NAME

std::experimental::parallel::transform_reduce - std::experimental::parallel::transform_reduce

Synopsis

Defined in header <experimental/numeric>
template<class InputIt, class UnaryOp, class T, class BinaryOp>

T transform_reduce(InputIt first, InputIt last, (1) (parallelism TS)

UnaryOp unary_op, T init, BinaryOp binary_op);
template<class ExecutionPolicy,

class InputIt, class UnaryOp, class T, class BinaryOp>
T transform_reduce(ExecutionPolicy&& policy, (2) (parallelism TS)
InputIt first, InputIt last,

UnaryOp unary_op, T init, BinaryOp binary_op);

Applies unary_op to each element in the range [first; last) and reduces the results
(possibly permuted and aggregated in unspecified manner) along with the initial
value init over binary_op.

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

The behavior is undefined if unary_op or 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
unary_op - unary FunctionObject that will be applied to each element of the input
range. The return type must be acceptable as input to binary_op
binary_op - binary FunctionObject that will be applied in unspecified order to the
results of unary_op, the results of other binary_op and init.

Type requirements

-
InputIt must meet the requirements of LegacyInputIterator.

Return value

Generalized sum of init and unary_op(*first), unary_op(*(first+1)), ...
unary_op(*(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 results of unary_op may be grouped and arranged in arbitrary
order.

Complexity

O(last - first) applications each of unary_op and 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

unary_op is not applied to init

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

transform_reduce can be used to parallelize std::inner_product:

// Run this code

#include <vector>
#include <iterator>
#include <functional>
#include <iostream>
#include <experimental/numeric>
#include <experimental/execution_policy>
#include <boost/iterator/zip_iterator.hpp>
#include <boost/tuple.hpp>

int main()
{
std::vector<double> xvalues(10007, 1.0), yvalues(10007, 1.0);

double result = std::experimental::parallel::transform_reduce(
std::experimental::parallel::par,
boost::iterators::make_zip_iterator(
boost::make_tuple(std::begin(xvalues), std::begin(yvalues))),
boost::iterators::make_zip_iterator(
boost::make_tuple(std::end(xvalues), std::end(yvalues))),
[](auto r) { return boost::get<0>(r) * boost::get<1>(r); }
0.0,
std::plus<>()
);
std::cout << result << '\n';
}

Output:

10007