//
// Copyright 2018 Pixar
//
// Licensed under the Apache License, Version 2.0 (the "Apache License")
// with the following modification; you may not use this file except in
// compliance with the Apache License and the following modification to it:
// Section 6. Trademarks. is deleted and replaced with:
//
// 6. Trademarks. This License does not grant permission to use the trade
//    names, trademarks, service marks, or product names of the Licensor
//    and its affiliates, except as required to comply with Section 4(c) of
//    the License and to reproduce the content of the NOTICE file.
//
// You may obtain a copy of the Apache License at
//
//     http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the Apache License with the above modification is
// distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
// KIND, either express or implied. See the Apache License for the specific
// language governing permissions and limitations under the Apache License.
//
#ifndef PXR_BASE_WORK_REDUCE_H
#define PXR_BASE_WORK_REDUCE_H

/// \file work/reduce.h
#include "pxr/pxr.h"
#include "pxr/base/work/threadLimits.h"
#include "pxr/base/work/api.h"

#include <tbb/blocked_range.h>
#include <tbb/parallel_reduce.h>
#include <tbb/task.h>

PXR_NAMESPACE_OPEN_SCOPE


///////////////////////////////////////////////////////////////////////////////
///     
/// Recursively splits the range [0, \p n) into subranges, which are then
/// reduced by invoking \p loopCallback in parallel. Each invocation of
/// \p loopCallback returns a single value that is the result of joining the
/// elements in the respective subrange. These values are then further joined
/// using the binary operator \p reductionCallback, until only a single value
/// remains. This single value is then the result of joining all elements over
/// the entire range [0, \p n).
/// 
/// The \p loopCallback must be of the form:
/// 
///     V LoopCallback(size_t begin, size_t end, const V &identity);
///     
/// The \p reductionCallback must be of the form:
/// 
///     V ReductionCallback(const V &lhs, const V &rhs);
/// 
/// For example, the following code reduces an array of mesh points into a
/// single bounding box:
/// 
/// ```{.cpp}
/// 
/// // Get the mesh points from which we are going to generate the bounding box.
/// const std::vector<Vector3> &points = GetMeshPoints();
/// 
/// // Generate the bounding box by parallel reducing the points.
/// BoundingBox bbox = WorkParallelReduceN(
///     BoundingBox(),
///     points.size(),
///     [&points](size_t b, size_t e, const BoundingBox &identity){
///         BoundingBox bbox(identity);
///         
///         // Insert each point in this subrange into the local bounding box.
///         for (size_t i = b; i != e; ++i) {
///             bbox.InsertPoint(points[i]);
///         }
///         
///         // Return the local bounding box, which now encapsulates all the
///         // points in this subrange.
///         return bbox;
///     },
///     [](const BoundingBox &lhs, const BoundingBox &rhs){
///         // Join two bounding boxes into a single bounding box. The
///         // algorithm will apply this reduction step recursively until there
///         // is only a single bounding box left.
///         BoundingBox bbox(lhs);
///         bbox.UnionWith(rhs);
///         return bbox;
///     }
/// );
/// 
/// ```
/// 
/// \p grainSize specifies a minimum amount of work to be done per-thread.
/// There is overhead to launching a task and a typical guideline is that
/// you want to have at least 10,000 instructions to count for the overhead of
/// launching that task.
///
template <typename Fn, typename Rn, typename V>
V
WorkParallelReduceN(
    const V &identity,
    size_t n,
    Fn &&loopCallback,
    Rn &&reductionCallback,
    size_t grainSize)
{
    if (n == 0)
        return identity;

    // Don't bother with parallel_reduce, if concurrency is limited to 1.
    if (WorkHasConcurrency()) {

        class Work_Body_TBB
        {
        public:
            Work_Body_TBB(Fn &fn) : _fn(fn) { }

            V operator()(
                const tbb::blocked_range<size_t> &r,
                const V &value) const {
                // Note that we std::forward _fn using Fn in order get the
                // right operator().
                // We maintain the right type in this way:
                //  If Fn is T&, then reference collapsing gives us T& for _fn
                //  If Fn is T, then std::forward correctly gives us T&& for _fn
                return std::forward<Fn>(_fn)(r.begin(), r.end(), value);
            }
        private:
            Fn &_fn;
        };

        // In most cases we do not want to inherit cancellation state from the
        // parent context, so we create an isolated task group context.
        tbb::task_group_context ctx(tbb::task_group_context::isolated);
        return tbb::parallel_reduce(tbb::blocked_range<size_t>(0,n,grainSize),
            identity,
            Work_Body_TBB(loopCallback),
            std::forward<Rn>(reductionCallback),
            tbb::auto_partitioner(),
            ctx);
    }
        
    // If concurrency is limited to 1, execute serially.
    return std::forward<Fn>(loopCallback)(0, n, identity);
}

///////////////////////////////////////////////////////////////////////////////
///
/// \overload
/// 
/// This overload does not accept a grain size parameter and instead attempts
/// to automatically deduce a grain size that is optimal for the current
/// resource utilization and provided workload.
///
template <typename Fn, typename Rn, typename V>
V
WorkParallelReduceN(
    const V &identity,
    size_t n,
    Fn &&loopCallback,
    Rn &&reductionCallback)
{
    return WorkParallelReduceN(identity, n, loopCallback, reductionCallback, 1);
}

PXR_NAMESPACE_CLOSE_SCOPE

#endif // PXR_BASE_WORK_REDUCE_H
