/*
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//                        Kokkos v. 2.0
//              Copyright (2014) Sandia Corporation
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#include <Kokkos_Core.hpp>
#include <Kokkos_DualView.hpp>
#include <impl/Kokkos_Timer.hpp>
#include <cstdio>
#include <cstdlib>

// DualView helps you manage data and computations that take place on
// two different memory spaces.  Examples include CUDA device memory
// and (CPU) host memory (currently implemented), or Intel Knights
// Landing MCDRAM and DRAM (not yet implemented).  For example, if you
// have ported only some parts of you application to run in CUDA,
// DualView can help manage moving data between the parts of your
// application that work best with CUDA, and the parts that work
// better on the CPU.
//
// A DualView takes the same template parameters as a View, but
// contains two Views: One that lives in the DualView's memory space,
// and one that lives in that memory space's host mirror space.  If
// both memory spaces are the same, then the two Views just alias one
// another.  This means that you can use DualView all the time, even
// when not running in a memory space like CUDA.  DualView's
// operations to help you manage memory take almost no time in that
// case.  This makes your code even more performance portable.

typedef Kokkos::DualView<double*> view_type;
typedef Kokkos::DualView<int**> idx_type;


template<class ExecutionSpace>
struct localsum {
  // If the functor has a public 'execution_space' typedef, that defines
  // the functor's execution space (where it runs in parallel).  This
  // overrides Kokkos' default execution space.
  typedef ExecutionSpace execution_space;

  typedef typename Kokkos::Impl::if_c<Kokkos::Impl::is_same<ExecutionSpace,Kokkos::DefaultExecutionSpace>::value ,
     idx_type::memory_space, idx_type::host_mirror_space>::type memory_space;

  // Get the view types on the particular device for which the functor
  // is instantiated.
  //
  // "const_data_type" is a typedef in View (and DualView) which is
  // the const version of the first template parameter of the View.
  // For example, the const_data_type version of double** is const
  // double**.
  Kokkos::View<idx_type::const_data_type, idx_type::array_layout, memory_space> idx;
  // "scalar_array_type" is a typedef in ViewTraits (and DualView) which is the
  // array version of the value(s) stored in the View.
  Kokkos::View<view_type::scalar_array_type, view_type::array_layout, memory_space> dest;
  Kokkos::View<view_type::const_data_type, view_type::array_layout,
               memory_space, Kokkos::MemoryRandomAccess> src;

  // Constructor takes DualViews, synchronizes them to the device,
  // then marks them as modified on the device.
  localsum (idx_type dv_idx, view_type dv_dest, view_type dv_src)
  {
    // Extract the view on the correct Device (i.e., the correct
    // memory space) from the DualView.  DualView has a template
    // method, view(), which is templated on the memory space.  If the
    // DualView has a View from that memory space, view() returns the
    // View in that space.
    idx = dv_idx.view<memory_space> ();
    dest = dv_dest.template view<memory_space> ();
    src = dv_src.template view<memory_space> ();

    // Synchronize the DualView to the correct Device.
    //
    // DualView's sync() method is templated on a memory space, and
    // synchronizes the DualView in a one-way fashion to that memory
    // space.  "Synchronizing" means copying, from the other memory
    // space to the Device memory space.  sync() does _nothing_ if the
    // Views on the two memory spaces are in sync.  DualView
    // determines this by the user manually marking one side or the
    // other as modified; see the modify() call below.

    dv_idx.sync<memory_space> ();
    dv_dest.template sync<memory_space> ();
    dv_src.template sync<memory_space> ();

    // Mark dest as modified on Device.
    dv_dest.template modify<memory_space> ();
  }

  KOKKOS_INLINE_FUNCTION
  void operator() (const int i) const {
    double tmp = 0.0;
    for (int j = 0; j < (int) idx.dimension_1(); ++j) {
      const double val = src(idx(i,j));
      tmp += val*val + 0.5*(idx.dimension_0()*val -idx.dimension_1()*val);
    }
    dest(i) += tmp;
  }
};

class ParticleType {
  public:
    double q;
    double m;
    double q_over_m;
    KOKKOS_INLINE_FUNCTION
    ParticleType(double q_ = -1, double m_ = 1):
     q(q_), m(m_), q_over_m(q/m) {}
protected:
};

  typedef Kokkos::DualView<ParticleType[10]> ParticleTypes;
int main (int narg, char* arg[]) {
  Kokkos::initialize (narg, arg);

// If View is non-trivial constructible type then add braces so it is out of scope
// before Kokkos::finalize() call
{
  ParticleTypes test("Test");
  Kokkos::fence();
  test.h_view(0) = ParticleType(-1e4,1);
  Kokkos::fence();

  int size = 1000000;

  // Create DualViews. This will allocate on both the device and its
  // host_mirror_device.
  idx_type idx ("Idx",size,64);
  view_type dest ("Dest",size);
  view_type src ("Src",size);


  srand (134231);

  // Get a reference to the host view of idx directly (equivalent to
  // idx.view<idx_type::host_mirror_space>() )
  idx_type::t_host h_idx = idx.h_view;
  for (int i = 0; i < size; ++i) {
    for (view_type::size_type j = 0; j < h_idx.dimension_1 (); ++j) {
      h_idx(i,j) = (size + i + (rand () % 500 - 250)) % size;
    }
  }

  // Mark idx as modified on the host_mirror_space so that a
  // sync to the device will actually move data.  The sync happens in
  // the functor's constructor.
  idx.modify<idx_type::host_mirror_space> ();

  // Run on the device.  This will cause a sync of idx to the device,
  // since it was marked as modified on the host.
  Kokkos::Timer timer;
  Kokkos::parallel_for(size,localsum<view_type::execution_space>(idx,dest,src));
  Kokkos::fence();
  double sec1_dev = timer.seconds();

  timer.reset();
  Kokkos::parallel_for(size,localsum<view_type::execution_space>(idx,dest,src));
  Kokkos::fence();
  double sec2_dev = timer.seconds();

  // Run on the host's default execution space (could be the same as device).
  // This will cause a sync back to the host of dest.  Note that if the Device is CUDA,
  // the data layout will not be optimal on host, so performance is
  // lower than what it would be for a pure host compilation.
  timer.reset();
  Kokkos::parallel_for(size,localsum<Kokkos::HostSpace::execution_space>(idx,dest,src));
  Kokkos::fence();
  double sec1_host = timer.seconds();

  timer.reset();
  Kokkos::parallel_for(size,localsum<Kokkos::HostSpace::execution_space>(idx,dest,src));
  Kokkos::fence();
  double sec2_host = timer.seconds();

  printf("Device Time with Sync: %f without Sync: %f \n",sec1_dev,sec2_dev);
  printf("Host   Time with Sync: %f without Sync: %f \n",sec1_host,sec2_host);
}

  Kokkos::finalize();
}

