ParallelTransforms Include the Header ParallelTransforms_1ParallelTransformsInclude Create a Unary Parallel-Transform Task ParallelTransforms_1ParallelTransformsOverARange Capture Iterators by Reference ParallelTransforms_1ParallelTransformsCaptureIteratorsByReference Create a Binary Parallel-Transform Task ParallelTransforms_1ParallelBinaryTransformsOverARange Configure a Partitioner ParallelTransforms_1ParallelTransformsCfigureAPartitioner Taskflow provides template functions for constructing tasks to perform parallel transforms over ranges of items. You need to include the header file, taskflow/algorithm/transform.hpp, for creating a parallel-transform task. #include<taskflow/algorithm/transform.hpp> 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(),[](inti){ std::cout<<"transformingitem"<<i<<"to"<<i+1<<'\n'; returni+1; }); 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>::iteratorfirst,last,d_first; tf::Taskinit=taskflow.emplace([&](){ src.resize(1000); tgt.resize(1000); first=src.begin(); last=src.end(); d_first=tgt.begin(); }); tf::Tasktransform=taskflow.transform( std::ref(first),std::ref(last),std::ref(d_first), [&](inti){ std::cout<<"transformingitem"<<i<<"to"<<i+1<<'\n'; returni+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. 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(), [](inti,intj){ returni+j; } ); 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::StaticPartitionerstatic_partitioner; tf::GuidedPartitionerguided_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()); //createaparallel-transformtaskwithstaticexecutionpartitioner taskflow.transform( src1.begin(),src1.end(),src2.begin(),tgt1.begin(), [](inti,intj){ returni+j; }, static_partitioner ); //createaparallel-transformtaskwithguidedexecutionpartitioner taskflow.transform( src1.begin(),src1.end(),src2.begin(),tgt2.begin(), [](inti,intj){ returni+j; }, guided_partitioner ); By default, parallel-transform tasks use tf::DefaultPartitioner if no partitioner is specified.