NAME

std::experimental::parallel::reduce - std::experimental::parallel::reduce

Synopsis

Defined in header <experimental/numeric>
template<class InputIt>

typename std::iterator_traits<InputIt>::value_type reduce( (1) (parallelism TS)

InputIt first, InputIt last);
template<class ExecutionPolicy, class InputIterator>

typename std::iterator_traits<InputIt>::value_type reduce( (2) (parallelism TS)

ExecutionPolicy&& policy, InputIt first, InputIt last);
template<class InputIt, class T> (3) (parallelism TS)
T reduce(InputIt first, InputIt last, T init);
template<class ExecutionPolicy, class InputIt, class T>
T reduce(ExecutionPolicy&& policy, InputIt first, InputIt last, (4) (parallelism TS)
T init);
template<class InputIt, class T, class BinaryOp>
T reduce(InputIt first, InputIt last, T init, BinaryOp (5) (parallelism TS)
binary_op);
template<class ExecutionPolicy, class InputIt, class T, class
BinaryOp>
(6) (parallelism TS)
T reduce(ExecutionPolicy&& policy,

InputIt first, InputIt last, T init, BinaryOp binary_op);

1) same as reduce(first, last, typename std::iterator_traits<InputIt>::value_type{})
3) same as reduce(first, last, init, std::plus<>())
5) Reduces the range [first; last), possibly permuted and aggregated in unspecified
manner, along with the initial value init over binary_op.
2,4,6) Same as (1,3,5), but executed according to policy

The behavior is non-deterministic if binary_op is not associative or not
commutative.

The behavior is undefined if binary_op modifies any element or invalidates any
iterator in [first; last).

Parameters

first, last - the range of elements to apply the algorithm to
init - the initial value of the generalized sum
policy - the execution policy
binary FunctionObject that will be applied in unspecified order to the
binary_op - result of dereferencing the input iterators, the results of other
binary_op and init.

Type requirements

-
InputIt must meet the requirements of LegacyInputIterator.

Return value

Generalized sum of init and *first, *(first+1), ... *(last-1) over binary_op,

where generalized sum GSUM(op, a
1, ..., a
N) is defined as follows:

* if N=1, a
1
* if N > 1, op(GSUM(op, b
1, ..., b
K), GSUM(op, b
M, ..., b
N)) where

* b
1, ..., b
N may be any permutation of a1, ..., aN and
* 1 < K+1 = M ≤ N

in other words, the elements of the range may be grouped and rearranged in arbitrary
order

Complexity

O(last - first) applications of binary_op.

Exceptions

* If execution of a function invoked as part of the algorithm throws an exception,

* if policy is parallel_vector_execution_policy, std::terminate is called
* if policy is sequential_execution_policy or parallel_execution_policy,
the algorithm exits with an exception_list containing all uncaught
exceptions. If there was only one uncaught exception, the algorithm may
rethrow it without wrapping in exception_list. It is unspecified how
much work the algorithm will perform before returning after the first
exception was encountered.
* if policy is some other type, the behavior is implementation-defined

* If the algorithm fails to allocate memory (either for itself or to construct an
exception_list when handling a user exception), std::bad_alloc is thrown.

Notes

If the range is empty, init is returned, unmodified

* If policy is an instance of sequential_execution_policy, all operations are
performed in the calling thread.
* If policy is an instance of parallel_execution_policy, operations may be
performed in unspecified number of threads, indeterminately sequenced with each
other
* If policy is an instance of parallel_vector_execution_policy, execution may be
both parallelized and vectorized: function body boundaries are not respected and
user code may be overlapped and combined in arbitrary manner (in particular,
this implies that a user-provided Callable must not acquire a mutex to access a
shared resource)

Example

reduce is the out-of-order version of std::accumulate:

// Run this code

#include <iostream>
#include <chrono>
#include <vector>
#include <numeric>
#include <experimental/execution_policy>
#include <experimental/numeric>

int main()
{
std::vector<double> v(10'000'007, 0.5);

{
auto t1 = std::chrono::high_resolution_clock::now();
double result = std::accumulate(v.begin(), v.end(), 0.0);
auto t2 = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::milli> ms = t2 - t1;
std::cout << std::fixed << "std::accumulate result " << result
<< " took " << ms.count() << " ms\n";
}

{
auto t1 = std::chrono::high_resolution_clock::now();
double result = std::experimental::parallel::reduce(
std::experimental::parallel::par,
v.begin(), v.end());
auto t2 = std::chrono::high_resolution_clock::now();
std::chrono::duration<double, std::milli> ms = t2 - t1;
std::cout << "parallel::reduce result "
<< result << " took " << ms.count() << " ms\n";
}
}

Possible output:

std::accumulate result 5000003.50000 took 12.7365 ms
parallel::reduce result 5000003.50000 took 5.06423 ms