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tools/cub-1.8.0/cub/agent/agent_reduce.cuh
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/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved.
*
* Redistribution and use in source and binary forms, with or without
* modification, are permitted provided that the following conditions are met:
* * Redistributions of source code must retain the above copyright
* notice, this list of conditions and the following disclaimer.
* * Redistributions in binary form must reproduce the above copyright
* notice, this list of conditions and the following disclaimer in the
* documentation and/or other materials provided with the distribution.
* * Neither the name of the NVIDIA CORPORATION nor the
* names of its contributors may be used to endorse or promote products
* derived from this software without specific prior written permission.
*
* THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
* ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
* WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
* DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY
* DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
* (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
* LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
* ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
* (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
* SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*
******************************************************************************/
/**
* \file
* cub::AgentReduce implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduction .
*/
#pragma once
#include <iterator>
#include "../block/block_load.cuh"
#include "../block/block_reduce.cuh"
#include "../grid/grid_mapping.cuh"
#include "../grid/grid_even_share.cuh"
#include "../util_type.cuh"
#include "../iterator/cache_modified_input_iterator.cuh"
#include "../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/******************************************************************************
* Tuning policy types
******************************************************************************/
/**
* Parameterizable tuning policy type for AgentReduce
*/
template <
int _BLOCK_THREADS, ///< Threads per thread block
int _ITEMS_PER_THREAD, ///< Items per thread (per tile of input)
int _VECTOR_LOAD_LENGTH, ///< Number of items per vectorized load
BlockReduceAlgorithm _BLOCK_ALGORITHM, ///< Cooperative block-wide reduction algorithm to use
CacheLoadModifier _LOAD_MODIFIER> ///< Cache load modifier for reading input elements
struct AgentReducePolicy
{
enum
{
BLOCK_THREADS = _BLOCK_THREADS, ///< Threads per thread block
ITEMS_PER_THREAD = _ITEMS_PER_THREAD, ///< Items per thread (per tile of input)
VECTOR_LOAD_LENGTH = _VECTOR_LOAD_LENGTH, ///< Number of items per vectorized load
};
static const BlockReduceAlgorithm BLOCK_ALGORITHM = _BLOCK_ALGORITHM; ///< Cooperative block-wide reduction algorithm to use
static const CacheLoadModifier LOAD_MODIFIER = _LOAD_MODIFIER; ///< Cache load modifier for reading input elements
};
/******************************************************************************
* Thread block abstractions
******************************************************************************/
/**
* \brief AgentReduce implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduction .
*
* Each thread reduces only the values it loads. If \p FIRST_TILE, this
* partial reduction is stored into \p thread_aggregate. Otherwise it is
* accumulated into \p thread_aggregate.
*/
template <
typename AgentReducePolicy, ///< Parameterized AgentReducePolicy tuning policy type
typename InputIteratorT, ///< Random-access iterator type for input
typename OutputIteratorT, ///< Random-access iterator type for output
typename OffsetT, ///< Signed integer type for global offsets
typename ReductionOp> ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
struct AgentReduce
{
//---------------------------------------------------------------------
// Types and constants
//---------------------------------------------------------------------
/// The input value type
typedef typename std::iterator_traits<InputIteratorT>::value_type InputT;
/// The output value type
typedef typename If<(Equals<typename std::iterator_traits<OutputIteratorT>::value_type, void>::VALUE), // OutputT = (if output iterator's value type is void) ?
typename std::iterator_traits<InputIteratorT>::value_type, // ... then the input iterator's value type,
typename std::iterator_traits<OutputIteratorT>::value_type>::Type OutputT; // ... else the output iterator's value type
/// Vector type of InputT for data movement
typedef typename CubVector<InputT, AgentReducePolicy::VECTOR_LOAD_LENGTH>::Type VectorT;
/// Input iterator wrapper type (for applying cache modifier)
typedef typename If<IsPointer<InputIteratorT>::VALUE,
CacheModifiedInputIterator<AgentReducePolicy::LOAD_MODIFIER, InputT, OffsetT>, // Wrap the native input pointer with CacheModifiedInputIterator
InputIteratorT>::Type // Directly use the supplied input iterator type
WrappedInputIteratorT;
/// Constants
enum
{
BLOCK_THREADS = AgentReducePolicy::BLOCK_THREADS,
ITEMS_PER_THREAD = AgentReducePolicy::ITEMS_PER_THREAD,
VECTOR_LOAD_LENGTH = CUB_MIN(ITEMS_PER_THREAD, AgentReducePolicy::VECTOR_LOAD_LENGTH),
TILE_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD,
// Can vectorize according to the policy if the input iterator is a native pointer to a primitive type
ATTEMPT_VECTORIZATION = (VECTOR_LOAD_LENGTH > 1) &&
(ITEMS_PER_THREAD % VECTOR_LOAD_LENGTH == 0) &&
(IsPointer<InputIteratorT>::VALUE) && Traits<InputT>::PRIMITIVE,
};
static const CacheLoadModifier LOAD_MODIFIER = AgentReducePolicy::LOAD_MODIFIER;
static const BlockReduceAlgorithm BLOCK_ALGORITHM = AgentReducePolicy::BLOCK_ALGORITHM;
/// Parameterized BlockReduce primitive
typedef BlockReduce<OutputT, BLOCK_THREADS, AgentReducePolicy::BLOCK_ALGORITHM> BlockReduceT;
/// Shared memory type required by this thread block
struct _TempStorage
{
typename BlockReduceT::TempStorage reduce;
};
/// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
//---------------------------------------------------------------------
// Per-thread fields
//---------------------------------------------------------------------
_TempStorage& temp_storage; ///< Reference to temp_storage
InputIteratorT d_in; ///< Input data to reduce
WrappedInputIteratorT d_wrapped_in; ///< Wrapped input data to reduce
ReductionOp reduction_op; ///< Binary reduction operator
//---------------------------------------------------------------------
// Utility
//---------------------------------------------------------------------
// Whether or not the input is aligned with the vector type (specialized for types we can vectorize)
template <typename Iterator>
static __device__ __forceinline__ bool IsAligned(
Iterator d_in,
Int2Type<true> /*can_vectorize*/)
{
return (size_t(d_in) & (sizeof(VectorT) - 1)) == 0;
}
// Whether or not the input is aligned with the vector type (specialized for types we cannot vectorize)
template <typename Iterator>
static __device__ __forceinline__ bool IsAligned(
Iterator /*d_in*/,
Int2Type<false> /*can_vectorize*/)
{
return false;
}
//---------------------------------------------------------------------
// Constructor
//---------------------------------------------------------------------
/**
* Constructor
*/
__device__ __forceinline__ AgentReduce(
TempStorage& temp_storage, ///< Reference to temp_storage
InputIteratorT d_in, ///< Input data to reduce
ReductionOp reduction_op) ///< Binary reduction operator
:
temp_storage(temp_storage.Alias()),
d_in(d_in),
d_wrapped_in(d_in),
reduction_op(reduction_op)
{}
//---------------------------------------------------------------------
// Tile consumption
//---------------------------------------------------------------------
/**
* Consume a full tile of input (non-vectorized)
*/
template <int IS_FIRST_TILE>
__device__ __forceinline__ void ConsumeTile(
OutputT &thread_aggregate,
OffsetT block_offset, ///< The offset the tile to consume
int /*valid_items*/, ///< The number of valid items in the tile
Int2Type<true> /*is_full_tile*/, ///< Whether or not this is a full tile
Int2Type<false> /*can_vectorize*/) ///< Whether or not we can vectorize loads
{
OutputT items[ITEMS_PER_THREAD];
// Load items in striped fashion
LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_wrapped_in + block_offset, items);
// Reduce items within each thread stripe
thread_aggregate = (IS_FIRST_TILE) ?
internal::ThreadReduce(items, reduction_op) :
internal::ThreadReduce(items, reduction_op, thread_aggregate);
}
/**
* Consume a full tile of input (vectorized)
*/
template <int IS_FIRST_TILE>
__device__ __forceinline__ void ConsumeTile(
OutputT &thread_aggregate,
OffsetT block_offset, ///< The offset the tile to consume
int /*valid_items*/, ///< The number of valid items in the tile
Int2Type<true> /*is_full_tile*/, ///< Whether or not this is a full tile
Int2Type<true> /*can_vectorize*/) ///< Whether or not we can vectorize loads
{
// Alias items as an array of VectorT and load it in striped fashion
enum { WORDS = ITEMS_PER_THREAD / VECTOR_LOAD_LENGTH };
// Fabricate a vectorized input iterator
InputT *d_in_unqualified = const_cast<InputT*>(d_in) + block_offset + (threadIdx.x * VECTOR_LOAD_LENGTH);
CacheModifiedInputIterator<AgentReducePolicy::LOAD_MODIFIER, VectorT, OffsetT> d_vec_in(
reinterpret_cast<VectorT*>(d_in_unqualified));
// Load items as vector items
InputT input_items[ITEMS_PER_THREAD];
VectorT *vec_items = reinterpret_cast<VectorT*>(input_items);
#pragma unroll
for (int i = 0; i < WORDS; ++i)
vec_items[i] = d_vec_in[BLOCK_THREADS * i];
// Convert from input type to output type
OutputT items[ITEMS_PER_THREAD];
#pragma unroll
for (int i = 0; i < ITEMS_PER_THREAD; ++i)
items[i] = input_items[i];
// Reduce items within each thread stripe
thread_aggregate = (IS_FIRST_TILE) ?
internal::ThreadReduce(items, reduction_op) :
internal::ThreadReduce(items, reduction_op, thread_aggregate);
}
/**
* Consume a partial tile of input
*/
template <int IS_FIRST_TILE, int CAN_VECTORIZE>
__device__ __forceinline__ void ConsumeTile(
OutputT &thread_aggregate,
OffsetT block_offset, ///< The offset the tile to consume
int valid_items, ///< The number of valid items in the tile
Int2Type<false> /*is_full_tile*/, ///< Whether or not this is a full tile
Int2Type<CAN_VECTORIZE> /*can_vectorize*/) ///< Whether or not we can vectorize loads
{
// Partial tile
int thread_offset = threadIdx.x;
// Read first item
if ((IS_FIRST_TILE) && (thread_offset < valid_items))
{
thread_aggregate = d_wrapped_in[block_offset + thread_offset];
thread_offset += BLOCK_THREADS;
}
// Continue reading items (block-striped)
while (thread_offset < valid_items)
{
OutputT item = d_wrapped_in[block_offset + thread_offset];
thread_aggregate = reduction_op(thread_aggregate, item);
thread_offset += BLOCK_THREADS;
}
}
//---------------------------------------------------------------
// Consume a contiguous segment of tiles
//---------------------------------------------------------------------
/**
* \brief Reduce a contiguous segment of input tiles
*/
template <int CAN_VECTORIZE>
__device__ __forceinline__ OutputT ConsumeRange(
GridEvenShare<OffsetT> &even_share, ///< GridEvenShare descriptor
Int2Type<CAN_VECTORIZE> can_vectorize) ///< Whether or not we can vectorize loads
{
OutputT thread_aggregate;
if (even_share.block_offset + TILE_ITEMS > even_share.block_end)
{
// First tile isn't full (not all threads have valid items)
int valid_items = even_share.block_end - even_share.block_offset;
ConsumeTile<true>(thread_aggregate, even_share.block_offset, valid_items, Int2Type<false>(), can_vectorize);
return BlockReduceT(temp_storage.reduce).Reduce(thread_aggregate, reduction_op, valid_items);
}
// At least one full block
ConsumeTile<true>(thread_aggregate, even_share.block_offset, TILE_ITEMS, Int2Type<true>(), can_vectorize);
even_share.block_offset += even_share.block_stride;
// Consume subsequent full tiles of input
while (even_share.block_offset + TILE_ITEMS <= even_share.block_end)
{
ConsumeTile<false>(thread_aggregate, even_share.block_offset, TILE_ITEMS, Int2Type<true>(), can_vectorize);
even_share.block_offset += even_share.block_stride;
}
// Consume a partially-full tile
if (even_share.block_offset < even_share.block_end)
{
int valid_items = even_share.block_end - even_share.block_offset;
ConsumeTile<false>(thread_aggregate, even_share.block_offset, valid_items, Int2Type<false>(), can_vectorize);
}
// Compute block-wide reduction (all threads have valid items)
return BlockReduceT(temp_storage.reduce).Reduce(thread_aggregate, reduction_op);
}
/**
* \brief Reduce a contiguous segment of input tiles
*/
__device__ __forceinline__ OutputT ConsumeRange(
OffsetT block_offset, ///< [in] Threadblock begin offset (inclusive)
OffsetT block_end) ///< [in] Threadblock end offset (exclusive)
{
GridEvenShare<OffsetT> even_share;
even_share.template BlockInit<TILE_ITEMS>(block_offset, block_end);
return (IsAligned(d_in + block_offset, Int2Type<ATTEMPT_VECTORIZATION>())) ?
ConsumeRange(even_share, Int2Type<true && ATTEMPT_VECTORIZATION>()) :
ConsumeRange(even_share, Int2Type<false && ATTEMPT_VECTORIZATION>());
}
/**
* Reduce a contiguous segment of input tiles
*/
__device__ __forceinline__ OutputT ConsumeTiles(
GridEvenShare<OffsetT> &even_share) ///< [in] GridEvenShare descriptor
{
// Initialize GRID_MAPPING_STRIP_MINE even-share descriptor for this thread block
even_share.template BlockInit<TILE_ITEMS, GRID_MAPPING_STRIP_MINE>();
return (IsAligned(d_in, Int2Type<ATTEMPT_VECTORIZATION>())) ?
ConsumeRange(even_share, Int2Type<true && ATTEMPT_VECTORIZATION>()) :
ConsumeRange(even_share, Int2Type<false && ATTEMPT_VECTORIZATION>());
}
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
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