SphinxExamples/src/Core/Common/DoDataParallelThreading/Code.cxx

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 *
 *  Copyright NumFOCUS
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
 *
 *         http://www.apache.org/licenses/LICENSE-2.0.txt
 *
 *  Unless required by applicable law or agreed to in writing, software
 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
 
#include "itkDomainThreader.h"
#include "itkThreadedIndexedContainerPartitioner.h"
 
// We have central nervous system cells of different types.
enum CELL_TYPE
{
  NEURON,
  ASTROCYTE,
  OLIGODENDROCYTE
};
 
// Type to hold our list of cells to count.
using CellContainerType = std::vector<CELL_TYPE>;
// Type to hold the count for each CELL_TYPE.
using CellCountType = std::map<CELL_TYPE, unsigned int>;
 
 
// This class performs the multi-threaded cell type counting for the
// CellCounter class, show below.  The CellCounter class is the TAssociate, and
// since this class is declared as a friend, it can access the CellCounter's
// private members to compute the cell type count for the CellCounter.
//
// While the threading class can access its associate's private members, it
// generally should only do so in a read-only manner.  Otherwise, attempting to
// write to the same member from multiple threads will cause race conditions
// and result in erroreous output or crash the program.  For this reason, the
// threading class contains its own data structures that can be written to in
// individual threads.  These data structures are set up in the
// BeforeThreadedExecution method, and the results contained in each data
// structure are collected in AfterThreadedExecution.  In this case, we have
// m_CellCountPerThread whose counts are initialized to zero in
// BeforeThreadedExecution and collected together in AfterThreadedExecution.
//
// All members and methods related to the multi-threaded computation are
// encapsulated in this class.
//
// The class inherits from itk::DomainThreader, which provides common
// functionality and defines the stages of the multi-threaded operation.
//
// The itk::DomainThreader is templated over the type of DomainPartitioner used
// to split up the domain, and type of the associated class.  The domain in
// this case is a range of indices of a std::vector< CELL_TYPE > to process, so
// we use a ThreadedIndexedContainerPartitioner.  Other options for a domains
// defined as an iterator range or an image region are the
// ThreadedIteratorRangePartitioner and the ThreadedImageRegionPartitioner,
// respectively.
 
template <class TAssociate>
class ComputeCellCountThreader : public itk::DomainThreader<itk::ThreadedIndexedContainerPartitioner, TAssociate>
{
public:
  // Standard ITK type alias.
  using Self = ComputeCellCountThreader;
  using Superclass = itk::DomainThreader<itk::ThreadedIndexedContainerPartitioner, TAssociate>;
  using Pointer = itk::SmartPointer<Self>;
  using ConstPointer = itk::SmartPointer<const Self>;
 
  // The domain is an index range.
  using DomainType = typename Superclass::DomainType;
 
  // This creates the ::New() method for instantiating the class.
  itkNewMacro(Self);
 
protected:
  // We need a constructor for the itkNewMacro.
  ComputeCellCountThreader() = default;
 
private:
  void
  BeforeThreadedExecution() override
  {
    // Reset the counts for all cell types to zero.
    this->m_Associate->m_CellCount[NEURON] = 0;
    this->m_Associate->m_CellCount[ASTROCYTE] = 0;
    this->m_Associate->m_CellCount[OLIGODENDROCYTE] = 0;
 
    // Resize our per-thread data structures to the number of threads that we
    // are actually going to use.  At this point the number of threads that
    // will be used have already been calculated and are available.  The number
    // of threads used depends on the number of cores or processors available
    // on the current system.  It will also be truncated if, for example, the
    // number of cells in the CellContainer is smaller than the number of cores
    // available.
    const itk::ThreadIdType numberOfThreads = this->GetNumberOfWorkUnitsUsed();
    this->m_CellCountPerThread.resize(numberOfThreads);
    for (itk::ThreadIdType ii = 0; ii < numberOfThreads; ++ii)
    {
      this->m_CellCountPerThread[ii][NEURON] = 0;
      this->m_CellCountPerThread[ii][ASTROCYTE] = 0;
      this->m_CellCountPerThread[ii][OLIGODENDROCYTE] = 0;
    }
  }
 
  void
  ThreadedExecution(const DomainType & subDomain, const itk::ThreadIdType threadId) override
  {
    // Look only at the range of cells by the set of indices in the subDomain.
    for (itk::IndexValueType ii = subDomain[0]; ii <= subDomain[1]; ++ii)
    {
      switch (this->m_Associate->m_Cells[ii])
      {
        case NEURON:
          // Accumulate in the per thread cell count.
          ++(this->m_CellCountPerThread[threadId][NEURON]);
          break;
        case ASTROCYTE:
          ++(this->m_CellCountPerThread[threadId][ASTROCYTE]);
          break;
        case OLIGODENDROCYTE:
          ++(this->m_CellCountPerThread[threadId][OLIGODENDROCYTE]);
          break;
      }
    }
  }
 
  void
  AfterThreadedExecution() override
  {
    // Accumulate the cell counts per thread in the associate's total cell
    // count.
    const itk::ThreadIdType numberOfThreads = this->GetNumberOfWorkUnitsUsed();
    for (itk::ThreadIdType ii = 0; ii < numberOfThreads; ++ii)
    {
      this->m_Associate->m_CellCount[NEURON] += this->m_CellCountPerThread[ii][NEURON];
 
      this->m_Associate->m_CellCount[ASTROCYTE] += this->m_CellCountPerThread[ii][ASTROCYTE];
 
      this->m_Associate->m_CellCount[OLIGODENDROCYTE] += this->m_CellCountPerThread[ii][OLIGODENDROCYTE];
    }
  }
 
  std::vector<CellCountType> m_CellCountPerThread;
};
 
 
// A class to count the cells.
class CellCounter
{
public:
  using Self = CellCounter;
 
  using ComputeCellCountThreaderType = ComputeCellCountThreader<Self>;
 
  // Constructor.  Create our Threader class instance.
  CellCounter() { this->m_ComputeCellCountThreader = ComputeCellCountThreaderType::New(); }
 
  // Set the cells we want to count.
  void
  SetCells(const CellContainerType & cells)
  {
    this->m_Cells.resize(cells.size());
    for (size_t ii = 0; ii < cells.size(); ++ii)
    {
      this->m_Cells[ii] = cells[ii];
    }
  }
 
  // Count the cells and return the count of each type.
  const CellCountType &
  ComputeCellCount()
  {
    ComputeCellCountThreaderType::DomainType completeDomain;
    completeDomain[0] = 0;
    completeDomain[1] = this->m_Cells.size() - 1;
    this->m_ComputeCellCountThreader->Execute(this, completeDomain);
    return this->m_CellCount;
  }
 
private:
  // Stores the count of each type of cell.
  CellCountType m_CellCount;
  // Stores the cells to count.
  CellContainerType m_Cells;
 
  // The ComputeCellCountThreader gets to access m_CellCount and m_Cells as
  // needed.
  friend class ComputeCellCountThreader<Self>;
  ComputeCellCountThreaderType::Pointer m_ComputeCellCountThreader;
};
 
 
int
main(int, char *[])
{
  // Our cells.
  static const CELL_TYPE cellsArr[] = { NEURON, ASTROCYTE, ASTROCYTE, OLIGODENDROCYTE, ASTROCYTE,
                                        NEURON, NEURON,    ASTROCYTE, ASTROCYTE,       OLIGODENDROCYTE };
 
  CellContainerType cells(cellsArr, cellsArr + sizeof(cellsArr) / sizeof(cellsArr[0]));
 
  // Count them in a multi-threader way.
  CellCounter cellCounter;
  cellCounter.SetCells(cells);
  const CellCountType multiThreadedCellCount = cellCounter.ComputeCellCount();
  std::cout << "Result of the multi-threaded cell count:\n";
  std::cout << "\tNEURON:          " << (*multiThreadedCellCount.find(NEURON)).second << "\n";
  std::cout << "\tASTROCYTE:       " << (*multiThreadedCellCount.find(ASTROCYTE)).second << "\n";
  std::cout << "\tOLIGODENDROCYTE: " << (*multiThreadedCellCount.find(OLIGODENDROCYTE)).second << "\n";
 
 
  // Count them in a single-threaded way.
  CellCountType singleThreadedCellCount;
  singleThreadedCellCount[NEURON] = 0;
  singleThreadedCellCount[ASTROCYTE] = 0;
  singleThreadedCellCount[OLIGODENDROCYTE] = 0;
  for (auto & cell : cells)
  {
    switch (cell)
    {
      case NEURON:
        // Accumulate in the per thread cell count.
        ++(singleThreadedCellCount[NEURON]);
        break;
      case ASTROCYTE:
        ++(singleThreadedCellCount[ASTROCYTE]);
        break;
      case OLIGODENDROCYTE:
        ++(singleThreadedCellCount[OLIGODENDROCYTE]);
        break;
    }
  }
  std::cout << "Result of the single-threaded cell count:\n";
  std::cout << "\tNEURON:          " << (*singleThreadedCellCount.find(NEURON)).second << "\n";
  std::cout << "\tASTROCYTE:       " << (*singleThreadedCellCount.find(ASTROCYTE)).second << "\n";
  std::cout << "\tOLIGODENDROCYTE: " << (*singleThreadedCellCount.find(OLIGODENDROCYTE)).second << "\n";
 
 
  // Did we get what was expected?  It is always good to check a multi-threaded
  // implementation against a single-threaded implementation to ensure that it
  // gets the same results.
  if ((*multiThreadedCellCount.find(NEURON)).second != singleThreadedCellCount[NEURON] ||
      (*multiThreadedCellCount.find(ASTROCYTE)).second != singleThreadedCellCount[ASTROCYTE] ||
      (*multiThreadedCellCount.find(OLIGODENDROCYTE)).second != singleThreadedCellCount[OLIGODENDROCYTE])
  {
    std::cerr << "Error: did not get the same results"
              << "for a single-threaded and multi-threaded calculation." << std::endl;
    return EXIT_FAILURE;
  }
 
  return EXIT_SUCCESS;
}