nrm1.hpp

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#pragma once
 
namespace monolish {
 
namespace {
template <typename F1> double Dnrm1_core(const F1 &x) {
  Logger &logger = Logger::get_instance();
  logger.func_in(monolish_func);
 
  double ans = 0;
  const double *xd = x.data();
  size_t size = x.size();
  const size_t xoffset = x.get_offset();
 
  if (x.get_device_mem_stat() == true) {
#if MONOLISH_USE_NVIDIA_GPU
#pragma omp target teams distribute parallel for reduction(+ : ans) map (tofrom: ans)
    for (size_t i = 0; i < size; i++) {
      ans += std::abs(xd[i + xoffset]);
    }
#else
    throw std::runtime_error(
        "error USE_GPU is false, but get_device_mem_stat() == true");
#endif
  } else {
#pragma omp parallel for reduction(+ : ans)
    for (size_t i = 0; i < size; i++) {
      ans += std::abs(xd[i + xoffset]);
    }
  }
 
#if MONOLISH_USE_MPI
  mpi::comm &comm = mpi::comm::get_instance();
  ans = comm.Allreduce(ans);
#endif
 
  logger.func_out();
  return ans;
}
 
template <typename F1> float Snrm1_core(const F1 &x) {
  Logger &logger = Logger::get_instance();
  logger.func_in(monolish_func);
 
  float ans = 0;
  const float *xd = x.data();
  size_t size = x.size();
  const size_t xoffset = x.get_offset();
 
  if (x.get_device_mem_stat() == true) {
#if MONOLISH_USE_NVIDIA_GPU
#pragma omp target teams distribute parallel for reduction(+ : ans) map (tofrom: ans)
    for (size_t i = 0; i < size; i++) {
      ans += std::abs(xd[i + xoffset]);
    }
#else
    throw std::runtime_error(
        "error USE_GPU is false, but get_device_mem_stat() == true");
#endif
  } else {
#pragma omp parallel for reduction(+ : ans)
    for (size_t i = 0; i < size; i++) {
      ans += std::abs(xd[i + xoffset]);
    }
  }
 
#if MONOLISH_USE_MPI
  mpi::comm &comm = mpi::comm::get_instance();
  ans = comm.Allreduce(ans);
#endif
 
  logger.func_out();
  return ans;
}
 
} // namespace
} // namespace monolish