Document Number:	N4310
Date:	2014-11-21
Revises:	N4104
Editor:	Jared Hoberock NVIDIA Corporation jhoberock@nvidia.com

Execution policies

2.1

In general

[parallel.execpol.general]

This clause describes classes that are execution policy types. An object of an execution policy type indicates the kinds of parallelism allowed in the execution of to an algorithm ~~whether it is allowed to execute in parallel~~ and expresses the consequent requirements on the element access functions.

[ Example:

std::vector<int> v = ...

// standard sequential sort
std::sort(vec.begin(), vec.end());

using namespace std::experimental::parallel;

// explicitly sequential sort
sort(seq, v.begin(), v.end());

// permitting parallel execution
sort(par, v.begin(), v.end());

// permitting vectorization as well
sort(par_vec, v.begin(), v.end());

// sort with dynamically-selected execution
size_t threshold = ...
execution_policy exec = seq;
if (v.size() > threshold)
{
  exec = par;
}

sort(exec, v.begin(), v.end());

— end example ]

[ Note: Because different parallel architectures may require idiosyncratic parameters for efficient execution, implementations of the Standard Library may provide additional execution policies to those described in this Technical Specification as extensions. — end note ]

2.2

Header `<experimental/execution_policy>` synopsis

[parallel.execpol.synopsis]

namespace std {
namespace experimental {
namespace parallel {
inline namespace v1 {
  // 2.3, Execution policy type trait
  template<class T> struct is_execution_policy;
  template<class T> constexpr bool is_execution_policy_v = is_execution_policy<T>::value;

  // 2.4, Sequential execution policy
  class sequential_execution_policy;

  // 2.5, Parallel execution policy
  class parallel_execution_policy;

  // 2.6, Parallel+Vector execution policy
  class parallel_vector_execution_policy;

  // 2.7, Dynamic execution policy
  class execution_policy;
}
}
}
}

2.3

Execution policy type trait

[parallel.execpol.type]

template<class T> struct is_execution_policy { see below };

is_execution_policy can be used to detect parallel execution policies for the purpose of excluding function signatures from otherwise ambiguous overload resolution participation.

is_execution_policy<T> shall be a UnaryTypeTrait with a BaseCharacteristic of true_type if T is the type of a standard or implementation-defined execution policy, otherwise false_type.

[ Note: This provision reserves the privilege of creating non-standard execution policies to the library implementation. — end note ]

The behavior of a program that adds specializations for is_execution_policy is undefined.

2.4

Sequential execution policy

[parallel.execpol.seq]

class sequential_execution_policy{ unspecified };

The class sequential_execution_policy is an execution policy type used as a unique type to disambiguate parallel algorithm overloading and require that a parallel algorithm's execution may not be parallelized.

2.5

Parallel execution policy

[parallel.execpol.par]

class parallel_execution_policy{ unspecified };

The class parallel_execution_policy is an execution policy type used as a unique type to disambiguate parallel algorithm overloading and indicate that a parallel algorithm's execution may be parallelized.

2.6

Parallel+Vector execution policy

[parallel.execpol.vec]

class parallel_vector_execution_policy{ unspecified };

The class class parallel_vector_execution_policy is an execution policy type used as a unique type to disambiguate parallel algorithm overloading and indicate that a parallel algorithm's execution may be vectorized and parallelized.

2.7

Dynamic execution policy

[parallel.execpol.dynamic]

class execution_policy
{
  public:
    // 2.7.1, execution_policy construct/assign
    template<class T> execution_policy(const T& exec);
    template<class T> execution_policy& operator=(const T& exec);

    // 2.7.2, execution_policy object access
    template<class T> T* get() noexcept;
    template<class T> const T* get() const noexcept;
};

The class execution_policy is a container for execution policy objects. execution_policy allows dynamic control over standard algorithm execution.

[ Example:

std::vector<float> sort_me = ...
        
using namespace std::experimental::parallel;
execution_policy exec = seq;

if(sort_me.size() > threshold)
{
  exec = std::par;
}
 
std::sort(exec, std::begin(sort_me), std::end(sort_me));

— end example ]

Objects of type execution_policy shall be constructible and assignable from objects of type T for which is_execution_policy<T>::value is true.

2.7.1

`execution_policy` construct/assign

[parallel.execpol.con]

template<class T> execution_policy(const T& exec);

Effects:: Constructs an execution_policy object with a copy of exec's state.
Remarks:: This constructor shall not participate in overload resolution unless is_execution_policy<T>::value is true.

template<class T> execution_policy& operator=(const T& exec);

Effects:: Assigns a copy of exec's state to *this.
Returns:: *this.

2.7.2

`execution_policy` object access

[parallel.execpol.access]


          const type_info& type() const noexcept;

Returns:: typeid(T), such that T is the type of the execution policy object contained by *this.


          template<class T> T* get() noexcept;
          template<class T> const T* get() const noexcept;

Returns:: If target_type() == typeid(T), a pointer to the stored execution policy object; otherwise a null pointer.
Requires:: is_execution_policy<T>::value is true.

2.8

Execution policy objects

[parallel.execpol.objects]

constexpr sequential_execution_policy      seq{};
constexpr parallel_execution_policy        par{};
constexpr parallel_vector_execution_policy par_vec{};

The header <experimental/execution_policy> declares a global object associated with each type of execution policy defined by this Technical Specification.

Parallel algorithms

[parallel.alg]

4.1

In general

[parallel.alg.general]

This clause describes components that C++ programs may use to perform operations on containers and other sequences in parallel.

4.1.1

Requirements on user-provided function objects

[parallel.alg.general.user]

Function objects passed into parallel algorithms as objects of type BinaryPredicate, Compare, and BinaryOperation shall not directly or indirectly modify objects via their arguments.

4.1.2

Effect of execution policies on algorithm execution

[parallel.alg.general.exec]

Parallel algorithms have template parameters named ExecutionPolicy which describe the manner in which the execution of these algorithms may be parallelized and the manner in which they apply the element access functions.

The invocations of element access functions in parallel algorithms invoked with an execution policy object of type sequential_execution_policy execute in sequential order in the calling thread.

The invocations of element access functions in parallel algorithms invoked with an execution policy object of type parallel_execution_policy are permitted to execute in an unordered fashion in ~~unspecified threads~~either the invoking thread or in a thread implicitly created by the library to support parallel algorithm execution. ~~, and~~ Any such invocations executing in the same thread are indeterminately sequenced ~~within each thread~~with respect to each other. [ Note: It is the caller's responsibility to ensure correctness, for example that the invocation does not introduce data races or deadlocks. — end note ]

[ Example:

using namespace std::experimental::parallel;
int a[] = {0,1};
std::vector<int> v;
for_each(par, std::begin(a), std::end(a), [&](int i) {
  v.push_back(i*2+1);
});

The program above has a data race because of the unsynchronized access to the container v. — end example ]

[ Example:

using namespace std::experimental::parallel;
std::atomic<int> x = 0;
int a[] = {1,2};
for_each(par, std::begin(a), std::end(a), [&](int n) {
  x.fetch_add(1, std::memory_order_relaxed);
  // spin wait for another iteration to change the value of x
  while (x.load(std::memory_order_relaxed) == 1) { }
});

The above example depends on the order of execution of the iterations, and is therefore undefined (may deadlock). — end example ]

[ Example:

using namespace std::experimental::parallel;
int x=0;
std::mutex m;
int a[] = {1,2};
for_each(par, std::begin(a), std::end(a), [&](int) {
  m.lock();
  ++x;
  m.unlock();
});

The above example synchronizes access to object x ensuring that it is incremented correctly. — end example ]

The invocations of element access functions in parallel algorithms invoked with an execution policy of type parallel_vector_execution_policy are permitted to execute in an unordered fashion in unspecified threads, and unsequenced with respect to one another within each thread. [ Note: This means that multiple function object invocations may be interleaved on a single thread. — end note ]

[ Note: This overrides the usual guarantee from the C++ standard, Section 1.9 [intro.execution] that function executions do not interleave with one another. — end note ]

Since parallel_vector_execution_policy allows the execution of element access functions to be interleaved on a single thread, synchronization, including the use of mutexes, risks deadlock. Thus the synchronization with parallel_vector_execution_policy is restricted as follows:

A standard library function is vectorization-unsafe if it is specified to synchronize with another function invocation, or another function invocation is specified to synchronize with it, and if it is not a memory allocation or deallocation function. Vectorization-unsafe standard library functions may not be invoked by user code called from parallel_vector_execution_policy algorithms.

[ Note: Implementations must ensure that internal synchronization inside standard library routines does not induce deadlock. — end note ]

[ Example:

using namespace std::experimental::parallel;
int x=0;
std::mutex m;
int a[] = {1,2};
for_each(par_vec, std::begin(a), std::end(a), [&](int) {
  m.lock();
  ++x;
  m.unlock();
});

The above program is invalid because the applications of the function object are not guaranteed to run on different threads. — end example ]

[ Note: The application of the function object may result in two consecutive calls to m.lock on the same thread, which may deadlock. — end note ]

[ Note: The semantics of the parallel_execution_policy or the parallel_vector_execution_policy invocation allow the implementation to fall back to sequential execution if the system cannot parallelize an algorithm invocation due to lack of resources. — end note ]

Algorithms invoked with an execution policy object of type execution_policy execute internally as if invoked with the contained execution policy object.

The semantics of parallel algorithms invoked with an execution policy object of implementation-defined type are implementation-defined.

4.1.3

`ExecutionPolicy` algorithm overloads

[parallel.alg.overloads]

The Parallel Algorithms Library provides overloads for each of the algorithms named in Table 1, corresponding to the algorithms with the same name in the C++ Standard Algorithms Library. For each algorithm in Table 1, if there are overloads for corresponding algorithms with the same name in the C++ Standard Algorithms Library, the overloads shall have an additional template type parameter named ExecutionPolicy, which shall be the first template parameter. In addition, each such overload shall have the new function parameter as the first function parameter of type ExecutionPolicy&&.

Unless otherwise specified, the semantics of ExecutionPolicy algorithm overloads are identical to their overloads without.

Parallel algorithms shall not participate in overload resolution unless is_execution_policy<decay_t<ExecutionPolicy>>::value is true.

adjacent_difference adjacent_find all_of any_of

copy copy_if copy_n count

count_if equal exclusive_scan fill

fill_n find find_end find_first_of

find_if find_if_not for_each for_each_n

generate generate_n includes inclusive_scan

inner_product inplace_merge is_heap is_heap_until

is_partitioned is_sorted is_sorted_until lexicographical_compare

max_element merge min_element minmax_element

mismatch move none_of nth_element

partial_sort partial_sort_copy partition partition_copy

reduce remove remove_copy remove_copy_if

remove_if replace replace_copy replace_copy_if

replace_if reverse reverse_copy rotate

rotate_copy search search_n set_difference

set_intersection set_symmetric_difference set_union sort

stable_partition stable_sort swap_ranges transform

transform_exclusive_scan transform_inclusive_scan transform_reduce uninitialized_copy

uninitialized_copy_n uninitialized_fill uninitialized_fill_n unique

unique_copy

[ Note: Not all algorithms in the Standard Library have counterparts in Table 1. — end note ]

4.2

Definitions

[parallel.alg.defns]

Define GENERALIZED_SUM(op, a1, ..., aN) as follows:

a1 when N is 1
op(GENERALIZED_SUM(op, b1, ..., bK), GENERALIZED_SUM(op, bM, ..., bN)) where
- b1, ..., bN may be any permutation of a1, ..., aN and
- 1 < K+1 = M ≤ N.

Define GENERALIZED_NONCOMMUTATIVE_SUM(op, a1, ..., aN) as follows:

a1 when N is 1
op(GENERALIZED_NONCOMMUTATIVE_SUM(op, a1, ..., aK), GENERALIZED_NONCOMMUTATIVE_SUM(op, aM, ..., aN) where 1 < K+1 = M ≤ N.

4.3

Non-Numeric Parallel Algorithms

[parallel.alg.ops]

4.3.1

Header `<experimental/algorithm>` synopsis

[parallel.alg.ops.synopsis]

namespace std {
namespace experimental {
namespace parallel {
inline namespace v1 {
  template<class ExecutionPolicy,
           class InputIterator, class Function>
    void for_each(ExecutionPolicy&& exec,
                  InputIterator first, InputIterator last,
                  Function f);
  template<class InputIterator, class Size, class Function>
    InputIterator for_each_n(InputIterator first, Size n,
                             Function f);
}
}
}
}

4.3.2

For each

[parallel.alg.foreach]


          template<class ExecutionPolicy,
                   class InputIterator, class Function>
            void for_each(ExecutionPolicy&& exec,
                          InputIterator first, InputIterator last,
                          Function f);

Effects:: Applies f to the result of dereferencing every iterator in the range [first,last). [ Note: If the type of first satisfies the requirements of a mutable iterator, f may apply nonconstant functions through the dereferenced iterator. — end note ]
Complexity:: Applies f exactly last - first times.
Remarks:: If f returns a result, the result is ignored.
Notes:: Unlike its sequential form, the parallel overload of for_each does not return a copy of its Function parameter, since parallelization may not permit efficient state accumulation.
Requires:: Unlike its sequential form, the parallel overload of for_each requires Function to meet the requirements of CopyConstructible.


          template<class InputIterator, class Size, class Function>
            InputIterator for_each_n(InputIterator first, Size n,
                                     Function f);

Requires:: Function shall meet the requirements of MoveConstructible [ Note: Function need not meet the requirements of CopyConstructible. — end note ]
Effects:: Applies f to the result of dereferencing every iterator in the range [first,first + n), starting from first and proceeding to first + n - 1. [ Note: If the type of first satisfies the requirements of a mutable iterator, f may apply nonconstant functions through the dereferenced iterator. — end note ]
Returns:: first + n for non-negative values of n and first for negative values.
Remarks:: If f returns a result, the result is ignored.


        template<class ExecutionPolicy,
                 class InputIterator, class Size, class Function>
                 InputIterator for_each_n(ExecutionPolicy && exec,
                                          InputIterator first, Size n,
                                          Function f);

Effects:: Applies f to the result of dereferencing every iterator in the range [first,first + n), starting from first and proceeding to first + n - 1. [ Note: If the type of first satisfies the requirements of a mutable iterator, f may apply nonconstant functions through the dereferenced iterator. — end note ]
Returns:: first + n for non-negative values of n and first for negative values.
Remarks:: If f returns a result, the result is ignored.
Notes:: Unlike its sequential form, the parallel overload of for_each_n requires Function to meet the requirements of CopyConstructible.

4.4

Numeric Parallel Algorithms

[parallel.alg.numeric]

4.4.1

Header `<experimental/numeric>` synopsis

[parallel.alg.numeric.synopsis]

namespace std {
namespace experimental {
namespace parallel {
inline namespace v1 {
  template<class InputIterator>
    typename iterator_traits<InputIterator>::value_type
      reduce(InputIterator first, InputIterator last);
  template<class InputIterator, class T>
    T reduce(InputIterator first, InputIterator last, T init);
  template<class InputIterator, class T, class BinaryOperation>
    T reduce(InputIterator first, InputIterator last, T init,
             BinaryOperation binary_op);

  template<class InputIterator, class OutputIterator,
           class T>
    OutputIterator
      exclusive_scan(InputIterator first, InputIterator last,
                     OutputIterator result,
                     T init);
  template<class InputIterator, class OutputIterator,
           class T, class BinaryOperation>
    OutputIterator
      exclusive_scan(InputIterator first, InputIterator last,
                     OutputIterator result,
                     T init, BinaryOperation binary_op);

  template<class InputIterator, class OutputIterator>
    OutputIterator
      inclusive_scan(InputIterator first, InputIterator last,
                     OutputIterator result);
  template<class InputIterator, class OutputIterator,
           class BinaryOperation>
    OutputIterator
      inclusive_scan(InputIterator first, InputIterator last,
                     OutputIterator result,
                     BinaryOperation binary_op);
  template<class InputIterator, class OutputIterator,
           class BinaryOperation, class T>
    OutputIterator
      inclusive_scan(InputIterator first, InputIterator last,
                     OutputIterator result,
                     BinaryOperation binary_op, T init);

  
  template<class InputIterator, class UnaryOperation,
           class T, class BinaryOperation>
    T transform_reduce(InputIterator first, InputIterator last,
                       UnaryOperation unary_op,
                       T init, BinaryOperation binary_op);

  template<class InputIterator, class OutputIterator,
           class UnaryOperation, class T, class BinaryOperation>
    OutputIterator
      transform_exclusive_scan(InputIterator first, InputIterator last,
                               OutputIterator result,
                               UnaryOperation unary_op,
                               T init, BinaryOperation binary_op);

  template<class InputIterator, class OutputIterator,
           class UnaryOperation, class BinaryOperation>
    OutputIterator
      transform_inclusive_scan(InputIterator first, InputIterator last,
                               OutputIterator result,
                               UnaryOperation unary_op,
                               BinaryOperation binary_op);

  template<class InputIterator, class OutputIterator,
           class UnaryOperation, class BinaryOperation, class T>
    OutputIterator
      transform_inclusive_scan(InputIterator first, InputIterator last,
                               OutputIterator result,
                               UnaryOperation unary_op,
                               BinaryOperation binary_op, T init);
  
}
}
}
}

4.4.2

Reduce

[parallel.alg.reduce]


          template<class InputIterator>
            typename iterator_traits<InputIterator>::value_type
              reduce(InputIterator first, InputIterator last);

Effects:: Same as reduce(first, last, typename iterator_traits<InputIterator>::value_type{}).


          template<class InputIterator, class T>
            T reduce(InputIterator first, InputIterator last, T init);

Effects:: Same as reduce(first, last, init, plus<>()).


          template<class InputIterator, class T, class BinaryOperation>
            T reduce(InputIterator first, InputIterator last, T init,
                     BinaryOperation binary_op);

Returns:: GENERALIZED_SUM(binary_op, init, *first, ..., *(first + (last - first) - 1)).
Requires:: binary_op shall not invalidate iterators or subranges, nor modify elements in the range [first,last).
Complexity:: O(last - first) applications of binary_op.
Notes:: The primary difference between reduce and accumulate is that the behavior of reduce may be non-deterministic for non-associative or non-commutative binary_op.

4.4.3

Exclusive scan

[parallel.alg.exclusive.scan]


          template<class InputIterator, class OutputIterator,
                   class T>
            OutputIterator
              exclusive_scan(InputIterator first, InputIterator last,
                             OutputIterator result,
                             T init);

Effects:: Same as exclusive_scan(first, last, result, init, plus<>()).


          template<class InputIterator, class OutputIterator,
                   class T, class BinaryOperation>
            OutputIterator
              exclusive_scan(InputIterator first, InputIterator last,
                             OutputIterator result,
                             T init, BinaryOperation binary_op);

Effects:: Assigns through each iterator i in [result,result + (last - first)) the value of GENERALIZED_NONCOMMUTATIVE_SUM(binary_op, init, *first, ..., *(first + (i - result) - 1)).
Returns:: The end of the resulting range beginning at result.
Requires:: binary_op shall not invalidate iterators or subranges, nor modify elements in the ranges [first,last) or [result,result + (last - first)).
Complexity:: O(last - first) applications of binary_op.
Notes:: The difference between exclusive_scan and inclusive_scan is that exclusive_scan excludes the ith input element from the ith sum. If binary_op is not mathematically associative, the behavior of exclusive_scan may be non-deterministic.

4.4.4

Inclusive scan

[parallel.alg.inclusive.scan]


          template<class InputIterator, class OutputIterator>
            OutputIterator
              inclusive_scan(InputIterator first, InputIterator last,
                             OutputIterator result);

Effects:: Same as inclusive_scan(first, last, result, plus<>()).


          template<class InputIterator, class OutputIterator,
                   class BinaryOperation>
            OutputIterator
              inclusive_scan(InputIterator first, InputIterator last,
                             OutputIterator result,
                             BinaryOperation binary_op);
          
          template<class InputIterator, class OutputIterator,
                   class BinaryOperation>
            OutputIterator
              inclusive_scan(InputIterator first, InputIterator last,
                             OutputIterator result,
                             BinaryOperation binary_op, T init);

Effects:: Assigns through each iterator i in [result,result + (last - first)) the value of GENERALIZED_NONCOMMUTATIVE_SUM(binary_op, *first, ..., *(first + (i - result))) or GENERALIZED_NONCOMMUTATIVE_SUM(binary_op, init, *first, ..., *(first + (i - result))) if init is provided.
Returns:: The end of the resulting range beginning at result.
Requires:: binary_op shall not invalidate iterators or subranges, nor modify elements in the ranges [first,last) or [result,result + (last - first)).
Complexity:: O(last - first) applications of binary_op.
Notes:: The difference between exclusive_scan and inclusive_scan is that inclusive_scan includes the ith input element in the ith sum. If binary_op is not mathematically associative, the behavior of inclusive_scan may be non-deterministic.

4.4.5

Transform reduce

[parallel.alg.transform.reduce]


        
          template<class InputIterator, class UnaryFunction, class T, class BinaryOperation>
          T transform_reduce(InputIterator first, InputIterator last,
                             UnaryOperation unary_op, T init, BinaryOperation binary_op);

Returns:: GENERALIZED_SUM(binary_op, init, unary_op(*first), ..., unary_op(*(first + (last - first) - 1))).
Requires:: Neither unary_op nor binary_op shall invalidate subranges, or modify elements in the range [first,last)
Complexity:: O(last - first) applications each of unary_op and binary_op.
Notes:: transform_reduce does not apply unary_op to init.

4.4.6

Transform exclusive scan

[parallel.alg.transform.exclusive.scan]


        
          template<class InputIterator, class OutputIterator,
                   class UnaryOperation,
                   class T, class BinaryOperation>
            OutputIterator
              transform_exclusive_scan(InputIterator first, InputIterator last,
                                       OutputIterator result,
                                       UnaryOperation unary_op,
                                       T init, BinaryOperation binary_op);

Effects:: Assigns through each iterator i in [result,result + (last - first)) the value of GENERALIZED_NONCOMMUTATIVE_SUM(binary_op, init, unary_op(*first), ..., unary_op(*(first + (i - result) - 1))).
Returns:: The end of the resulting range beginning at result.
Requires:: Neither unary_op nor binary_op shall invalidate iterators or subranges, or modify elements in the ranges [first,last) or [result,result + (last - first)).
Complexity:: O(last - first) applications each of unary_op and binary_op.
Notes:: The difference between transform_exclusive_scan and transform_inclusive_scan is that transform_exclusive_scan excludes the ith input element from the ith sum. If binary_op is not mathematically associative, the behavior of transform_exclusive_scan may be non-deterministic. transform_exclusive_scan does not apply unary_op to init.

4.4.7

Transform inclusive scan

[parallel.alg.transform.inclusive.scan]


        
          template<class InputIterator, class OutputIterator,
                   class UnaryOperation,
                   class BinaryOperation>
            OutputIterator
              transform_inclusive_scan(InputIterator first, InputIterator last,
                                       OutputIterator result,
                                       UnaryOperation unary_op,
                                       BinaryOperation binary_op);

          template<class InputIterator, class OutputIterator,
                   class UnaryOperation,
                   class BinaryOperation, class T>
            OutputIterator
              transform_inclusive_scan(InputIterator first, InputIterator last,
                                       OutputIterator result,
                                       UnaryOperation unary_op,
                                       BinaryOperation binary_op, T init);

Effects:: Assigns through each iterator i in [result,result + (last - first)) the value of GENERALIZED_NONCOMMUTATIVE_SUM(binary_op, unary_op(*first), ..., unary_op(*(first + (i - result)))) or GENERALIZED_NONCOMMUTATIVE_SUM(binary_op, init, unary_op(*first), ..., unary_op(*(first + (i - result)))) if init is provided.
Returns:: The end of the resulting range beginning at result.
Requires:: Neither unary_op nor binary_op shall invalidate iterators or subranges, or modify elements in the ranges [first,last) or [result,result + (last - first)).
Complexity:: O(last - first) applications each of unary_op and binary_op.
Notes:: The difference between transform_exclusive_scan and transform_inclusive_scan is that transform_inclusive_scan includes the ith input element from the ith sum. If binary_op is not mathematically associative, the behavior of transform_inclusive_scan may be non-deterministic. transform_inclusive_scan does not apply unary_op to init.

Working Draft, Technical Specification for C++ Extensions for Parallelism

General

Scope

Normative references

Namespaces and headers

Terms and definitions

Execution policies

In general

Header `<experimental/execution_policy>` synopsis

Execution policy type trait

Sequential execution policy

Parallel execution policy

Parallel+Vector execution policy

Dynamic execution policy

`execution_policy` construct/assign

`execution_policy` object access

Execution policy objects

Parallel exceptions

Exception reporting behavior

Header `<experimental/exception_list>` synopsis

Parallel algorithms

In general

Requirements on user-provided function objects

Effect of execution policies on algorithm execution

`ExecutionPolicy` algorithm overloads

Definitions

Non-Numeric Parallel Algorithms

Header `<experimental/algorithm>` synopsis

For each

Numeric Parallel Algorithms

Header `<experimental/numeric>` synopsis

Reduce

Exclusive scan

Inclusive scan

Transform reduce

Transform exclusive scan

Transform inclusive scan