scala.collection

# parallel

#### package parallel

Package object for parallel collections.

Source
  package.scala

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### Type Members

1. #### trait AdaptiveWorkStealingForkJoinTasks extends ForkJoinTasks with AdaptiveWorkStealingTasks

2. #### trait AdaptiveWorkStealingTasks extends Tasks

   This trait implements scheduling by employing an adaptive work stealing technique.

3. #### implicit class CollectionsHaveToParArray[C, T] extends AnyRef

   Adds toParArray method to collection classes.

4. #### trait Combiner[-Elem, +To] extends Builder[Elem, To] with Sizing with Parallel

   The base trait for all combiners.

   The base trait for all combiners. A combiner incremental collection construction just like a regular builder, but also implements an efficient merge operation of two builders via `combine` method. Once the collection is constructed, it may be obtained by invoking the `result` method.

   The complexity of the `combine` method should be less than linear for best performance. The `result` method doesn't have to be a constant time operation, but may be performed in parallel.

   Elem

     the type of the elements added to the builder

   To

     the type of the collection the builder produces

   Since

     2.9

5. #### trait CombinerFactory[U, Repr] extends AnyRef

6. #### class ExecutionContextTaskSupport extends TaskSupport with ExecutionContextTasks

   A task support that uses an execution context to schedule tasks.

   A task support that uses an execution context to schedule tasks.

   It can be used with the default execution context implementation in the `scala.concurrent` package. It internally forwards the call to either a forkjoin based task support or a thread pool executor one, depending on what the execution context uses.

   By default, parallel collections are parametrized with this task support object, so parallel collections share the same execution context backend as the rest of the `scala.concurrent` package.

   See also

     scala.collection.parallel.TaskSupport for more information.

7. #### trait ExecutionContextTasks extends Tasks

   This tasks implementation uses execution contexts to spawn a parallel computation.

   This tasks implementation uses execution contexts to spawn a parallel computation.

   As an optimization, it internally checks whether the execution context is the standard implementation based on fork/join pools, and if it is, creates a `ForkJoinTaskSupport` that shares the same pool to forward its request to it.

   Otherwise, it uses an execution context exclusive `Tasks` implementation to divide the tasks into smaller chunks and execute operations on it.

8. #### trait FactoryOps[From, Elem, To] extends AnyRef

9. #### class ForkJoinTaskSupport extends TaskSupport with AdaptiveWorkStealingForkJoinTasks

   A task support that uses a fork join pool to schedule tasks.

   A task support that uses a fork join pool to schedule tasks.

   See also

     scala.collection.parallel.TaskSupport for more information.

10. #### trait ForkJoinTasks extends Tasks with HavingForkJoinPool

    An implementation trait for parallel tasks based on the fork/join framework.

11. #### trait HavingForkJoinPool extends AnyRef

    A trait describing objects that provide a fork/join pool.

12. #### trait IterableSplitter[+T] extends AugmentedIterableIterator[T] with Splitter[T] with Signalling with DelegatedSignalling

    Parallel iterators allow splitting and provide a `remaining` method to obtain the number of elements remaining in the iterator.

    Parallel iterators allow splitting and provide a `remaining` method to obtain the number of elements remaining in the iterator.

    T

      type of the elements iterated.

13. #### trait ParIterable[+T] extends GenIterable[T] with GenericParTemplate[T, ParIterable] with ParIterableLike[T, ParIterable[T], scala.Iterable[T]]

    A template trait for parallel iterable collections.

    A template trait for parallel iterable collections.

    This is a base trait for Scala parallel collections. It defines behaviour common to all parallel collections. Concrete parallel collections should inherit this trait and `ParIterable` if they want to define specific combiner factories.

    Parallel operations are implemented with divide and conquer style algorithms that parallelize well. The basic idea is to split the collection into smaller parts until they are small enough to be operated on sequentially.

    All of the parallel operations are implemented as tasks within this trait. Tasks rely on the concept of splitters, which extend iterators. Every parallel collection defines:

        def splitter: IterableSplitter[T]

    which returns an instance of `IterableSplitter[T]`, which is a subtype of `Splitter[T]`. Splitters have a method `remaining` to check the remaining number of elements, and method `split` which is defined by splitters. Method `split` divides the splitters iterate over into disjunct subsets:

        def split: Seq[Splitter]

    which splits the splitter into a sequence of disjunct subsplitters. This is typically a very fast operation which simply creates wrappers around the receiver collection. This can be repeated recursively.

    Tasks are scheduled for execution through a scala.collection.parallel.TaskSupport object, which can be changed through the `tasksupport` setter of the collection.

    Method `newCombiner` produces a new combiner. Combiners are an extension of builders. They provide a method `combine` which combines two combiners and returns a combiner containing elements of both combiners. This method can be implemented by aggressively copying all the elements into the new combiner or by lazily binding their results. It is recommended to avoid copying all of the elements for performance reasons, although that cost might be negligible depending on the use case. Standard parallel collection combiners avoid copying when merging results, relying either on a two-step lazy construction or specific data-structure properties.

    Methods:

        def seq: Sequential
        def par: Repr

    produce the sequential or parallel implementation of the collection, respectively. Method `par` just returns a reference to this parallel collection. Method `seq` is efficient - it will not copy the elements. Instead, it will create a sequential version of the collection using the same underlying data structure. Note that this is not the case for sequential collections in general - they may copy the elements and produce a different underlying data structure.

    The combination of methods `toMap`, `toSeq` or `toSet` along with `par` and `seq` is a flexible way to change between different collection types.

    Since this trait extends the `GenIterable` trait, methods like `size` must also be implemented in concrete collections, while `iterator` forwards to `splitter` by default.

    Each parallel collection is bound to a specific fork/join pool, on which dormant worker threads are kept. The fork/join pool contains other information such as the parallelism level, that is, the number of processors used. When a collection is created, it is assigned the default fork/join pool found in the `scala.parallel` package object.

    Parallel collections are not necessarily ordered in terms of the `foreach` operation (see `Traversable`). Parallel sequences have a well defined order for iterators - creating an iterator and traversing the elements linearly will always yield the same order. However, bulk operations such as `foreach`, `map` or `filter` always occur in undefined orders for all parallel collections.

    Existing parallel collection implementations provide strict parallel iterators. Strict parallel iterators are aware of the number of elements they have yet to traverse. It's also possible to provide non-strict parallel iterators, which do not know the number of elements remaining. To do this, the new collection implementation must override `isStrictSplitterCollection` to `false`. This will make some operations unavailable.

    To create a new parallel collection, extend the `ParIterable` trait, and implement `size`, `splitter`, `newCombiner` and `seq`. Having an implicit combiner factory requires extending this trait in addition, as well as providing a companion object, as with regular collections.

    Method `size` is implemented as a constant time operation for parallel collections, and parallel collection operations rely on this assumption.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    T

      the element type of the collection

    Since

      2.9

14. #### trait ParIterableLike[+T, +Repr <: ParIterable[T], +Sequential <: scala.Iterable[T] with IterableLike[T, Sequential]] extends GenIterableLike[T, Repr] with CustomParallelizable[T, Repr] with Parallel with HasNewCombiner[T, Repr]

    A template trait for parallel collections of type `ParIterable[T]`.

    A template trait for parallel collections of type `ParIterable[T]`.

    This is a base trait for Scala parallel collections. It defines behaviour common to all parallel collections. Concrete parallel collections should inherit this trait and `ParIterable` if they want to define specific combiner factories.

    Parallel operations are implemented with divide and conquer style algorithms that parallelize well. The basic idea is to split the collection into smaller parts until they are small enough to be operated on sequentially.

    All of the parallel operations are implemented as tasks within this trait. Tasks rely on the concept of splitters, which extend iterators. Every parallel collection defines:

        def splitter: IterableSplitter[T]

    which returns an instance of `IterableSplitter[T]`, which is a subtype of `Splitter[T]`. Splitters have a method `remaining` to check the remaining number of elements, and method `split` which is defined by splitters. Method `split` divides the splitters iterate over into disjunct subsets:

        def split: Seq[Splitter]

    which splits the splitter into a sequence of disjunct subsplitters. This is typically a very fast operation which simply creates wrappers around the receiver collection. This can be repeated recursively.

    Tasks are scheduled for execution through a scala.collection.parallel.TaskSupport object, which can be changed through the `tasksupport` setter of the collection.

    Method `newCombiner` produces a new combiner. Combiners are an extension of builders. They provide a method `combine` which combines two combiners and returns a combiner containing elements of both combiners. This method can be implemented by aggressively copying all the elements into the new combiner or by lazily binding their results. It is recommended to avoid copying all of the elements for performance reasons, although that cost might be negligible depending on the use case. Standard parallel collection combiners avoid copying when merging results, relying either on a two-step lazy construction or specific data-structure properties.

    Methods:

        def seq: Sequential
        def par: Repr

    produce the sequential or parallel implementation of the collection, respectively. Method `par` just returns a reference to this parallel collection. Method `seq` is efficient - it will not copy the elements. Instead, it will create a sequential version of the collection using the same underlying data structure. Note that this is not the case for sequential collections in general - they may copy the elements and produce a different underlying data structure.

    The combination of methods `toMap`, `toSeq` or `toSet` along with `par` and `seq` is a flexible way to change between different collection types.

    Since this trait extends the `GenIterable` trait, methods like `size` must also be implemented in concrete collections, while `iterator` forwards to `splitter` by default.

    Each parallel collection is bound to a specific fork/join pool, on which dormant worker threads are kept. The fork/join pool contains other information such as the parallelism level, that is, the number of processors used. When a collection is created, it is assigned the default fork/join pool found in the `scala.parallel` package object.

    Parallel collections are not necessarily ordered in terms of the `foreach` operation (see `Traversable`). Parallel sequences have a well defined order for iterators - creating an iterator and traversing the elements linearly will always yield the same order. However, bulk operations such as `foreach`, `map` or `filter` always occur in undefined orders for all parallel collections.

    Existing parallel collection implementations provide strict parallel iterators. Strict parallel iterators are aware of the number of elements they have yet to traverse. It's also possible to provide non-strict parallel iterators, which do not know the number of elements remaining. To do this, the new collection implementation must override `isStrictSplitterCollection` to `false`. This will make some operations unavailable.

    To create a new parallel collection, extend the `ParIterable` trait, and implement `size`, `splitter`, `newCombiner` and `seq`. Having an implicit combiner factory requires extending this trait in addition, as well as providing a companion object, as with regular collections.

    Method `size` is implemented as a constant time operation for parallel collections, and parallel collection operations rely on this assumption.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    T

      the element type of the collection

    Repr

      the type of the actual collection containing the elements

15. #### trait ParMap[K, +V] extends GenMap[K, V] with GenericParMapTemplate[K, V, ParMap] with ParIterable[(K, V)] with ParMapLike[K, V, ParMap[K, V], Map[K, V]]

    A template trait for parallel maps.

    A template trait for parallel maps.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    K

      the key type of the map

    V

      the value type of the map

    Since

      2.9

16. #### trait ParMapLike[K, +V, +Repr <: ParMapLike[K, V, Repr, Sequential] with ParMap[K, V], +Sequential <: Map[K, V] with MapLike[K, V, Sequential]] extends GenMapLike[K, V, Repr] with ParIterableLike[(K, V), Repr, Sequential]

    A template trait for mutable parallel maps.

    A template trait for mutable parallel maps. This trait is to be mixed in with concrete parallel maps to override the representation type.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    K

      the key type of the map

    V

      the value type of the map

    Since

      2.9

17. #### trait ParSeq[+T] extends GenSeq[T] with ParIterable[T] with GenericParTemplate[T, ParSeq] with ParSeqLike[T, ParSeq[T], scala.Seq[T]]

    A template trait for parallel sequences.

    A template trait for parallel sequences.

    Parallel sequences inherit the `Seq` trait. Their indexing and length computations are defined to be efficient. Like their sequential counterparts they always have a defined order of elements. This means they will produce resulting parallel sequences in the same way sequential sequences do. However, the order in which they perform bulk operations on elements to produce results is not defined and is generally nondeterministic. If the higher-order functions given to them produce no sideeffects, then this won't be noticeable.

    This trait defines a new, more general `split` operation and reimplements the `split` operation of `ParallelIterable` trait using the new `split` operation.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    T

      the type of the elements in this parallel sequence

18. #### trait ParSeqLike[+T, +Repr <: ParSeq[T], +Sequential <: scala.Seq[T] with SeqLike[T, Sequential]] extends GenSeqLike[T, Repr] with ParIterableLike[T, Repr, Sequential]

    A template trait for sequences of type `ParSeq[T]`, representing parallel sequences with element type `T`.

    A template trait for sequences of type `ParSeq[T]`, representing parallel sequences with element type `T`.

    Parallel sequences inherit the `Seq` trait. Their indexing and length computations are defined to be efficient. Like their sequential counterparts they always have a defined order of elements. This means they will produce resulting parallel sequences in the same way sequential sequences do. However, the order in which they perform bulk operations on elements to produce results is not defined and is generally nondeterministic. If the higher-order functions given to them produce no sideeffects, then this won't be noticeable.

    This trait defines a new, more general `split` operation and reimplements the `split` operation of `ParallelIterable` trait using the new `split` operation.

    T

      the type of the elements contained in this collection

    Repr

      the type of the actual collection containing the elements

    Sequential

      the type of the sequential version of this parallel collection

19. #### trait ParSet[T] extends GenSet[T] with GenericParTemplate[T, ParSet] with ParIterable[T] with ParSetLike[T, ParSet[T], Set[T]]

    A template trait for parallel sets.

    A template trait for parallel sets.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    T

      the element type of the set

    Since

      2.9

20. #### trait ParSetLike[T, +Repr <: ParSetLike[T, Repr, Sequential] with ParSet[T], +Sequential <: Set[T] with SetLike[T, Sequential]] extends GenSetLike[T, Repr] with ParIterableLike[T, Repr, Sequential]

    A template trait for parallel sets.

    A template trait for parallel sets. This trait is mixed in with concrete parallel sets to override the representation type.

    The higher-order functions passed to certain operations may contain side-effects. Since implementations of bulk operations may not be sequential, this means that side-effects may not be predictable and may produce data-races, deadlocks or invalidation of state if care is not taken. It is up to the programmer to either avoid using side-effects or to use some form of synchronization when accessing mutable data.

    T

      the element type of the set

    Since

      2.9

21. #### trait PreciseSplitter[+T] extends Splitter[T]

    A precise splitter (or a precise split iterator) can be split into arbitrary number of splitters that traverse disjoint subsets of arbitrary sizes.

    A precise splitter (or a precise split iterator) can be split into arbitrary number of splitters that traverse disjoint subsets of arbitrary sizes.

    Implementors might want to override the parameterless `split` method for efficiency.

    T

      type of the elements this splitter traverses

    Since

      2.9

22. #### trait SeqSplitter[+T] extends IterableSplitter[T] with AugmentedSeqIterator[T] with PreciseSplitter[T]

    Parallel sequence iterators allow splitting into arbitrary subsets.

    Parallel sequence iterators allow splitting into arbitrary subsets.

    T

      type of the elements iterated.

23. #### trait Splitter[+T] extends Iterator[T]

    A splitter (or a split iterator) can be split into more splitters that traverse over disjoint subsets of elements.

    A splitter (or a split iterator) can be split into more splitters that traverse over disjoint subsets of elements.

    T

      type of the elements this splitter traverses

    Since

      2.9

24. #### trait Task[R, +Tp] extends AnyRef

25. #### trait TaskSupport extends Tasks

    A trait implementing the scheduling of a parallel collection operation.

    A trait implementing the scheduling of a parallel collection operation.

    Parallel collections are modular in the way operations are scheduled. Each parallel collection is parametrized with a task support object which is responsible for scheduling and load-balancing tasks to processors.

    A task support object can be changed in a parallel collection after it has been created, but only during a quiescent period, i.e. while there are no concurrent invocations to parallel collection methods.

    There are currently a few task support implementations available for parallel collections. The scala.collection.parallel.ForkJoinTaskSupport uses a fork-join pool internally.

    The scala.collection.parallel.ExecutionContextTaskSupport uses the default execution context implementation found in scala.concurrent, and it reuses the thread pool used in scala.concurrent.

    The execution context task support is set to each parallel collection by default, so parallel collections reuse the same fork-join pool as the future API.

    Here is a way to change the task support of a parallel collection:

        import scala.collection.parallel._
        val pc = mutable.ParArray(1, 2, 3)
        pc.tasksupport = new ForkJoinTaskSupport(
          new scala.concurrent.forkjoin.ForkJoinPool(2))

    See also

      Configuring Parallel Collections section on the parallel collection's guide for more information.

26. #### trait Tasks extends AnyRef

    A trait that declares task execution capabilities used by parallel collections.

27. #### trait TraversableOps[T] extends AnyRef

28. #### trait AdaptiveWorkStealingThreadPoolTasks extends ThreadPoolTasks with AdaptiveWorkStealingTasks

    Annotations
      @deprecated
    Deprecated

      (Since version 2.11.0) Use `AdaptiveWorkStealingForkJoinTasks` instead.

29. #### final case class CompositeThrowable(throwables: Set[Throwable]) extends Exception with Product with Serializable

    Composite throwable - thrown when multiple exceptions are thrown at the same time.

    Composite throwable - thrown when multiple exceptions are thrown at the same time.

    Annotations
      @deprecated
    Deprecated

      (Since version 2.11.0) This class will be removed.

30. #### class ThreadPoolTaskSupport extends TaskSupport with AdaptiveWorkStealingThreadPoolTasks

    A task support that uses a thread pool executor to schedule tasks.

    A task support that uses a thread pool executor to schedule tasks.

    Annotations
      @deprecated
    Deprecated

      (Since version 2.11.0) Use `ForkJoinTaskSupport` instead.

    See also

      scala.collection.parallel.TaskSupport for more information.

31. #### trait ThreadPoolTasks extends Tasks

    An implementation of tasks objects based on the Java thread pooling API.

    An implementation of tasks objects based on the Java thread pooling API.

    Annotations
      @deprecated
    Deprecated

      (Since version 2.11.0) Use `ForkJoinTasks` instead.

32. #### trait ThrowableOps extends AnyRef

    Annotations
      @deprecated
    Deprecated

      (Since version 2.11.0) This trait will be removed.

### Value Members

1. #### val CHECK_RATE: Int

2. #### object ForkJoinTasks

3. #### object FutureThreadPoolTasks

4. #### val MIN_FOR_COPY: Int

5. #### object ParIterable extends ParFactory[ParIterable]

   This object provides a set of operations to create ``ParIterable`` values.

6. #### object ParMap extends ParMapFactory[ParMap]

7. #### object ParSeq extends ParFactory[ParSeq]

8. #### object ParSet extends ParSetFactory[ParSet]

9. #### val SQRT2: Double

10. #### object Splitter

11. #### val availableProcessors: Int

12. #### val defaultTaskSupport: TaskSupport

13. #### package immutable

14. #### package mutable

15. #### def setTaskSupport[Coll](c: Coll, t: TaskSupport): Coll

16. #### def thresholdFromSize(sz: Int, parallelismLevel: Int): Int

    Computes threshold from the size of the collection and the parallelism level.

### Deprecated Value Members

1. #### object ThreadPoolTasks

   Annotations
     @deprecated
   Deprecated

     (Since version 2.11.0) Use `ForkJoinTasks` instead.

### Inherited from AnyRef

### Inherited from Any

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