GenericScanner.h

// -*- tab-width: 2; indent-tabs-mode: nil; coding: utf-8-with-signature -*-
//-----------------------------------------------------------------------------
// Copyright 2000-2024 CEA (www.cea.fr) IFPEN (www.ifpenergiesnouvelles.com)
// See the top-level COPYRIGHT file for details.
// SPDX-License-Identifier: Apache-2.0
//-----------------------------------------------------------------------------
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/* GenericScanner.h                                            (C) 2000-2024 */
/*                                                                           */
/* Algorithme de 'scan' pour les accélérateurs.                              */
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#ifndef ARCANE_ACCELERATOR_GENERICSCANNER_H
#define ARCANE_ACCELERATOR_GENERICSCANNER_H
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#include "arcane/utils/ArrayView.h"
#include "arcane/utils/FatalErrorException.h"
 
#include "arcane/utils/NumArray.h"
 
#include "arcane/accelerator/core/RunQueue.h"
 
#include "arcane/accelerator/AcceleratorGlobal.h"
#include "arcane/accelerator/CommonUtils.h"
#include "arcane/accelerator/RunCommandLaunchInfo.h"
#include "arcane/accelerator/RunCommandLoop.h"
#include "arcane/accelerator/ScanImpl.h"
#include "arcane/accelerator/MultiThreadAlgo.h"
 
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namespace Arcane::Accelerator::impl
{
 
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/*!
 * \internal
 * \brief Classe pour effectuer un scan exlusif ou inclusif avec un opérateur spécifique.
 */
class ScannerImpl
{
 public:
 
  explicit ScannerImpl(const RunQueue& queue)
  : m_queue(queue)
  {}
 
 public:
 
  template <bool IsExclusive, typename InputIterator, typename OutputIterator,
            typename Operator, typename DataType>
  void apply(Int32 nb_item, InputIterator input_data, OutputIterator output_data,
             DataType init_value, Operator op, const TraceInfo& trace_info)
  {
    RunCommand command = makeCommand(m_queue);
    command << trace_info;
    impl::RunCommandLaunchInfo launch_info(command, nb_item);
    launch_info.beginExecute();
    eExecutionPolicy exec_policy = m_queue.executionPolicy();
    switch (exec_policy) {
#if defined(ARCANE_COMPILING_CUDA)
    case eExecutionPolicy::CUDA: {
      size_t temp_storage_size = 0;
      cudaStream_t stream = impl::CudaUtils::toNativeStream(&m_queue);
      // Premier appel pour connaitre la taille pour l'allocation
      if constexpr (IsExclusive)
        ARCANE_CHECK_CUDA(::cub::DeviceScan::ExclusiveScan(nullptr, temp_storage_size,
                                                           input_data, output_data, op, init_value, nb_item, stream));
      else
        ARCANE_CHECK_CUDA(::cub::DeviceScan::InclusiveScan(nullptr, temp_storage_size,
                                                           input_data, output_data, op, nb_item, stream));
      void* temp_storage = m_storage.allocate(temp_storage_size);
      if constexpr (IsExclusive)
        ARCANE_CHECK_CUDA(::cub::DeviceScan::ExclusiveScan(temp_storage, temp_storage_size,
                                                           input_data, output_data, op, init_value, nb_item, stream));
      else
        ARCANE_CHECK_CUDA(::cub::DeviceScan::InclusiveScan(temp_storage, temp_storage_size,
                                                           input_data, output_data, op, nb_item, stream));
    } break;
#endif
#if defined(ARCANE_COMPILING_HIP)
    case eExecutionPolicy::HIP: {
      size_t temp_storage_size = 0;
      // Premier appel pour connaitre la taille pour l'allocation
      hipStream_t stream = impl::HipUtils::toNativeStream(&m_queue);
      if constexpr (IsExclusive)
        ARCANE_CHECK_HIP(rocprim::exclusive_scan(nullptr, temp_storage_size, input_data, output_data,
                                                 init_value, nb_item, op, stream));
      else
        ARCANE_CHECK_HIP(rocprim::inclusive_scan(nullptr, temp_storage_size, input_data, output_data,
                                                 nb_item, op, stream));
      void* temp_storage = m_storage.allocate(temp_storage_size);
      if constexpr (IsExclusive)
        ARCANE_CHECK_HIP(rocprim::exclusive_scan(temp_storage, temp_storage_size, input_data, output_data,
                                                 init_value, nb_item, op, stream));
      else
        ARCANE_CHECK_HIP(rocprim::inclusive_scan(temp_storage, temp_storage_size, input_data, output_data,
                                                 nb_item, op, stream));
    } break;
#endif
#if defined(ARCANE_COMPILING_SYCL)
    case eExecutionPolicy::SYCL: {
#if defined(ARCANE_USE_SCAN_ONEDPL) && defined(__INTEL_LLVM_COMPILER)
      sycl::queue queue = impl::SyclUtils::toNativeStream(&m_queue);
      auto policy = oneapi::dpl::execution::make_device_policy(queue);
      if constexpr (IsExclusive) {
        oneapi::dpl::exclusive_scan(policy, input_data, input_data + nb_item, output_data, init_value, op);
      }
      else {
        oneapi::dpl::inclusive_scan(policy, input_data, input_data + nb_item, output_data, op);
      }
#else
      NumArray<DataType, MDDim1>
      copy_input_data(nb_item);
      NumArray<DataType, MDDim1> copy_output_data(nb_item);
      SmallSpan<DataType> in_data = copy_input_data.to1DSmallSpan();
      SmallSpan<DataType> out_data = copy_output_data.to1DSmallSpan();
      {
        auto command = makeCommand(m_queue);
        command << RUNCOMMAND_LOOP1(iter, nb_item)
        {
          auto [i] = iter();
          in_data[i] = input_data[i];
        };
      }
      m_queue.barrier();
      SyclScanner<IsExclusive, DataType, Operator> scanner;
      scanner.doScan(m_queue, in_data, out_data, init_value);
      {
        auto command = makeCommand(m_queue);
        command << RUNCOMMAND_LOOP1(iter, nb_item)
        {
          auto [i] = iter();
          output_data[i] = out_data[i];
        };
      }
      m_queue.barrier();
#endif
    } break;
#endif
    case eExecutionPolicy::Thread:
      // Si le nombre de valeurs est 1 on utilise la version séquentielle.
      // TODO: il serait judicieux de faire cela aussi pour des valeurs plus importantes
      // car en général sur les petites boucles le multi-threading est contre productif.
      if (nb_item > 1) {
        MultiThreadAlgo scanner;
        scanner.doScan<IsExclusive, DataType>(launch_info.loopRunInfo(), nb_item, input_data, output_data, init_value, op);
        break;
      }
      [[fallthrough]];
    case eExecutionPolicy::Sequential: {
      DataType sum = init_value;
      for (Int32 i = 0; i < nb_item; ++i) {
        DataType v = *input_data;
        if constexpr (IsExclusive) {
          *output_data = sum;
          sum = op(v, sum);
        }
        else {
          sum = op(v, sum);
          *output_data = sum;
        }
        ++input_data;
        ++output_data;
      }
    } break;
    default:
      ARCANE_FATAL(getBadPolicyMessage(exec_policy));
    }
    launch_info.endExecute();
  }
 
 private:
 
  RunQueue m_queue;
  GenericDeviceStorage m_storage;
};
 
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} // namespace Arcane::Accelerator::impl
 
namespace Arcane::Accelerator
{
 
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/*!
 * \brief Algorithmes de scan exclusif ou inclusif sur accélérateurs.
 *
 * Voir https://en.wikipedia.org/wiki/Prefix_sum.
 *
 * Dans les méthodes suivantes, l'argument \a queue ne doit pas être nul.
 */
template <typename DataType>
class Scanner
{
 public:
 
  //! Somme exclusive
  static void exclusiveSum(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output)
  {
    _applyArray<true>(queue, input, output, ScannerSumOperator<DataType>{});
  }
  //! Minimum exclusif
  static void exclusiveMin(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output)
  {
    _applyArray<true>(queue, input, output, ScannerMinOperator<DataType>{});
  }
  //! Maximum exclusif
  static void exclusiveMax(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output)
  {
    _applyArray<true>(queue, input, output, ScannerMaxOperator<DataType>{});
  }
  //! Somme inclusive
  static void inclusiveSum(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output)
  {
    _applyArray<false>(queue, input, output, ScannerSumOperator<DataType>{});
  }
  //! Minimum inclusif
  static void inclusiveMin(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output)
  {
    _applyArray<false>(queue, input, output, ScannerMinOperator<DataType>{});
  }
  //! Maximum inclusif
  static void inclusiveMax(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output)
  {
    _applyArray<false>(queue, input, output, ScannerMaxOperator<DataType>{});
  }
 
 private:
 
  template <bool IsExclusive, typename Operator>
  static void _applyArray(RunQueue* queue, SmallSpan<const DataType> input, SmallSpan<DataType> output, const Operator& op)
  {
    ARCANE_CHECK_POINTER(queue);
    impl::ScannerImpl scanner(*queue);
    const Int32 nb_item = input.size();
    if (output.size() != nb_item)
      ARCANE_FATAL("Sizes are not equals: input={0} output={1}", nb_item, output.size());
    const DataType* input_data = input.data();
    DataType* output_data = output.data();
    DataType init_value = op.defaultValue();
    scanner.apply<IsExclusive>(nb_item, input_data, output_data, init_value, op, TraceInfo{});
  }
};
 
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/*!
 * \brief Algorithmes de scan exclusif ou inclusif sur accélérateurs.
 *
 * Voir https://en.wikipedia.org/wiki/Prefix_sum.
 *
 * Dans les méthodes de scan, les valeurs entre les entrées et les sorties
 * ne doivent pas se chevaucher.
 */
class GenericScanner
{
 public:
 
  /*!
   * \brief Itérateur sur une lambda pour positionner une valeur via un index.
   */
  template <typename DataType, typename SetterLambda>
  class SetterLambdaIterator
  {
   public:
 
    //! Permet de positionner un élément de l'itérateur de sortie
    class Setter
    {
     public:
 
      ARCCORE_HOST_DEVICE explicit Setter(const SetterLambda& s, Int32 index)
      : m_index(index)
      , m_lambda(s)
      {}
      ARCCORE_HOST_DEVICE void operator=(const DataType& value)
      {
        m_lambda(m_index, value);
      }
 
     public:
 
      Int32 m_index = 0;
      SetterLambda m_lambda;
    };
 
    using value_type = DataType;
    using iterator_category = std::random_access_iterator_tag;
    using reference = Setter;
    using difference_type = ptrdiff_t;
    using pointer = void;
    using ThatClass = SetterLambdaIterator<DataType, SetterLambda>;
 
   public:
 
    ARCCORE_HOST_DEVICE SetterLambdaIterator(const SetterLambda& s)
    : m_lambda(s)
    {}
    ARCCORE_HOST_DEVICE explicit SetterLambdaIterator(const SetterLambda& s, Int32 v)
    : m_index(v)
    , m_lambda(s)
    {}
 
   public:
 
    ARCCORE_HOST_DEVICE ThatClass& operator++()
    {
      ++m_index;
      return (*this);
    }
    ARCCORE_HOST_DEVICE friend ThatClass operator+(const ThatClass& iter, Int32 x)
    {
      return ThatClass(iter.m_lambda, iter.m_index + x);
    }
    ARCCORE_HOST_DEVICE friend ThatClass operator+(Int32 x, const ThatClass& iter)
    {
      return ThatClass(iter.m_lambda, iter.m_index + x);
    }
    ARCCORE_HOST_DEVICE friend bool operator<(const ThatClass& iter1, const ThatClass& iter2)
    {
      return iter1.m_index < iter2.m_index;
    }
    ARCCORE_HOST_DEVICE ThatClass operator-(Int32 x)
    {
      return ThatClass(m_lambda, m_index - x);
    }
    ARCCORE_HOST_DEVICE Int32 operator-(const ThatClass& x) const
    {
      return m_index - x.m_index;
    }
    ARCCORE_HOST_DEVICE reference operator*() const
    {
      return Setter(m_lambda, m_index);
    }
    ARCCORE_HOST_DEVICE reference operator[](Int32 x) const { return Setter(m_lambda, m_index + x); }
    ARCCORE_HOST_DEVICE friend bool operator!=(const ThatClass& a, const ThatClass& b)
    {
      return a.m_index != b.m_index;
    }
 
   private:
 
    Int32 m_index = 0;
    SetterLambda m_lambda;
  };
 
 public:
 
  explicit GenericScanner(const RunQueue& queue)
  : m_queue(queue)
  {}
 
 public:
 
  template <typename DataType, typename GetterLambda, typename SetterLambda, typename Operator>
  void applyWithIndexExclusive(Int32 nb_value, const DataType& initial_value,
                               const GetterLambda& getter_lambda,
                               const SetterLambda& setter_lambda,
                               const Operator& op_lambda,
                               const TraceInfo& trace_info = TraceInfo())
  {
    _applyWithIndex<true>(nb_value, initial_value, getter_lambda, setter_lambda, op_lambda, trace_info);
  }
 
  template <typename DataType, typename GetterLambda, typename SetterLambda, typename Operator>
  void applyWithIndexInclusive(Int32 nb_value, const DataType& initial_value,
                               const GetterLambda& getter_lambda,
                               const SetterLambda& setter_lambda,
                               const Operator& op_lambda,
                               const TraceInfo& trace_info = TraceInfo())
  {
    _applyWithIndex<false>(nb_value, initial_value, getter_lambda, setter_lambda, op_lambda, trace_info);
  }
 
  template <typename InputDataType, typename OutputDataType, typename Operator>
  void applyExclusive(const OutputDataType& initial_value,
                      SmallSpan<const InputDataType> input,
                      SmallSpan<OutputDataType> output,
                      const Operator& op_lambda,
                      const TraceInfo& trace_info = TraceInfo())
  {
    _apply<true>(initial_value, input, output, op_lambda, trace_info);
  }
 
  template <typename InputDataType, typename OutputDataType, typename Operator>
  void applyInclusive(const OutputDataType& initial_value,
                      SmallSpan<const InputDataType> input,
                      SmallSpan<OutputDataType> output,
                      const Operator& op_lambda,
                      const TraceInfo& trace_info = TraceInfo())
  {
    _apply<false>(initial_value, input, output, op_lambda, trace_info);
  }
 
 private:
 
  template <bool IsExclusive, typename DataType, typename GetterLambda, typename SetterLambda, typename Operator>
  void _applyWithIndex(Int32 nb_value, const DataType& initial_value,
                       const GetterLambda& getter_lambda,
                       const SetterLambda& setter_lambda,
                       const Operator& op_lambda,
                       const TraceInfo& trace_info)
  {
    impl::GetterLambdaIterator<DataType, GetterLambda> input_iter(getter_lambda);
    SetterLambdaIterator<DataType, SetterLambda> output_iter(setter_lambda);
    impl::ScannerImpl scanner(m_queue);
    scanner.apply<IsExclusive>(nb_value, input_iter, output_iter, initial_value, op_lambda, trace_info);
  }
 
  template <bool IsExclusive, typename InputDataType, typename OutputDataType, typename Operator>
  void _apply(const OutputDataType& initial_value,
              SmallSpan<const InputDataType> input,
              SmallSpan<OutputDataType> output,
              const Operator& op,
              const TraceInfo& trace_info = TraceInfo())
  {
    const Int32 nb_item = input.size();
    if (output.size() != nb_item)
      ARCANE_FATAL("Sizes are not equals: input={0} output={1}", nb_item, output.size());
    auto* input_data = input.data();
    auto* output_data = output.data();
    impl::ScannerImpl scanner(m_queue);
    scanner.apply<IsExclusive>(nb_item, input_data, output_data, initial_value, op, trace_info);
  }
 
 private:
 
  RunQueue m_queue;
};
 
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} // namespace Arcane::Accelerator
 
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#endif
 
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