CUDASTDForEach

# Parallel Iterations

Include the Header

Include the Header

CUDASTDForEach_1CUDASTDParallelIterationIncludeTheHeader
Index-based Parallel Iterations

Index-based Parallel Iterations

CUDASTDForEach_1CUDASTDIndexBasedParallelFor
Iterator-based Parallel Iterations

Iterator-based Parallel Iterations

CUDASTDForEach_1CUDASTDIteratorBasedParallelFor

Taskflow provides standard template methods for performing parallel iterations over a range of items a CUDA GPU.

# Include the Header

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

    #include<taskflow/cuda/algorithm/for_each.hpp>

# Index-based Parallel Iterations

Index-based parallel-for performs parallel iterations over a range `[first, last)` with the given `step` size. The task created by tf::cuda_for_each_index represents a kernel of parallel execution for the following loop:

    //positivestep:first,first+step,first+2*step,...
    for(autoi=first;i<last;i+=step){
    callable(i);
    }
    //negativestep:first,first-step,first-2*step,...
    for(autoi=first;i>last;i+=step){
    callable(i);
    }

Each iteration `i` is independent of each other and is assigned one kernel thread to run the callable. The following example creates a kernel that assigns each entry of `data` to 1 over the range ``[0, 100) with step size 1.

    tf::cudaDefaultExecutionPolicypolicy;
    autodata=tf::cuda_malloc_shared<int>(100);

    //assignseachelementindatato1overtherange[0,100)withstepsize1
    tf::cuda_for_each_index(
    policy,0,100,1,[data]__device__(intidx){data[idx]=1;}
    );

    //synchronizetheexecution
    policy.synchronize();

The parallel-iteration algorithm runs asynchronously through the stream specified in the execution policy. You need to synchronize the stream to obtain correct results.

# Iterator-based Parallel Iterations

Iterator-based parallel-for performs parallel iterations over a range specified by two STL-styled iterators, `first` and `last`. The task created by tf::cuda_for_each represents a parallel execution of the following loop:

    for(autoi=first;i<last;i++){
    callable(*i);
    }

The two iterators, `first` and `last`, are typically two raw pointers to the first element and the next to the last element in the range in GPU memory space. The following example creates a `for_each` kernel that assigns each element in `gpu_data` to 1 over the range `[data, data + 1000)`.

    tf::cudaDefaultExecutionPolicypolicy;
    autodata=tf::cuda_malloc_shared<int>(1000);

    //assignseachelementindatato1overtherange[0,1000)withstepsize1
    tf::cuda_for_each(
    policy,data,data+1000,[]__device__(int&item){item=1;}
    );

    //synchronizetheexecution
    policy.synchronize();

Each iteration is independent of each other and is assigned one kernel thread to run the callable. Since the callable runs on GPU, it must be declared with a `__device__` specifier.