/* SPDX-FileCopyrightText: 2023 Blender Authors
 *
 * SPDX-License-Identifier: GPL-2.0-or-later */

#pragma once

/** \file
 * \ingroup bli
 */

#ifdef WITH_TBB
/* Quiet top level deprecation message, unrelated to API usage here. */
#  if defined(WIN32) && !defined(NOMINMAX)
/* TBB includes Windows.h which will define min/max macros causing issues
 * when we try to use std::min and std::max later on. */
#    define NOMINMAX
#    define TBB_MIN_MAX_CLEANUP
#  endif
#  include <tbb/blocked_range.h>
#  include <tbb/parallel_for.h>
#  include <tbb/parallel_for_each.h>
#  include <tbb/parallel_invoke.h>
#  include <tbb/parallel_reduce.h>
#  include <tbb/task_arena.h>
#  ifdef WIN32
/* We cannot keep this defined, since other parts of the code deal with this on their own, leading
 * to multiple define warnings unless we un-define this, however we can only undefine this if we
 * were the ones that made the definition earlier. */
#    ifdef TBB_MIN_MAX_CLEANUP
#      undef NOMINMAX
#    endif
#  endif
#endif

#include "BLI_function_ref.hh"
#include "BLI_index_range.hh"
#include "BLI_lazy_threading.hh"
#include "BLI_span.hh"
#include "BLI_utildefines.h"

namespace blender {

/**
 * Wrapper type around an integer to differentiate it from other parameters in a function call.
 */
struct GrainSize {
  int64_t value;

  explicit constexpr GrainSize(const int64_t grain_size) : value(grain_size) {}
};

}  // namespace blender

namespace blender::threading {

template<typename Range, typename Function>
inline void parallel_for_each(Range &&range, const Function &function)
{
#ifdef WITH_TBB
  tbb::parallel_for_each(range, function);
#else
  for (auto &value : range) {
    function(value);
  }
#endif
}

namespace detail {
void parallel_for_impl(IndexRange range,
                       int64_t grain_size,
                       FunctionRef<void(IndexRange)> function);
void parallel_for_weighted_impl(IndexRange range,
                                int64_t grain_size,
                                FunctionRef<void(IndexRange)> function,
                                FunctionRef<void(IndexRange, MutableSpan<int64_t>)> task_sizes_fn);
void memory_bandwidth_bound_task_impl(FunctionRef<void()> function);
}  // namespace detail

template<typename Function>
inline void parallel_for(IndexRange range, int64_t grain_size, const Function &function)
{
  if (range.is_empty()) {
    return;
  }
  if (range.size() <= grain_size) {
    function(range);
    return;
  }
  detail::parallel_for_impl(range, grain_size, function);
}

/**
 * Almost like `parallel_for` but allows passing in a function that estimates the amount of work
 * per index. This allows distributing work to threads more evenly.
 *
 * Using this function makes sense when the work load for each index can differ significantly, so
 * that it is impossible to determine a good constant grain size.
 *
 * This function has a bit more overhead than the unweighted #parallel_for. If that is noticeable
 * highly depends on the use-case. So the overhead should be measured when trying to use this
 * function for cases where all tasks may be very small.
 *
 * \param task_size_fn: Gets the task index as input and computes that tasks size.
 * \param grain_size: Determines approximately how large a combined task should be. For example, if
 * the grain size is 100, then 5 tasks of size 20 fit into it.
 */
template<typename Function, typename TaskSizeFn>
inline void parallel_for_weighted(IndexRange range,
                                  int64_t grain_size,
                                  const Function &function,
                                  const TaskSizeFn &task_size_fn)
{
  if (range.is_empty()) {
    return;
  }
  detail::parallel_for_weighted_impl(
      range, grain_size, function, [&](const IndexRange sub_range, MutableSpan<int64_t> r_sizes) {
        for (const int64_t i : sub_range.index_range()) {
          const int64_t task_size = task_size_fn(sub_range[i]);
          BLI_assert(task_size >= 0);
          r_sizes[i] = task_size;
        }
      });
}

/**
 * Move the sub-range boundaries down to the next aligned index. The "global" begin and end
 * remain fixed though.
 */
inline IndexRange align_sub_range(const IndexRange unaligned_range,
                                  const int64_t alignment,
                                  const IndexRange global_range)
{
  const int64_t global_begin = global_range.start();
  const int64_t global_end = global_range.one_after_last();
  const int64_t alignment_mask = ~(alignment - 1);

  const int64_t unaligned_begin = unaligned_range.start();
  const int64_t unaligned_end = unaligned_range.one_after_last();
  const int64_t aligned_begin = std::max(global_begin, unaligned_begin & alignment_mask);
  const int64_t aligned_end = unaligned_end == global_end ?
                                  unaligned_end :
                                  std::max(global_begin, unaligned_end & alignment_mask);
  const IndexRange aligned_range = IndexRange::from_begin_end(aligned_begin, aligned_end);
  return aligned_range;
}

/**
 * Same as #parallel_for but tries to make the sub-range sizes multiples of the given alignment.
 * This can improve performance when the range is processed using vectorized and/or unrolled loops,
 * because the fallback loop that processes remaining values is used less often. A disadvantage of
 * using this instead of #parallel_for is that the size differences between sub-ranges can be
 * larger, which means that work is distributed less evenly.
 */
template<typename Function>
inline void parallel_for_aligned(const IndexRange range,
                                 const int64_t grain_size,
                                 const int64_t alignment,
                                 const Function &function)
{
  parallel_for(range, grain_size, [&](const IndexRange unaligned_range) {
    const IndexRange aligned_range = align_sub_range(unaligned_range, alignment, range);
    function(aligned_range);
  });
}

template<typename Value, typename Function, typename Reduction>
inline Value parallel_reduce(IndexRange range,
                             int64_t grain_size,
                             const Value &identity,
                             const Function &function,
                             const Reduction &reduction)
{
#ifdef WITH_TBB
  if (range.size() >= grain_size) {
    lazy_threading::send_hint();
    return tbb::parallel_reduce(
        tbb::blocked_range<int64_t>(range.first(), range.one_after_last(), grain_size),
        identity,
        [&](const tbb::blocked_range<int64_t> &subrange, const Value &ident) {
          return function(IndexRange(subrange.begin(), subrange.size()), ident);
        },
        reduction);
  }
#else
  UNUSED_VARS(grain_size, reduction);
#endif
  return function(range, identity);
}

template<typename Value, typename Function, typename Reduction>
inline Value parallel_reduce_aligned(const IndexRange range,
                                     const int64_t grain_size,
                                     const int64_t alignment,
                                     const Value &identity,
                                     const Function &function,
                                     const Reduction &reduction)
{
  parallel_reduce(
      range,
      grain_size,
      identity,
      [&](const IndexRange unaligned_range, const Value &ident) {
        const IndexRange aligned_range = align_sub_range(unaligned_range, alignment, range);
        function(aligned_range, ident);
      },
      reduction);
}

/**
 * Execute all of the provided functions. The functions might be executed in parallel or in serial
 * or some combination of both.
 */
template<typename... Functions> inline void parallel_invoke(Functions &&...functions)
{
#ifdef WITH_TBB
  tbb::parallel_invoke(std::forward<Functions>(functions)...);
#else
  (functions(), ...);
#endif
}

/**
 * Same #parallel_invoke, but allows disabling threading dynamically. This is useful because when
 * the individual functions do very little work, there is a lot of overhead from starting parallel
 * tasks.
 */
template<typename... Functions>
inline void parallel_invoke(const bool use_threading, Functions &&...functions)
{
  if (use_threading) {
    lazy_threading::send_hint();
    parallel_invoke(std::forward<Functions>(functions)...);
  }
  else {
    (functions(), ...);
  }
}

/** See #BLI_task_isolate for a description of what isolating a task means. */
template<typename Function> inline void isolate_task(const Function &function)
{
#ifdef WITH_TBB
  lazy_threading::ReceiverIsolation isolation;
  tbb::this_task_arena::isolate(function);
#else
  function();
#endif
}

/**
 * Should surround parallel code that is highly bandwidth intensive, e.g. it just fills a buffer
 * with no or just few additional operations. If the buffers are large, it's beneficial to limit
 * the number of threads doing the work because that just creates more overhead on the hardware
 * level and doesn't provide a notable performance benefit beyond a certain point.
 */
template<typename Function>
inline void memory_bandwidth_bound_task(const int64_t approximate_bytes_touched,
                                        const Function &function)
{
  /* Don't limit threading when all touched memory can stay in the CPU cache, because there a much
   * higher memory bandwidth is available compared to accessing RAM. This value is supposed to be
   * on the order of the L3 cache size. Accessing that value is not quite straight forward and even
   * if it was, it's not clear if using the exact cache size would be beneficial because there is
   * often more stuff going on on the CPU at the same time. */
  if (approximate_bytes_touched <= 8 * 1024 * 1024) {
    function();
    return;
  }
  detail::memory_bandwidth_bound_task_impl(function);
}

}  // namespace blender::threading