namespace tf {

/** @page ParallelIterations Parallel Iterations

%Taskflow provides template functions for constructing
tasks to perform parallel iterations over ranges of items.

@tableofcontents

@section ParallelIterationsIncludeTheHeader Include the Header

You need to include the header file, `%taskflow/algorithm/for_each.hpp`, 
for using parallel-iteration algorithms.

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

@section A1IndexBasedParallelFor Create an Index-based Parallel-Iteration Task

Index-based parallel-for performs parallel iterations over a range <tt>[first, last)</tt> with the given @c step size.
The task created by tf::Taskflow::for_each_index(B first, E last, S step, C callable, P part) 
represents parallel execution of the following loop:

@code{.cpp}
// positive step
for(auto i=first; i<last; i+=step) {
  callable(i);
}

// negative step
for(auto i=first; i>last; i+=step) {
  callable(i);
}
@endcode

We support only integer-based range. The range can go positive or negative direction. 

@code{.cpp}
taskflow.for_each_index(0, 100,  2, [](int i) { });  // 50 loops with a + step
taskflow.for_each_index(100, 0, -2, [](int i) { });  // 50 loops with a - step
@endcode

Notice that either positive or negative direction is defined in terms of the range,
<tt>[first, last)</tt>, where @c end is excluded.
In the positive case, the 50 items are 0, 2, 4, 6, 8, ..., 96, 98.
In the negative case, the 50 items are 100, 98, 96, 04, ... 4, 2.
An example of the %Taskflow graph for the positive case under 12 workers is depicted below:


<!-- @image html images/parallel_for_1.svg width=100% -->
@dotfile images/parallel_for_1.dot

Instead of explicitly specifying the index range and the callable for each index invocation,
the overload 
tf::Taskflow::for_each_by_index(R range, C callable, P part) provides you with a more flexible way to 
iterate over subranges of indices.
This overload uses tf::IndexRange to partition the range into subranges, 
allowing finer control over how each subrange is processed.
For instance, the code below does the same thing using two different approaches:

@code{.cpp}
std::vector<int> data1(100), data2(100);

// Approach 1: initialize data1 using explicit index range
taskflow.for_each_index(0, 100, 1, [&](int i){ data1[i] = 10; });

// Approach 2: initialize data2 using tf::IndexRange
tf::IndexRange<int> range(0, 100, 1);
taskflow.for_each_by_index(range, [&](tf::IndexRange<int> subrange){
  for(int i=subrange.begin(); i<subrange.end(); i+=subrange.step_size()) {
    data2[i] = 10;
  }
});
@endcode

Both approaches produce the same result, but the second approach offers more flexibility
in terms of how each partitioned subrange is iterated. 
This is particularly useful for applications that benefit from SIMD optimizations or 
other range-based processing strategies.


@section ParallelForEachCaptureIndicesByReference Capture Indices by Reference

You can pass indices by reference using @std_ref
to marshal parameter update between dependent tasks.
This is especially useful when the range indices are unknown
at the time of creating a for-each-index task,
but is initialized from another task.

@code{.cpp}
int* vec;
int first, last;

auto init = taskflow.emplace([&](){
  first = 0;
  last  = 1000;
  vec = new int[1000];  
});

auto pf = taskflow.for_each_index(std::ref(first), std::ref(last), 1, 
  [&] (int i) {
    std::cout << "parallel iteration on index " << vec[i] << '\n';
  }
);

// wrong! must use std::ref, or first and last are captured by copy
// auto pf = taskflow.for_each_index(first, last, 1, [&](int i) {
//   std::cout << "parallel iteration on index " << vec[i] << '\n';
// });

init.precede(pf);
@endcode

When @c init finishes, the parallel-for task @c pf will see @c first as 0 and @c last as 1000 and performs parallel iterations over the 1000 items.


@section A1IteratorBasedParallelFor Create an Iterator-based Parallel-Iteration Task

Iterator-based parallel-for performs parallel iterations over a range specified by two <a href="https://en.cppreference.com/w/cpp/iterator/iterator">STL-styled iterators</a>, @c first and @c last. 
The task created by tf::Taskflow::for_each(B first, E last, C callable, P part) represents 
a parallel execution of the following loop:

@code{.cpp}
for(auto i=first; i<last; i++) {
  callable(*i);
}
@endcode

tf::Taskflow::for_each(B first, E last, C callable, P part) 
simultaneously applies the callable to the object obtained by dereferencing every iterator 
in the range <tt>[first, last)</tt>.
It is user's responsibility for ensuring that the range is valid within the execution of the parallel-for task. Iterators must have the post-increment operator @++ defined. 

@code{.cpp}
std::vector<int> vec = {1, 2, 3, 4, 5};
taskflow.for_each(vec.begin(), vec.end(), [](int i){ 
  std::cout << "parallel for on item " << i << '\n';  
});

std::list<std::string> list = {"hi", "from", "t", "a", "s", "k", "f", "low"};
taskflow.for_each(list.begin(), list.end(), [](const std::string& str){ 
  std::cout << "parallel for on item " << str << '\n';  
});
@endcode

@section ParallelForEachCaptureIteratorsByReference Capture Iterators by Reference

Similar to tf::Taskflow::for_each_index, iterators of tf::Taskflow::for_each 
are templated to allow capturing range parameters by reference, such that one task can
set up the range before another task performs the parallel-for algorithm.
For example:

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

tf::Task init = taskflow.emplace([&](){
  vec.resize(1000);
  first = vec.begin();
  last  = vec.end();
});

tf::Task pf = taskflow.for_each(std::ref(first), std::ref(last), [&](int i) {
  std::cout << "parallel iteration on item " << i << '\n';
});

// wrong! must use std::ref, or first and last are captured by copy
// tf::Task pf = taskflow.for_each(first, last, [&](int i) {
//   std::cout << "parallel iteration on item " << i << '\n';
// });

init.precede(pf);
@endcode

When @c init finishes, the parallel-for task @c pf will see @c first pointing to the beginning of @c vec and @c last pointing to the end of @c vec and performs parallel iterations over the 1000 items. The two tasks form an end-to-end task graph where the parameters of parallel-for are computed on the fly.

@section ParallelIterationsConfigureAPartitioner Configure a Partitioner

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

@code{.cpp}
std::vector<int> vec(1024, 0);

// create two partitioners with a chunk size of 10
tf::StaticPartitioner static_partitioner(10);
tf::GuidedPartitioner guided_partitioner(10);

// create a parallel-iteration task with static partitioner
taskflow.for_each(
  vec.begin(), vec.end(), [&](int i) {
    std::cout << "parallel iteration on item " << i << '\n';
  },
  static_partitioner
);

// create a parallel-iteration task with guided partitioner
taskflow.for_each(
  vec.begin(), vec.end(), [&](int i) {
    std::cout << "parallel iteration on item " << i << '\n';
  },
  guided_partitioner
);
@endcode

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

*/

}






