parallel
This trait implements scheduling by employing an adaptive work stealing technique.
Adds toParArray method to collection classes.
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.
the type of the elements added to the builder
the type of the collection the builder produces
2.9
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.
scala.collection.parallel.TaskSupport for more information.
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.
A task support that uses a fork join pool to schedule tasks.
A task support that uses a fork join pool to schedule tasks.
scala.collection.parallel.TaskSupport for more information.
An implementation trait for parallel tasks based on the fork/join framework.
A trait describing objects that provide a fork/join pool.
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.
type of the elements iterated.
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.
the element type of the collection
2.9
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.
the element type of the collection
the type of the actual collection containing the elements
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.
the key type of the map
the value type of the map
2.9
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.
the key type of the map
the value type of the map
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.
the type of the elements in this parallel sequence
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.
the type of the elements contained in this collection
the type of the actual collection containing the elements
the type of the sequential version of this parallel collection
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.
the element type of the set
2.9
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.
the element type of the set
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.
type of the elements this splitter traverses
2.9
Parallel sequence iterators allow splitting into arbitrary subsets.
Parallel sequence iterators allow splitting into arbitrary subsets.
type of the elements iterated.
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.
type of the elements this splitter traverses
2.9
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))
Configuring Parallel Collections section on the parallel collection's guide for more information.
A trait that declares task execution capabilities used by parallel collections.
(Since version 2.11.0) Use AdaptiveWorkStealingForkJoinTasks instead.
Composite throwable - thrown when multiple exceptions are thrown at the same time.
Composite throwable - thrown when multiple exceptions are thrown at the same time.
(Since version 2.11.0) This class will be removed.
A task support that uses a thread pool executor to schedule tasks.
A task support that uses a thread pool executor to schedule tasks.
(Since version 2.11.0) Use ForkJoinTaskSupport instead.
scala.collection.parallel.TaskSupport for more information.
An implementation of tasks objects based on the Java thread pooling API.
An implementation of tasks objects based on the Java thread pooling API.
(Since version 2.11.0) Use ForkJoinTasks instead.
(Since version 2.11.0) This trait will be removed.
This object provides a set of operations to create ParIterable
Computes threshold from the size of the collection and the parallelism level.
(Since version 2.11.0) Use ForkJoinTasks instead.
Package object for parallel collections.