mesytec-mnode/external/taskflow-3.8.0/doxygen/install/sycl_compile.dox

namespace tf {

/** @page CompileTaskflowWithSYCL Compile Taskflow with SYCL

@tableofcontents

@section InstallSYCLCompiler Install SYCL Compiler

To compile %Taskflow with SYCL code, you need the DPC++ clang compiler,
which can be acquired from
<a href="https://intel.github.io/llvm-docs/GetStartedGuide.html">Getting 
Started with oneAPI DPC++</a>.

@section CompileTaskflowWithSYCLDirectly Compile Source Code Directly

%Taskflow's GPU programming interface for SYCL is tf::syclFlow.
Consider the following `simple.cpp` program that performs the canonical
saxpy (single-precision AX + Y) operation on a GPU:

@code{.cpp}
#include <taskflow/taskflow.hpp>  // core taskflow routines
#include <taskflow/syclflow.hpp>  // core syclflow routines

int main() {
  
  tf::Executor executor;
  tf::Taskflow taskflow("saxpy example");
  
  sycl::queue queue;
  
  auto X = sycl::malloc_shared<float>(N, queue);
  auto Y = sycl::malloc_shared<float>(N, queue);
  
  taskflow.emplace_on([&](tf::syclFlow& sf){
    tf::syclTask fillX = sf.fill(X, 1.0f, N).name("fillX");
    tf::syclTask fillY = sf.fill(Y, 2.0f, N).name("fillY");
    tf::syclTask saxpy = sf.parallel_for(sycl::range<1>(N), 
      [=] (sycl::id<1> id) {
        X[id] = 3.0f * X[id] + Y[id];
      }
    ).name("saxpy");
    saxpy.succeed(fillX, fillY);
  }, queue).name("syclFlow");
  
  executor.run(taskflow).wait();
}
@endcode

Use DPC++ clang to compile the program with the following options:

@li @c -fsycl: enable SYCL compilation mode
@li @c -fsycl-targets=nvptx64-nvidia-cuda-sycldevice: enable CUDA target
@li @c -fsycl-unnamed-lambda: enable unnamed SYCL lambda kernel

@code{.shell-session}
~$ clang++ -fsycl -fsycl-unnamed-lambda \
           -fsycl-targets=nvptx64-nvidia-cuda-sycldevice \  # for CUDA target
           -I path/to/taskflow -pthread -std=c++17 simple.cpp -o simple
~$ ./simple
@endcode


@attention
You need to include @c taskflow/syclflow.hpp in order to use tf::syclFlow.


@section CompileTaskflowWithSYCLSeparately 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 SYCL code into separate object files and link them
to form the final executable.
Consider the following example that defines two tasks
on two different pieces (@c main.cpp and @c syclflow.cpp) of source code:

@code{.cpp}
// main.cpp
#include <taskflow/taskflow.hpp>

tf::Task make_syclflow(tf::Taskflow& taskflow);  // create a syclFlow 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_syclflow(taskflow);

  task1.precede(task2);

  executor.run(taskflow).wait();

  return 0;
}
@endcode

@code{.cpp}
// syclflow.cpp
#include <taskflow/taskflow.hpp>
#include <taskflow/syclflow.hpp>

inline sycl::queue queue;    // create a global sycl queue

tf::Task make_syclflow(tf::Taskflow& taskflow) {
  return taskflow.emplace_on([](tf::syclFlow& cf){
    printf("syclflow.cpp!\n");
    cf.single_task([](){}).name("kernel");
  }, queue).name("gpu task");
}
@endcode

Compile each source to an object using DPC++ clang:

@code{.shell-session}
~$ clang++ -I path/to/taskflow/ -pthread -std=c++17 -c main.cpp -o main.o
~$ clang++ -fsycl -fsycl-unnamed-lambda \
           -fsycl-targets=nvptx64-nvidia-cuda-sycldevice \
           -I path/to/taskflow/ -pthread -std=c++17 -c syclflow.cpp -o syclflow.o

# now we have the two compiled .o objects, main.o and syclflow.o
~$ ls
main.o syclflow.o 
@endcode

Next, link the two object files to the final executable:

@code{.shell-session}
~$ clang++ -fsycl -fsycl-unnamed-lambda \
           -fsycl-targets=nvptx64-nvidia-cuda-sycldevice \  # for CUDA target
           main.o syclflow.o -pthread -std=c++17 -o main

# run the main program 
~$ ./main
main.cpp!
syclflow.cpp!
@endcode

*/


}