Taskflow Algorithms » Parallel Transforms

Include the Header

You need to include the header file, taskflow/algorithm/transform.hpp, for creating a parallel-transform task.

#include <taskflow/algorithm/transform.hpp>

Create a Unary Parallel-Transform Task

Parallel-transform transforms a range of items, possibly with a different type for the transformed data, and stores the result in another range. The task created by tf::Taskflow::transform(B first1, E last1, O d_first, C c, P&& part) is equivalent to a parallel execution of the following loop:

while (first1 != last1) {
  *d_first++ = c(*first1++);
}

tf::Taskflow::transform simultaneously applies the callable c to the object obtained by dereferencing every iterator in the range [first1, last1) and stores the result in another range beginning at d_first. It is user's responsibility for ensuring the range is valid within the execution of the parallel-transform task.

std::vector<int> src = {1, 2, 3, 4, 5};
std::vector<int> tgt(src.size());
taskflow.transform(src.begin(), src.end(), tgt.begin(), [](int i){ 
  std::cout << "transforming item " << i << " to " << i + 1 << '\n';
  return i + 1;
});

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-transform task, but needs initialization from another task.

std::vector<int> src, tgt;
std::vector<int>::iterator first, last, d_first;

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

tf::Task transform = taskflow.transform(
  std::ref(first), std::ref(last), std::ref(d_first), 
  [&](int i) {
    std::cout << "transforming item " << i << " to " << i + 1 << '\n';
    return i+1;
  }
);

init.precede(transform);

When init finishes, the parallel-transform task transform will see first pointing to the beginning of src and last pointing to the end of src. Then, it simultaneously transforms these 1000 items by adding one to each element and stores the result in another range starting at d_first.

Create a Binary Parallel-Transform Task

You can use the overload, tf::Taskflow::transform(B1 first1, E1 last1, B2 first2, O d_first, C c, P&& part), to perform parallel transforms on two source ranges pointed by first1 and first2 using the binary operator c and store the result in another range pointed by d_first. This method is equivalent to the parallel execution of the following loop:

while (first1 != last1) {
  *d_first++ = c(*first1++, *first2++);
}

The following example creates a parallel-transform task that adds two ranges of elements one by one and stores the result in a target range:

std::vector<int> src1 = {1, 2, 3, 4, 5};
std::vector<int> src2 = {5, 4, 3, 2, 1};
std::vector<int> tgt(src1.size());
taskflow.transform(
  src1.begin(), src1.end(), src2.begin(), tgt.begin(), 
  [](int i, int j){ 
    return i + j;
  }
);

Configure a Partitioner

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

tf::StaticPartitioner static_partitioner;
tf::GuidedPartitioner guided_partitioner;

std::vector<int> src1 = {1, 2, 3, 4, 5};
std::vector<int> src2 = {5, 4, 3, 2, 1};
std::vector<int> tgt1(src1.size());
std::vector<int> tgt2(src2.size());

// create a parallel-transform task with static execution partitioner
taskflow.transform(
  src1.begin(), src1.end(), src2.begin(), tgt1.begin(), 
  [](int i, int j){ 
    return i + j;
  },
  static_partitioner
);

// create a parallel-transform task with guided execution partitioner
taskflow.transform(
  src1.begin(), src1.end(), src2.begin(), tgt2.begin(), 
  [](int i, int j){ 
    return i + j;
  },
  guided_partitioner
);