namespace tf {

/** @page ParallelReduction Parallel Reduction

%Taskflow provides template function that constructs a task to perform parallel reduction over a range of items.

@tableofcontents

@section ParallelReductionInclude Include the Header

You need to include the header file, <tt>taskflow/algorithm/reduce.hpp</tt>,
for creating a parallel-reduction task.

@code{.cpp}
#include <taskflow/algorithm/reduce.hpp>
@endcode

@section A2ParallelReduction Create a Parallel-Reduction Task

The reduction task created by 
tf::Taskflow::reduce(B first, E last, T& result, O bop, P&& part) performs
parallel reduction over a range of elements specified by <tt>[first, last)</tt> using the binary operator @c bop and stores the reduced result in @c result.
It represents the parallel execution of the following reduction loop:

@code{.cpp}
for(auto itr=first; itr<last; itr++) {
  result = bop(result, *itr);
}
@endcode

At runtime, the reduction task spawns a subflow to perform parallel reduction.
The reduced result is stored in @c result that will be captured by reference in the reduction task.
It is your responsibility to ensure @c result remains alive during the parallel execution.

@code{.cpp}
int sum = 100;
std::vector<int> vec = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

tf::Task task = taskflow.reduce(vec.begin(), vec.end(), sum, 
  [] (int l, int r) { return l + r; }  // binary reducer operator
);
executor.run(taskflow).wait();

assert(sum == 100 + 55);
@endcode

The order in which the binary operator is applied to pairs of elements is @em unspecified.
In other words, the elements of the range may be grouped and rearranged in arbitrary order.
The result and the argument types of the binary operator must be consistent with the input data type.

@section ParallelReductionCaptureIteratorsByReference Capture Iterators by Reference

You can pass iterators by reference using @std_ref
to marshal parameter update between dependent tasks.
This is especially useful when the range is unknown
at the time of creating a parallel-reduction task,
but needs initialization from another task.

@code{.cpp}
int sum = 100;
std::vector<int> vec;
std::vector<int>::iterator first, last;

tf::Task init = taskflow.emplace([&](){
  vec   = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};
  first = vec.begin();
  last  = vec.end();
});

tf::Task task = taskflow.reduce(std::ref(first), std::ref(last), sum, 
  [] (int l, int r) { return l + r; }  // binary reducer operator
);

// wrong! must use std::ref, or first and last are captured by copy
// tf::Task task = taskflow.reduce(first, last, sum, [] (int l, int r) { 
//   return l + r;    // binary reducer operator
// });

init.precede(task);

executor.run(taskflow).wait();

assert(sum == 100 + 55);
@endcode

In the above example, when @c init finishes, @c vec has been initialized to 10 elements with
@c first and @c last pointing to the data range of @c vec.
The reduction task will then work on this initialized range
as a result of passing iterators by reference.

@section A2ParallelTransformationReduction Create a Parallel-Transform-Reduction Task

It is common to transform each element into a new data type and
then perform reduction on the transformed elements.
%Taskflow provides a method, 
tf::Taskflow::transform_reduce(B first, E last, T& result, BOP bop, UOP uop, P&& part), 
that applies @c uop to transform each element in the specified range 
and then perform parallel reduction over @c result and transformed elements.
It represents the parallel execution of the following reduction loop:

@code{.cpp}
for(auto itr=first; itr<last; itr++) {
  result = bop(result, uop(*itr));
}
@endcode

The example below transforms each digit in a string to an integer number
and then sums up all integers in parallel.

@code{.cpp}
std::string str = "12345678";
int sum {0};
tf::Task task = taskflow.transform_reduce(str.begin(), str.end(), sum,
  [] (int a, int b) {      // binary reduction operator
    return a + b;
  },  
  [] (char c) -> int {     // unary transformation operator
    return c - '0';
  }   
); 
executor.run(taskflow).wait(); 
assert(sum == 1 + 2 + 3 + 4 + 5 + 6 + 7 + 8);  // sum will be 36 
@endcode

The order in which we apply the binary operator on the transformed elements is @em unspecified.
It is possible that the binary operator will take @em r-value in both arguments, for example, 
<tt>bop(uop(*itr1), uop(*itr2))</tt>, due to the transformed temporaries.
When data passing is expensive, 
you may define the result type @c T to be move-constructible.

@section ParallelReductionCfigureAPartitioner Configure a Partitioner

You can configure a partitioner for parallel-reduction tasks to run with different
scheduling methods, such as guided partitioning, dynamic partitioning, and static partitioning.
The following example creates two parallel-reduction tasks using two different
partitioners, one with the static partitioning algorithm and 
another one with the guided partitioning algorithm:

@code{.cpp}
tf::StaticPartitioner static_partitioner;
tf::GuidedPartitioner guided_partitioner;

int sum1 = 100, sum2 = 100;
std::vector<int> vec = {1, 2, 3, 4, 5, 6, 7, 8, 9, 10};

// create a parallel-reduction task with static partitioner
taskflow.reduce(vec.begin(), vec.end(), sum1, 
  [] (int l, int r) { return l + r; },
  static_partitioner
);

// create a parallel-reduction task with guided partitioner
taskflow.reduce(vec.begin(), vec.end(), sum2, 
  [] (int l, int r) { return l + r; },
  guided_partitioner
);
@endcode

@note
By default, parallel-reduction tasks use tf::DefaultPartitioner
if no partitioner is specified. 

*/

}