OrcaSlicer/src/igl/parallel_for.h

// This file is part of libigl, a simple c++ geometry processing library.
// 
// Copyright (C) 2016 Alec Jacobson <alecjacobson@gmail.com>
// 
// This Source Code Form is subject to the terms of the Mozilla Public License 
// v. 2.0. If a copy of the MPL was not distributed with this file, You can 
// obtain one at http://mozilla.org/MPL/2.0/.
#ifndef IGL_PARALLEL_FOR_H
#define IGL_PARALLEL_FOR_H
#include "igl_inline.h"
#include <functional>

//#warning "Defining IGL_PARALLEL_FOR_FORCE_SERIAL"
//#define IGL_PARALLEL_FOR_FORCE_SERIAL

namespace igl
{
  // PARALLEL_FOR Functional implementation of a basic, open-mp style, parallel
  // for loop. If the inner block of a for-loop can be rewritten/encapsulated in
  // a single (anonymous/lambda) function call `func` so that the serial code
  // looks like:
  // 
  //     for(int i = 0;i<loop_size;i++)
  //     {
  //       func(i);
  //     }
  //
  // then `parallel_for(loop_size,func,min_parallel)` will use as many threads as
  // available on the current hardware to parallelize this for loop so long as
  // loop_size<min_parallel, otherwise it will just use a serial for loop.
  //
  // Inputs:
  //   loop_size  number of iterations. I.e. for(int i = 0;i<loop_size;i++) ...
  //   func  function handle taking iteration index as only argument to compute
  //     inner block of for loop I.e. for(int i ...){ func(i); }
  //   min_parallel  min size of loop_size such that parallel (non-serial)
  //     thread pooling should be attempted {0}
  // Returns true iff thread pool was invoked
  template<typename Index, typename FunctionType >
  inline bool parallel_for(
    const Index loop_size, 
    const FunctionType & func,
    const size_t min_parallel=0);
  // PARALLEL_FOR Functional implementation of an open-mp style, parallel for
  // loop with accumulation. For example, serial code separated into n chunks
  // (each to be parallelized with a thread) might look like:
  //     
  //     Eigen::VectorXd S;
  //     const auto & prep_func = [&S](int n){ S = Eigen:VectorXd::Zero(n); };
  //     const auto & func = [&X,&S](int i, int t){ S(t) += X(i); };
  //     const auto & accum_func = [&S,&sum](int t){ sum += S(t); };
  //     prep_func(n);
  //     for(int i = 0;i<loop_size;i++)
  //     {
  //       func(i,i%n);
  //     }
  //     double sum = 0;
  //     for(int t = 0;t<n;t++)
  //     {
  //       accum_func(t);
  //     }
  // 
  // Inputs:
  //   loop_size  number of iterations. I.e. for(int i = 0;i<loop_size;i++) ...
  //   prep_func function handle taking n >= number of threads as only
  //     argument 
  //   func  function handle taking iteration index i and thread id t as only
  //     arguments to compute inner block of for loop I.e. 
  //     for(int i ...){ func(i,t); }
  //   accum_func  function handle taking thread index as only argument, to be
  //     called after all calls of func, e.g., for serial accumulation across
  //     all n (potential) threads, see n in description of prep_func.
  //   min_parallel  min size of loop_size such that parallel (non-serial)
  //     thread pooling should be attempted {0}
  // Returns true iff thread pool was invoked
  template<
    typename Index, 
    typename PrepFunctionType, 
    typename FunctionType, 
    typename AccumFunctionType 
    >
  inline bool parallel_for(
    const Index loop_size, 
    const PrepFunctionType & prep_func,
    const FunctionType & func,
    const AccumFunctionType & accum_func,
    const size_t min_parallel=0);
}

// Implementation

#include <cmath>
#include <cassert>
#include <thread>
#include <vector>
#include <algorithm>

template<typename Index, typename FunctionType >
inline bool igl::parallel_for(
  const Index loop_size, 
  const FunctionType & func,
  const size_t min_parallel)
{
  using namespace std;
  // no op preparation/accumulation
  const auto & no_op = [](const size_t /*n/t*/){};
  // two-parameter wrapper ignoring thread id
  const auto & wrapper = [&func](Index i,size_t /*t*/){ func(i); };
  return parallel_for(loop_size,no_op,wrapper,no_op,min_parallel);
}

template<
  typename Index, 
  typename PreFunctionType,
  typename FunctionType, 
  typename AccumFunctionType>
inline bool igl::parallel_for(
  const Index loop_size, 
  const PreFunctionType & prep_func,
  const FunctionType & func,
  const AccumFunctionType & accum_func,
  const size_t min_parallel)
{
  assert(loop_size>=0);
  if(loop_size==0) return false;
  // Estimate number of threads in the pool
  // http://ideone.com/Z7zldb
  const static size_t sthc = std::thread::hardware_concurrency();
  const size_t nthreads = 
#ifdef IGL_PARALLEL_FOR_FORCE_SERIAL
    0;
#else
    loop_size<min_parallel?0:(sthc==0?8:sthc);
#endif
  if(nthreads==0)
  {
    // serial
    prep_func(1);
    for(Index i = 0;i<loop_size;i++) func(i,0);
    accum_func(0);
    return false;
  }else
  {
    // Size of a slice for the range functions
    Index slice = 
      std::max(
        (Index)std::round((loop_size+1)/static_cast<double>(nthreads)),(Index)1);
 
    // [Helper] Inner loop
    const auto & range = [&func](const Index k1, const Index k2, const size_t t)
    {
      for(Index k = k1; k < k2; k++) func(k,t);
    };
    prep_func(nthreads);
    // Create pool and launch jobs
    std::vector<std::thread> pool;
    pool.reserve(nthreads);
    // Inner range extents
    Index i1 = 0;
    Index i2 = std::min(0 + slice, loop_size);
    {
      size_t t = 0;
      for (; t+1 < nthreads && i1 < loop_size; ++t)
      {
        pool.emplace_back(range, i1, i2, t);
        i1 = i2;
        i2 = std::min(i2 + slice, loop_size);
      }
      if (i1 < loop_size)
      {
        pool.emplace_back(range, i1, loop_size, t);
      }
    }
    // Wait for jobs to finish
    for (std::thread &t : pool) if (t.joinable()) t.join();
    // Accumulate across threads
    for(size_t t = 0;t<nthreads;t++)
    {
      accum_func(t);
    }
    return true;
  }
}
 
//#ifndef IGL_STATIC_LIBRARY
//#include "parallel_for.cpp"
//#endif
#endif