Building and Installing » Compile Taskflow with CUDA

Install CUDA Compiler

To compile Taskflow with CUDA code, you need a nvcc compiler. Please visit the official page of Downloading CUDA Toolkit.

Compile Source Code Directly

Taskflow's GPU programming interface for CUDA is tf::cudaFlow. Consider the following simple.cu program that launches a single kernel function to output a message:

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

int main(int argc, const char** argv) {

  tf::Executor executor;
  tf::Taskflow taskflow;

  tf::Task task1 = taskflow.emplace([](){}).name("cpu task");
  tf::Task task2 = taskflow.emplace([](){
    // create a cudaFlow of a single-threaded task
    tf::cudaFlow cf;
    cf.single_task([] __device__ () { printf("hello cudaFlow!\n"); });
    
    // launch the cudaflow through a stream
    tf::cudaStream stream;
    cf.run(stream);
    stream.synchronize();
  }).name("gpu task");

  task1.precede(task2);

  executor.run(taskflow).wait();
  return 0;
}

The easiest way to compile Taskflow with CUDA code (e.g., cudaFlow, kernels) is to use nvcc:

~$ nvcc -std=c++17 -I path/to/taskflow/ --extended-lambda simple.cu -o simple
~$ ./simple
hello cudaFlow!

Compile Source Code Separately

Large GPU applications often compile a program into separate objects and link them together to form an executable or a library. You can compile your CPU code and GPU code separately with Taskflow using nvcc and other compilers (such as g++ and clang++). Consider the following example that defines two tasks on two different pieces (main.cpp and cudaflow.cpp) of source code:

// main.cpp
#include <taskflow/taskflow.hpp>

tf::Task make_cudaflow(tf::Taskflow& taskflow);  // create a cudaFlow task

int main() {

  tf::Executor executor;
  tf::Taskflow taskflow;

  tf::Task task1 = taskflow.emplace([](){ std::cout << "main.cpp!\n"; })
                           .name("cpu task");
  tf::Task task2 = make_cudaflow(taskflow);

  task1.precede(task2);

  executor.run(taskflow).wait();

  return 0;
}

// cudaflow.cpp
#include <taskflow/taskflow.hpp>
#include <taskflow/cudaflow.hpp>

tf::Task make_cudaflow(tf::Taskflow& taskflow) {
  return taskflow.emplace([](){
    // create a cudaFlow of a single-threaded task
    tf::cudaFlow cf;
    cf.single_task([] __device__ () { printf("cudaflow.cpp!\n"); });
    
    // launch the cudaflow through a stream
    tf::cudaStream stream;
    cf.run(stream);
    stream.synchronize();
  }).name("gpu task");
}

Compile each source to an object (g++ as an example):

~$ g++ -std=c++17 -I path/to/taskflow -c main.cpp -o main.o
~$ nvcc -std=c++17 --extended-lambda -x cu -I path/to/taskflow \
        -dc cudaflow.cpp -o cudaflow.o
~$ ls
# now we have the two compiled .o objects, main.o and cudaflow.o
main.o cudaflow.o 

The –extended-lambda option tells nvcc to generate GPU code for the lambda defined with device. The -x cu tells nvcc to treat the input files as .cu files containing both CPU and GPU code. By default, nvcc treats .cpp files as CPU-only code. This option is required to have nvcc generate device code here, but it is also a handy way to avoid renaming source files in larger projects. The –dc option tells nvcc to generate device code for later linking.

You may also need to specify the target architecture to tell nvcc to target on a compatible SM architecture using the option -arch. For instance, the following command requires device code linking to have compute capability 7.5 or later:

~$ nvcc -std=c++17 --extended-lambda -x cu -arch=sm_75 -I path/to/taskflow \
        -dc cudaflow.cpp -o cudaflow.o

~$ nvcc main.o cudaflow.o -o main

# run the main program 
~$ ./main
main.cpp!
cudaflow.cpp!

~$ nvcc -o gpuCode.o -dlink main.o cudaflow.o

# replace /usr/local/cuda/lib64 with your own CUDA library installation path
~$ g++ -pthread -L /usr/local/cuda/lib64/ -lcudart \
   gpuCode.o main.o cudaflow.o -o main

# run the main program
~$ ./main
main.cpp!
cudaflow.cpp!