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/**
 * @file
 * cub::BlockReduceRaking provides raking-based methods of parallel reduction across a CUDA thread
 * block.  Supports non-commutative reduction operators.
 */

#pragma once

#include <cub/config.cuh>

#if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC)
#  pragma GCC system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG)
#  pragma clang system_header
#elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC)
#  pragma system_header
#endif // no system header

#include <cub/block/block_raking_layout.cuh>
#include <cub/thread/thread_reduce.cuh>
#include <cub/util_ptx.cuh>
#include <cub/warp/warp_reduce.cuh>

CUB_NAMESPACE_BEGIN

/**
 * @brief BlockReduceRaking provides raking-based methods of parallel reduction across a CUDA thread
 *        block. Supports non-commutative reduction operators.
 *
 * Supports non-commutative binary reduction operators.  Unlike commutative
 * reduction operators (e.g., addition), the application of a non-commutative
 * reduction operator (e.g, string concatenation) across a sequence of inputs must
 * honor the relative ordering of items and partial reductions when applying the
 * reduction operator.
 *
 * Compared to the implementation of BlockReduceRakingCommutativeOnly (which
 * does not support non-commutative operators), this implementation requires a
 * few extra rounds of inter-thread communication.
 *
 * @tparam T
 *   Data type being reduced
 *
 * @tparam BLOCK_DIM_X
 *   The thread block length in threads along the X dimension
 *
 * @tparam BLOCK_DIM_Y
 *   The thread block length in threads along the Y dimension
 *
 * @tparam BLOCK_DIM_Z
 *   The thread block length in threads along the Z dimension
 *
 * @tparam LEGACY_PTX_ARCH
 *   The PTX compute capability for which to to specialize this collective
 */
template <typename T, int BLOCK_DIM_X, int BLOCK_DIM_Y, int BLOCK_DIM_Z, int LEGACY_PTX_ARCH = 0>
struct BlockReduceRaking
{
    /// Constants
    enum
    {
        /// The thread block size in threads
        BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z,
    };

    /// Layout type for padded thread block raking grid
    typedef BlockRakingLayout<T, BLOCK_THREADS> BlockRakingLayout;

    ///  WarpReduce utility type
    typedef typename WarpReduce<T, BlockRakingLayout::RAKING_THREADS>::InternalWarpReduce WarpReduce;

    /// Constants
    enum
    {
        /// Number of raking threads
        RAKING_THREADS = BlockRakingLayout::RAKING_THREADS,

        /// Number of raking elements per warp synchronous raking thread
        SEGMENT_LENGTH = BlockRakingLayout::SEGMENT_LENGTH,

        /// Cooperative work can be entirely warp synchronous
        WARP_SYNCHRONOUS = (int(RAKING_THREADS) == int(BLOCK_THREADS)),

        /// Whether or not warp-synchronous reduction should be unguarded (i.e., the warp-reduction elements is a power of two
        WARP_SYNCHRONOUS_UNGUARDED = PowerOfTwo<RAKING_THREADS>::VALUE,

        /// Whether or not accesses into smem are unguarded
        RAKING_UNGUARDED = BlockRakingLayout::UNGUARDED,

    };

    /// Shared memory storage layout type
    union _TempStorage
    {
      /// Storage for warp-synchronous reduction
      typename WarpReduce::TempStorage warp_storage;

      /// Padded thread block raking grid
      typename BlockRakingLayout::TempStorage raking_grid;
    };

    /// Alias wrapper allowing storage to be unioned
    struct TempStorage : Uninitialized<_TempStorage> {};


    // Thread fields
    _TempStorage &temp_storage;
    unsigned int linear_tid;


    /// Constructor
    __device__ __forceinline__ BlockReduceRaking(
        TempStorage &temp_storage)
    :
        temp_storage(temp_storage.Alias()),
        linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z))
    {}

    /**
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] partial
     *   <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
     *
     * @param[in] num_valid
     *   Number of valid elements (may be less than BLOCK_THREADS)
     */
    template <bool IS_FULL_TILE, typename ReductionOp, int ITERATION>
    __device__ __forceinline__ T RakingReduction(ReductionOp reduction_op,
                                                 T *raking_segment,
                                                 T partial,
                                                 int num_valid,
                                                 Int2Type<ITERATION> /*iteration*/)
    {
        // Update partial if addend is in range
        if ((IS_FULL_TILE && RAKING_UNGUARDED) || ((linear_tid * SEGMENT_LENGTH) + ITERATION < num_valid))
        {
            T addend = raking_segment[ITERATION];
            partial = reduction_op(partial, addend);
        }
        return RakingReduction<IS_FULL_TILE>(reduction_op, raking_segment, partial, num_valid, Int2Type<ITERATION + 1>());
    }

    /**
     * @param[in] reduction_op
     *   Binary reduction operator
     *
     * @param[in] partial
     *   <b>[<em>lane</em><sub>0</sub> only]</b> Warp-wide aggregate reduction of input items
     *
     * @param[in] num_valid
     *   Number of valid elements (may be less than BLOCK_THREADS)
     */
    template <bool IS_FULL_TILE, typename ReductionOp>
    __device__ __forceinline__ T RakingReduction(ReductionOp /*reduction_op*/,
                                                 T * /*raking_segment*/,
                                                 T partial,
                                                 int /*num_valid*/,
                                                 Int2Type<SEGMENT_LENGTH> /*iteration*/)
    {
        return partial;
    }

    /**
     * @brief Computes a thread block-wide reduction using the specified reduction operator. The
     *        first num_valid threads each contribute one reduction partial. The return value is
     *        only valid for thread<sub>0</sub>.
     *
     * @param[in] partial
     *   Calling thread's input partial reductions
     *
     * @param[in] num_valid
     *   Number of valid elements (may be less than BLOCK_THREADS)
     *
     * @param[in] reduction_op
     *   Binary reduction operator
     */
    template <bool IS_FULL_TILE, typename ReductionOp>
    __device__ __forceinline__ T Reduce(T partial, int num_valid, ReductionOp reduction_op)
    {
        if (WARP_SYNCHRONOUS)
        {
            // Short-circuit directly to warp synchronous reduction (unguarded if active threads is a power-of-two)
            partial = WarpReduce(temp_storage.warp_storage).template Reduce<IS_FULL_TILE>(
                partial,
                num_valid,
                reduction_op);
        }
        else
        {
            // Place partial into shared memory grid.
            *BlockRakingLayout::PlacementPtr(temp_storage.raking_grid, linear_tid) = partial;

            CTA_SYNC();

            // Reduce parallelism to one warp
            if (linear_tid < RAKING_THREADS)
            {
                // Raking reduction in grid
                T *raking_segment = BlockRakingLayout::RakingPtr(temp_storage.raking_grid, linear_tid);
                partial = raking_segment[0];

                partial = RakingReduction<IS_FULL_TILE>(reduction_op, raking_segment, partial, num_valid, Int2Type<1>());

                int valid_raking_threads = (IS_FULL_TILE) ?
                    RAKING_THREADS :
                    (num_valid + SEGMENT_LENGTH - 1) / SEGMENT_LENGTH;

                partial = WarpReduce(temp_storage.warp_storage).template Reduce<IS_FULL_TILE && RAKING_UNGUARDED>(
                    partial,
                    valid_raking_threads,
                    reduction_op);

            }
        }

        return partial;
    }

    /**
     * @brief Computes a thread block-wide reduction using addition (+) as the reduction operator.
     *        The first num_valid threads each contribute one reduction partial. The return value is
     *        only valid for thread<sub>0</sub>.
     *
     * @param[in] partial
     *   Calling thread's input partial reductions
     *
     * @param[in] num_valid
     *   Number of valid elements (may be less than BLOCK_THREADS)
     */
    template <bool IS_FULL_TILE>
    __device__ __forceinline__ T Sum(T partial, int num_valid)
    {
        cub::Sum reduction_op;

        return Reduce<IS_FULL_TILE>(partial, num_valid, reduction_op);
    }
};

CUB_NAMESPACE_END

