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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::AgentSegmentFixup implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key.
*/
#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/agent/single_pass_scan_operators.cuh>
#include <cub/block/block_discontinuity.cuh>
#include <cub/block/block_load.cuh>
#include <cub/block/block_scan.cuh>
#include <cub/block/block_store.cuh>
#include <cub/iterator/cache_modified_input_iterator.cuh>
#include <cub/iterator/constant_input_iterator.cuh>
#include <iterator>
CUB_NAMESPACE_BEGIN
/******************************************************************************
* Tuning policy types
******************************************************************************/
/**
* @brief Parameterizable tuning policy type for AgentSegmentFixup
*
* @tparam _BLOCK_THREADS
* Threads per thread block
*
* @tparam _ITEMS_PER_THREAD
* Items per thread (per tile of input)
*
* @tparam _LOAD_ALGORITHM
* The BlockLoad algorithm to use
*
* @tparam _LOAD_MODIFIER
* Cache load modifier for reading input elements
*
* @tparam _SCAN_ALGORITHM
* The BlockScan algorithm to use
*/
template <int _BLOCK_THREADS,
int _ITEMS_PER_THREAD,
BlockLoadAlgorithm _LOAD_ALGORITHM,
CacheLoadModifier _LOAD_MODIFIER,
BlockScanAlgorithm _SCAN_ALGORITHM>
struct AgentSegmentFixupPolicy
{
enum
{
/// Threads per thread block
BLOCK_THREADS = _BLOCK_THREADS,
/// Items per thread (per tile of input)
ITEMS_PER_THREAD = _ITEMS_PER_THREAD,
};
/// The BlockLoad algorithm to use
static constexpr BlockLoadAlgorithm LOAD_ALGORITHM = _LOAD_ALGORITHM;
/// Cache load modifier for reading input elements
static constexpr CacheLoadModifier LOAD_MODIFIER = _LOAD_MODIFIER;
/// The BlockScan algorithm to use
static constexpr BlockScanAlgorithm SCAN_ALGORITHM = _SCAN_ALGORITHM;
};
/******************************************************************************
* Thread block abstractions
******************************************************************************/
/**
* @brief AgentSegmentFixup implements a stateful abstraction of CUDA thread blocks for
* participating in device-wide reduce-value-by-key
*
* @tparam AgentSegmentFixupPolicyT
* Parameterized AgentSegmentFixupPolicy tuning policy type
*
* @tparam PairsInputIteratorT
* Random-access input iterator type for keys
*
* @tparam AggregatesOutputIteratorT
* Random-access output iterator type for values
*
* @tparam EqualityOpT
* KeyT equality operator type
*
* @tparam ReductionOpT
* ValueT reduction operator type
*
* @tparam OffsetT
* Signed integer type for global offsets
*/
template <typename AgentSegmentFixupPolicyT,
typename PairsInputIteratorT,
typename AggregatesOutputIteratorT,
typename EqualityOpT,
typename ReductionOpT,
typename OffsetT>
struct AgentSegmentFixup
{
//---------------------------------------------------------------------
// Types and constants
//---------------------------------------------------------------------
// Data type of key-value input iterator
using KeyValuePairT = cub::detail::value_t<PairsInputIteratorT>;
// Value type
using ValueT = typename KeyValuePairT::Value;
// Tile status descriptor interface type
using ScanTileStateT = ReduceByKeyScanTileState<ValueT, OffsetT>;
// Constants
enum
{
BLOCK_THREADS = AgentSegmentFixupPolicyT::BLOCK_THREADS,
ITEMS_PER_THREAD = AgentSegmentFixupPolicyT::ITEMS_PER_THREAD,
TILE_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD,
// Whether or not do fixup using RLE + global atomics
USE_ATOMIC_FIXUP = (std::is_same<ValueT, float>::value ||
std::is_same<ValueT, int>::value ||
std::is_same<ValueT, unsigned int>::value ||
std::is_same<ValueT, unsigned long long>::value),
// Whether or not the scan operation has a zero-valued identity value
// (true if we're performing addition on a primitive type)
HAS_IDENTITY_ZERO = (std::is_same<ReductionOpT, cub::Sum>::value) &&
(Traits<ValueT>::PRIMITIVE),
};
// Cache-modified Input iterator wrapper type (for applying cache modifier) for keys
// Wrap the native input pointer with CacheModifiedValuesInputIterator
// or directly use the supplied input iterator type
using WrappedPairsInputIteratorT = cub::detail::conditional_t<
std::is_pointer<PairsInputIteratorT>::value,
CacheModifiedInputIterator<AgentSegmentFixupPolicyT::LOAD_MODIFIER,
KeyValuePairT,
OffsetT>,
PairsInputIteratorT>;
// Cache-modified Input iterator wrapper type (for applying cache modifier) for fixup values
// Wrap the native input pointer with CacheModifiedValuesInputIterator
// or directly use the supplied input iterator type
using WrappedFixupInputIteratorT = cub::detail::conditional_t<
std::is_pointer<AggregatesOutputIteratorT>::value,
CacheModifiedInputIterator<AgentSegmentFixupPolicyT::LOAD_MODIFIER,
ValueT,
OffsetT>,
AggregatesOutputIteratorT>;
// Reduce-value-by-segment scan operator
using ReduceBySegmentOpT = ReduceByKeyOp<cub::Sum>;
// Parameterized BlockLoad type for pairs
using BlockLoadPairs = BlockLoad<KeyValuePairT,
BLOCK_THREADS,
ITEMS_PER_THREAD,
AgentSegmentFixupPolicyT::LOAD_ALGORITHM>;
// Parameterized BlockScan type
using BlockScanT = BlockScan<KeyValuePairT,
BLOCK_THREADS,
AgentSegmentFixupPolicyT::SCAN_ALGORITHM>;
// Callback type for obtaining tile prefix during block scan
using TilePrefixCallbackOpT =
TilePrefixCallbackOp<KeyValuePairT, ReduceBySegmentOpT, ScanTileStateT>;
// Shared memory type for this thread block
union _TempStorage
{
struct ScanStorage
{
// Smem needed for tile scanning
typename BlockScanT::TempStorage scan;
// Smem needed for cooperative prefix callback
typename TilePrefixCallbackOpT::TempStorage prefix;
} scan_storage;
// Smem needed for loading keys
typename BlockLoadPairs::TempStorage load_pairs;
};
// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
//---------------------------------------------------------------------
// Per-thread fields
//---------------------------------------------------------------------
_TempStorage &temp_storage; ///< Reference to temp_storage
WrappedPairsInputIteratorT d_pairs_in; ///< Input keys
AggregatesOutputIteratorT d_aggregates_out; ///< Output value aggregates
WrappedFixupInputIteratorT d_fixup_in; ///< Fixup input values
InequalityWrapper<EqualityOpT> inequality_op; ///< KeyT inequality operator
ReductionOpT reduction_op; ///< Reduction operator
ReduceBySegmentOpT scan_op; ///< Reduce-by-segment scan operator
//---------------------------------------------------------------------
// Constructor
//---------------------------------------------------------------------
/**
* @param temp_storage
* Reference to temp_storage
*
* @param d_pairs_in
* Input keys
*
* @param d_aggregates_out
* Output value aggregates
*
* @param equality_op
* KeyT equality operator
*
* @param reduction_op
* ValueT reduction operator
*/
__device__ __forceinline__ AgentSegmentFixup(TempStorage &temp_storage,
PairsInputIteratorT d_pairs_in,
AggregatesOutputIteratorT d_aggregates_out,
EqualityOpT equality_op,
ReductionOpT reduction_op)
: temp_storage(temp_storage.Alias())
, d_pairs_in(d_pairs_in)
, d_aggregates_out(d_aggregates_out)
, d_fixup_in(d_aggregates_out)
, inequality_op(equality_op)
, reduction_op(reduction_op)
, scan_op(reduction_op)
{}
//---------------------------------------------------------------------
// Cooperatively scan a device-wide sequence of tiles with other CTAs
//---------------------------------------------------------------------
/**
* @brief Process input tile. Specialized for atomic-fixup
*
* @param num_remaining
* Number of global input items remaining (including this tile)
*
* @param tile_idx
* Tile index
*
* @param tile_offset
* Tile offset
*
* @param tile_state
* Global tile state descriptor
*
* @param use_atomic_fixup
* Marker whether to use atomicAdd (instead of reduce-by-key)
*/
template <bool IS_LAST_TILE>
__device__ __forceinline__ void ConsumeTile(OffsetT num_remaining,
int tile_idx,
OffsetT tile_offset,
ScanTileStateT &tile_state,
Int2Type<true> use_atomic_fixup)
{
KeyValuePairT pairs[ITEMS_PER_THREAD];
// Load pairs
KeyValuePairT oob_pair;
oob_pair.key = -1;
if (IS_LAST_TILE)
BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs, num_remaining, oob_pair);
else
BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs);
// RLE
#pragma unroll
for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
ValueT* d_scatter = d_aggregates_out + pairs[ITEM - 1].key;
if (pairs[ITEM].key != pairs[ITEM - 1].key)
atomicAdd(d_scatter, pairs[ITEM - 1].value);
else
pairs[ITEM].value = reduction_op(pairs[ITEM - 1].value, pairs[ITEM].value);
}
// Flush last item if valid
ValueT* d_scatter = d_aggregates_out + pairs[ITEMS_PER_THREAD - 1].key;
if ((!IS_LAST_TILE) || (pairs[ITEMS_PER_THREAD - 1].key >= 0))
atomicAdd(d_scatter, pairs[ITEMS_PER_THREAD - 1].value);
}
/**
* @brief Process input tile. Specialized for reduce-by-key fixup
*
* @param num_remaining
* Number of global input items remaining (including this tile)
*
* @param tile_idx
* Tile index
*
* @param tile_offset
* Tile offset
*
* @param tile_state
* Global tile state descriptor
*
* @param use_atomic_fixup
* Marker whether to use atomicAdd (instead of reduce-by-key)
*/
template <bool IS_LAST_TILE>
__device__ __forceinline__ void ConsumeTile(OffsetT num_remaining,
int tile_idx,
OffsetT tile_offset,
ScanTileStateT &tile_state,
Int2Type<false> use_atomic_fixup)
{
KeyValuePairT pairs[ITEMS_PER_THREAD];
KeyValuePairT scatter_pairs[ITEMS_PER_THREAD];
// Load pairs
KeyValuePairT oob_pair;
oob_pair.key = -1;
if (IS_LAST_TILE)
BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs, num_remaining, oob_pair);
else
BlockLoadPairs(temp_storage.load_pairs).Load(d_pairs_in + tile_offset, pairs);
CTA_SYNC();
KeyValuePairT tile_aggregate;
if (tile_idx == 0)
{
// Exclusive scan of values and segment_flags
BlockScanT(temp_storage.scan_storage.scan).ExclusiveScan(pairs, scatter_pairs, scan_op, tile_aggregate);
// Update tile status if this is not the last tile
if (threadIdx.x == 0)
{
// Set first segment id to not trigger a flush (invalid from exclusive scan)
scatter_pairs[0].key = pairs[0].key;
if (!IS_LAST_TILE)
tile_state.SetInclusive(0, tile_aggregate);
}
}
else
{
// Exclusive scan of values and segment_flags
TilePrefixCallbackOpT prefix_op(tile_state, temp_storage.scan_storage.prefix, scan_op, tile_idx);
BlockScanT(temp_storage.scan_storage.scan).ExclusiveScan(pairs, scatter_pairs, scan_op, prefix_op);
tile_aggregate = prefix_op.GetBlockAggregate();
}
// Scatter updated values
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
if (scatter_pairs[ITEM].key != pairs[ITEM].key)
{
// Update the value at the key location
ValueT value = d_fixup_in[scatter_pairs[ITEM].key];
value = reduction_op(value, scatter_pairs[ITEM].value);
d_aggregates_out[scatter_pairs[ITEM].key] = value;
}
}
// Finalize the last item
if (IS_LAST_TILE)
{
// Last thread will output final count and last item, if necessary
if (threadIdx.x == BLOCK_THREADS - 1)
{
// If the last tile is a whole tile, the inclusive prefix contains accumulated value reduction for the last segment
if (num_remaining == TILE_ITEMS)
{
// Update the value at the key location
OffsetT last_key = pairs[ITEMS_PER_THREAD - 1].key;
d_aggregates_out[last_key] = reduction_op(tile_aggregate.value, d_fixup_in[last_key]);
}
}
}
}
/**
* @brief Scan tiles of items as part of a dynamic chained scan
*
* @param num_items
* Total number of input items
*
* @param num_tiles
* Total number of input tiles
*
* @param tile_state
* Global tile state descriptor
*/
__device__ __forceinline__ void ConsumeRange(OffsetT num_items,
int num_tiles,
ScanTileStateT &tile_state)
{
// Blocks are launched in increasing order, so just assign one tile per block
int tile_idx = (blockIdx.x * gridDim.y) + blockIdx.y; // Current tile index
OffsetT tile_offset = tile_idx * TILE_ITEMS; // Global offset for the current tile
OffsetT num_remaining = num_items - tile_offset; // Remaining items (including this tile)
if (num_remaining > TILE_ITEMS)
{
// Not the last tile (full)
ConsumeTile<false>(num_remaining, tile_idx, tile_offset, tile_state, Int2Type<USE_ATOMIC_FIXUP>());
}
else if (num_remaining > 0)
{
// The last tile (possibly partially-full)
ConsumeTile<true>(num_remaining, tile_idx, tile_offset, tile_state, Int2Type<USE_ATOMIC_FIXUP>());
}
}
};
CUB_NAMESPACE_END
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