std::experimental::parallel::transform_reduce (3) - Linux Manuals

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


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


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).


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
* 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.


O(last - first) applications each of unary_op and binary_op.


* 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.


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)


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(
              boost::make_tuple(std::begin(xvalues), std::begin(yvalues))),
              boost::make_tuple(std::end(xvalues), std::end(yvalues))),
          [](auto r) { return boost::get<0>(r) * boost::get<1>(r); }
      std::cout << result << '\n';



See also

                 sums up a range of elements
accumulate (function template)
                 applies a function to a range of elements
transform (function template)

reduce similar to std::accumulate, except out of order
                 (function template)
(parallelism TS)