initialization.
Side note - The example presented was crafted with slight trickery in order to guarantee that the output is deterministic, so if you run instructions exactly the same way in GHCI you will get the exact same output. Non-determinism comes from thread scheduling, rather than from random number generator, because we use exactly the same seed for each worker, but workers run concurrently. Exact output is not really needed, except for the doctests to pass.
Run a scheduler with stateful workers, while discarding computation results.
Same as withSchedulerWS, except instead of a list it produces Results, which allows for distinguishing between the ways computation was terminated.
Get the underlying Scheduler, which cannot access WorkerStates.
Trivial (no parallelism)
The most basic scheduler that simply runs the task instead of scheduling it. Early termination requests are bluntly ignored.
This trivial scheduler will behave in the same way as withScheduler with Seq computation strategy, except it is restricted to PrimMonad, instead of MonadUnliftIO.
This trivial scheduler will behave in a similar way as withSchedulerR with Seq computation strategy, except it is restricted to PrimMonad, instead of MonadUnliftIO and the work isn't scheduled, but rather computed immediately.
Scheduling computation
8 declarationsSchedule an action to be picked up and computed by a worker from a pool of jobs. Similar to scheduleWorkId, except the job doesn't get the worker id.
Same as scheduleWork, but only for a Scheduler that doesn't keep the results.
Schedule an action to be picked up and computed by a worker from a pool of jobs. Argument supplied to the job will be the id of the worker doing the job. This is useful for identification of a thread that will be doing the work, since there is one-to-one mapping from ThreadId to WorkerId for a particular scheduler.
Same as scheduleWorkId, but only for a Scheduler that doesn't keep the results.
Schedule a job that will get a worker state passed as an argument
Same as scheduleWorkState, but dont' keep the result of computation.
Schedule the same action to run n times concurrently. This differs from
replicateConcurrently by allowing the caller to use the Scheduler freely,
or to allow early termination via terminate across all (identical) threads.
To be called within a withScheduler block.
Same as replicateWork, but it does not retain the results of scheduled jobs
Batches
9 declarationsBatch is an artifical checkpoint that can be controlled by the user throughout the lifetime of a scheduler.
Run a single batch of jobs. Supplied action will not return until all jobs placed on the queue are done or the whole batch is cancelled with one of these cancelBatch, cancelBatch_ or cancelBatchWith.
It waits for all scheduled jobs to finish and collects the computed results into a list. It is a blocking operation, but if there are no jobs in progress it will return immediately. It is safe to continue using the supplied scheduler after this function returns. However, if any of the jobs resulted in an exception it will be rethrown by this function, which, unless caught, will further put the scheduler in a terminated state.
It is important to note that any job that hasn't had its results collected from the scheduler prior to starting the batch it will end up on the batch result list.
Same as runBatch, except it ignores results of computation
Cancel batch with supplied identifier, which will lead to scheduler to return FinishedEarly result. This is an idempotent operation and has no affect if currently running batch does not match supplied identifier. Returns False when cancelling did not succeed due to mismatched identifier or does not return at all since all jobs get cancelled immediately. For trivial schedulers however there is no way to perform concurrent cancelation and it will return True.
Same as cancelBatch, but only works with schedulers that don't care about results
Same as cancelBatch_, but the result of computation will be set to FinishedEarlyWith
Check if the supplied batch has already finished.
This function gives a way to get access to the main batch that started implicitely.
Early termination
3 declarationsAs soon as possible try to terminate any computation that is being performed by all workers managed by this scheduler and collect whatever results have been computed, with supplied element guaranteed to being the last one. In case when Results type is returned this function will cause the scheduler to produce FinishedEarly
Important - With Seq strategy this will not stop other scheduled tasks from being computed, although it will make sure their results are discarded.
Same as terminate, but returning a single element list containing the supplied argument. This can be very useful for parallel search algorithms. In case when Results is the return type this function will cause the scheduler to produce FinishedEarlyWith
Important - Same as with terminate, when Seq strategy is used, this will not prevent computation from continuing, but the scheduler will return only the result supplied to this function.
Workers
5 declarationsA unique id for the worker in the Scheduler context. It will
always be a number from 0 up to, but not including, the number of workers a scheduler
has, which in turn can always be determined with numWorkers function.
Constructors
Instances9Bounded, Enum, Eq, Integral, Num, Ord, …
Bounded WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueEnum WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueEq WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueIntegral WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueNum WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueOrd WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueRead WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueReal WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.QueueShow WorkerIdDefined in scheduler-2.0.1.0 · Control.Scheduler.Queue
Each worker is capable of keeping it's own state, that can be share for different
schedulers, but not at the same time. In other words using the same WorkerStates on
Control.Scheduler.withSchedulerS concurrently will result in an error. Can be initialized with
initWorkerStates
Get the number of workers. Will mainly depend on the computation strategy and/or number of capabilities you have. Related function is getCompWorkers.
Get the computation strategy the states where initialized with.
Initialize a separate state for each worker.
Computation strategies
4 declarationsComputation strategy to use when scheduling work.
Constructors
Instances5Eq, Show, Semigroup, Monoid, NFData
Eq CompDefined in scheduler-2.0.1.0 · Control.Scheduler.ComputationShow CompDefined in scheduler-2.0.1.0 · Control.Scheduler.ComputationSemigroup CompDefined in scheduler-2.0.1.0 · Control.Scheduler.ComputationMonoid CompDefined in scheduler-2.0.1.0 · Control.Scheduler.ComputationNFData CompDefined in scheduler-2.0.1.0 · Control.Scheduler.Computation
Parallel computation using all available cores. Same as ParOn []
Parallel computation using all available cores. Same as ParN 0
Figure out how many workers will this computation strategy create.
Note - If at any point during program execution global number of capabilities gets changed with setNumCapabilities, it will have no affect on this function, unless it hasn't yet been called with Par or Par' arguments.
Useful functions
5 declarationsReplicate an action n times and schedule them acccording to the supplied computation
strategy.
Just like replicateConcurrently, but discards the results of computation.
Map an action over each element of the Traversable t acccording to the supplied computation
strategy.
Just like traverseConcurrently, but restricted to Foldable and discards the results of computation.
This is generally a faster way to traverse while ignoring the result rather than using mapM_.
Exceptions
1 declarationIf any one of the workers dies with an exception, even if that exceptions is asynchronous, it will be re-thrown in the scheduling thread.
let didAWorkerDie = handleJust asyncExceptionFromException (return . (== ThreadKilled)) . fmap or:t didAWorkerDiedidAWorkerDie :: Foldable t => IO (t Bool) -> IO BooldidAWorkerDie $ withScheduler Par $ \ s -> scheduleWork s $ pure FalseFalsedidAWorkerDie $ withScheduler Par $ \ s -> scheduleWork s $ myThreadId >>= killThread >> pure FalseTrue
Exception that gets thrown whenever concurrent access is attempted to the WorkerStates
Instances3Eq, Show, Exception
Eq MutexExceptionDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesShow MutexExceptionDefined in scheduler-2.0.1.0 · Control.Scheduler.TypesException MutexExceptionDefined in scheduler-2.0.1.0 · Control.Scheduler.Types