cudaFlow Algorithms » Parallel Iterations | Taskflow QuickStart

# cudaFlow Algorithms » Parallel Iterations

tf::cudaFlow provides two template methods, tf::cudaFlow::for_each and tf::cudaFlow::for_each_index, for creating tasks to perform parallel iterations over a range of items.

## Include the Header

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

    #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::cudaFlow::for_each_index(I first, I last, I step, C callable) represents a kernel of parallel execution for the following loop:

    // positive step: first, first+step, first+2*step, ...
    for(auto i=first; i<last; i+=step) {
      callable(i);
    }
    // negative step: first, first-step, first-2*step, ...
    for(auto i=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. Since the callable runs on GPU, it must be declared with a `__device__` specifier. The following example creates a kernel that assigns each entry of `gpu_data` to 1 over the range [0, 100) with step size 1.

    // assigns each element in gpu_data to 1 over the range [0, 100) with step size 1
    cudaflow.for_each_index(0, 100, 1, [gpu_data] __device__ (int idx) {
      gpu_data[idx] = 1;
    });

## 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::cudaFlow::for_each(I first, I last, C callable) represents a parallel execution of the following loop:

    for(auto i=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 `[gpu_data, gpu_data + 1000)`.

    // assigns each element to 1 over the range [gpu_data, gpu_data + 1000)
    cudaflow.for_each(gpu_data, gpu_data + 1000, [] __device__ (int& item) {
      item = 1;
    });

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.

## Miscellaneous Items

The parallel-iteration algorithms are also available in tf::cudaFlowCapturer::for_each and tf::cudaFlowCapturer::for_each_index.

Tab / T to search, Esc to close
…

Search for symbols, directories, files, pages or modules. You can omit any prefix from the symbol or file path; adding a `:` or `/` suffix lists all members of given symbol or directory.

Use ↓ / ↑ to navigate through the list, Enter to go. Tab autocompletes common prefix, you can copy a link to the result using ⌘ L while ⌘ M produces a Markdown link.

Sorry, nothing was found.