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/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2022, 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::DevicePartition provides device-wide, parallel operations for
* partitioning sequences of data items residing within device-accessible memory.
*/
#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 <stdio.h>
#include <iterator>
#include <cub/device/dispatch/dispatch_select_if.cuh>
#include <cub/device/dispatch/dispatch_three_way_partition.cuh>
#include <cub/util_deprecated.cuh>
CUB_NAMESPACE_BEGIN
/**
* @brief DevicePartition provides device-wide, parallel operations for
* partitioning sequences of data items residing within device-accessible
* memory. 
* @ingroup SingleModule
*
* @par Overview
* These operations apply a selection criterion to construct a partitioned
* output sequence from items selected/unselected from a specified input
* sequence.
*
* @par Usage Considerations
* \cdp_class{DevicePartition}
*
* @par Performance
* \linear_performance{partition}
*
* @par
* The following chart illustrates DevicePartition::If
* performance across different CUDA architectures for @p int32 items,
* where 50% of the items are randomly selected for the first partition.
* \plots_below
*
* @image html partition_if_int32_50_percent.png
*
*/
struct DevicePartition
{
/**
* @brief Uses the @p d_flags sequence to split the corresponding items from
* @p d_in into a partitioned sequence @p d_out. The total number of
* items copied into the first partition is written to
* @p d_num_selected_out. 
*
* @par
* - The value type of @p d_flags must be castable to @p bool (e.g.,
* @p bool, @p char, @p int, etc.).
* - Copies of the selected items are compacted into @p d_out and maintain
* their original relative ordering, however copies of the unselected
* items are compacted into the rear of @p d_out in reverse order.
* - The range `[d_out, d_out + num_items)` shall not overlap
* `[d_in, d_in + num_items)` nor `[d_flags, d_flags + num_items)` in any
* way. The range `[d_in, d_in + num_items)` may overlap
* `[d_flags, d_flags + num_items)`.
* - \devicestorage
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an @p int device vector.
* @par
* @code
* #include <cub/cub.cuh>
* // or equivalently <cub/device/device_partition.cuh>
*
* // Declare, allocate, and initialize device-accessible pointers for
* // input, flags, and output
* int num_items; // e.g., 8
* int *d_in; // e.g., [1, 2, 3, 4, 5, 6, 7, 8]
* char *d_flags; // e.g., [1, 0, 0, 1, 0, 1, 1, 0]
* int *d_out; // e.g., [ , , , , , , , ]
* int *d_num_selected_out; // e.g., [ ]
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = nullptr;
* std::size_t temp_storage_bytes = 0;
* cub::DevicePartition::Flagged(
* d_temp_storage, temp_storage_bytes,
* d_in, d_flags, d_out, d_num_selected_out, num_items);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DevicePartition::Flagged(
* d_temp_storage, temp_storage_bytes,
* d_in, d_flags, d_out, d_num_selected_out, num_items);
*
* // d_out <-- [1, 4, 6, 7, 8, 5, 3, 2]
* // d_num_selected_out <-- [4]
* @endcode
*
* @tparam InputIteratorT
* **[inferred]** Random-access input iterator type for reading
* input items \iterator
*
* @tparam FlagIterator
* **[inferred]** Random-access input iterator type for reading
* selection flags \iterator
*
* @tparam OutputIteratorT
* **[inferred]** Random-access output iterator type for writing
* output items \iterator
*
* @tparam NumSelectedIteratorT
* **[inferred]** Output iterator type for recording the number
* of items selected \iterator
*
* @param[in] d_temp_storage
* Device-accessible allocation of temporary storage. When `nullptr`, the
* required allocation size is written to @p temp_storage_bytes and no
* work is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of @p d_temp_storage allocation
*
* @param[in] d_in
* Pointer to the input sequence of data items
*
* @param[in] d_flags
* Pointer to the input sequence of selection flags
*
* @param[out] d_out
* Pointer to the output sequence of partitioned data items
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected (i.e., the
* offset of the unselected partition)
*
* @param[in] num_items
* Total number of items to select from
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename InputIteratorT,
typename FlagIterator,
typename OutputIteratorT,
typename NumSelectedIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Flagged(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
FlagIterator d_flags,
OutputIteratorT d_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
cudaStream_t stream = 0)
{
using OffsetT = int; // Signed integer type for global offsets
using SelectOp = NullType; // Selection op (not used)
using EqualityOp = NullType; // Equality operator (not used)
using DispatchSelectIfT = DispatchSelectIf<InputIteratorT,
FlagIterator,
OutputIteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
true>;
return DispatchSelectIfT::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
d_flags,
d_out,
d_num_selected_out,
SelectOp{},
EqualityOp{},
num_items,
stream);
}
template <typename InputIteratorT,
typename FlagIterator,
typename OutputIteratorT,
typename NumSelectedIteratorT>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Flagged(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
FlagIterator d_flags,
OutputIteratorT d_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return Flagged<InputIteratorT,
FlagIterator,
OutputIteratorT,
NumSelectedIteratorT>(d_temp_storage,
temp_storage_bytes,
d_in,
d_flags,
d_out,
d_num_selected_out,
num_items,
stream);
}
/**
* @brief Uses the @p select_op functor to split the corresponding items
* from @p d_in into a partitioned sequence @p d_out. The total
* number of items copied into the first partition is written to
* @p d_num_selected_out. 
*
* @par
* - Copies of the selected items are compacted into @p d_out and maintain
* their original relative ordering, however copies of the unselected
* items are compacted into the rear of @p d_out in reverse order.
* - The range `[d_out, d_out + num_items)` shall not overlap
* `[d_in, d_in + num_items)` in any way.
* - \devicestorage
*
* @par Performance
* The following charts illustrate saturated partition-if performance across
* different CUDA architectures for @p int32 and @p int64 items,
* respectively. Items are selected for the first partition with 50%
* probability.
*
* @image html partition_if_int32_50_percent.png
* @image html partition_if_int64_50_percent.png
*
* @par
* The following charts are similar, but 5% selection probability for the
* first partition:
*
* @image html partition_if_int32_5_percent.png
* @image html partition_if_int64_5_percent.png
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an @p int device vector.
* @par
* @code
* #include <cub/cub.cuh>
* // or equivalently <cub/device/device_partition.cuh>
*
* // Functor type for selecting values less than some criteria
* struct LessThan
* {
* int compare;
*
* CUB_RUNTIME_FUNCTION __forceinline__
* explicit LessThan(int compare) : compare(compare) {}
*
* CUB_RUNTIME_FUNCTION __forceinline__
* bool operator()(const int &a) const
* {
* return (a < compare);
* }
* };
*
* // Declare, allocate, and initialize device-accessible pointers for
* // input and output
* int num_items; // e.g., 8
* int *d_in; // e.g., [0, 2, 3, 9, 5, 2, 81, 8]
* int *d_out; // e.g., [ , , , , , , , ]
* int *d_num_selected_out; // e.g., [ ]
* LessThan select_op(7);
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = nullptr;
* std::size_t temp_storage_bytes = 0;
* cub::DevicePartition::If(
* d_temp_storage, temp_storage_bytes,
* d_in, d_out, d_num_selected_out, num_items, select_op);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DevicePartition::If(
* d_temp_storage, temp_storage_bytes,
* d_in, d_out, d_num_selected_out, num_items, select_op);
*
* // d_out <-- [0, 2, 3, 5, 2, 8, 81, 9]
* // d_num_selected_out <-- [5]
*
* @endcode
*
* @tparam InputIteratorT
* **[inferred]** Random-access input iterator type for reading input
* items \iterator
*
* @tparam OutputIteratorT
* **[inferred]** Random-access output iterator type for writing output
* items \iterator
*
* @tparam NumSelectedIteratorT
* **[inferred]** Output iterator type for recording the number of items
* selected \iterator
*
* @tparam SelectOp
* **[inferred]** Selection functor type having member
* `bool operator()(const T &a)`
*
* @param[in] d_temp_storage
* Device-accessible allocation of temporary storage. When `nullptr`, the
* required allocation size is written to `temp_storage_bytes` and no
* work is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of @p d_temp_storage allocation
*
* @param[in] d_in
* Pointer to the input sequence of data items
*
* @param[out] d_out
* Pointer to the output sequence of partitioned data items
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected (i.e., the
* offset of the unselected partition)
*
* @param[in] num_items
* Total number of items to select from
*
* @param[in] select_op
* Unary selection operator
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename InputIteratorT,
typename OutputIteratorT,
typename NumSelectedIteratorT,
typename SelectOp>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
If(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
SelectOp select_op,
cudaStream_t stream = 0)
{
using OffsetT = int; // Signed integer type for global offsets
using FlagIterator = NullType *; // FlagT iterator type (not used)
using EqualityOp = NullType; // Equality operator (not used)
using DispatchSelectIfT = DispatchSelectIf<InputIteratorT,
FlagIterator,
OutputIteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
true>;
return DispatchSelectIfT::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
nullptr,
d_out,
d_num_selected_out,
select_op,
EqualityOp{},
num_items,
stream);
}
template <typename InputIteratorT,
typename OutputIteratorT,
typename NumSelectedIteratorT,
typename SelectOp>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
If(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
OutputIteratorT d_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
SelectOp select_op,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return If<InputIteratorT, OutputIteratorT, NumSelectedIteratorT, SelectOp>(
d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
d_num_selected_out,
num_items,
select_op,
stream);
}
/**
* @brief Uses two functors to split the corresponding items from @p d_in
* into a three partitioned sequences @p d_first_part_out
* @p d_second_part_out and @p d_unselected_out.
* The total number of items copied into the first partition is written
* to `d_num_selected_out[0]`, while the total number of items copied
* into the second partition is written to `d_num_selected_out[1]`.
*
* @par
* - Copies of the items selected by @p select_first_part_op are compacted
* into @p d_first_part_out and maintain their original relative ordering.
* - Copies of the items selected by @p select_second_part_op are compacted
* into @p d_second_part_out and maintain their original relative ordering.
* - Copies of the unselected items are compacted into the
* @p d_unselected_out in reverse order.
* - The ranges `[d_out, d_out + num_items)`,
* `[d_first_part_out, d_first_part_out + d_num_selected_out[0])`,
* `[d_second_part_out, d_second_part_out + d_num_selected_out[1])`,
* `[d_unselected_out, d_unselected_out + num_items - d_num_selected_out[0] - d_num_selected_out[1])`,
* shall not overlap in any way.
*
* @par Snippet
* The code snippet below illustrates how this algorithm can partition an
* input vector into small, medium, and large items so that the relative
* order of items remain deterministic.
*
* Let's consider any value that doesn't exceed six a small one. On the
* other hand, any value that exceeds 50 will be considered a large one.
* Since the value used to define a small part doesn't match one that
* defines the large part, the intermediate segment is implied.
*
* These definitions partition a value space into three categories. We want
* to preserve the order of items in which they appear in the input vector.
* Since the algorithm provides stable partitioning, this is possible.
*
* Since the number of items in each category is unknown beforehand, we need
* three output arrays of num_items elements each. To reduce the memory
* requirements, we can combine the output storage for two categories.
*
* Since each value falls precisely in one category, it's safe to add
* "large" values into the head of the shared output vector and the "middle"
* values into its tail. To add items into the tail of the output array, we
* can use `thrust::reverse_iterator`.
* @par
* @code
* #include <cub/cub.cuh>
* // or equivalently <cub/device/device_partition.cuh>
*
* // Functor type for selecting values less than some criteria
* struct LessThan
* {
* int compare;
*
* CUB_RUNTIME_FUNCTION __forceinline__
* explicit LessThan(int compare) : compare(compare) {}
*
* CUB_RUNTIME_FUNCTION __forceinline__
* bool operator()(const int &a) const
* {
* return a < compare;
* }
* };
*
* // Functor type for selecting values greater than some criteria
* struct GreaterThan
* {
* int compare;
*
* CUB_RUNTIME_FUNCTION __forceinline__
* explicit GreaterThan(int compare) : compare(compare) {}
*
* CUB_RUNTIME_FUNCTION __forceinline__
* bool operator()(const int &a) const
* {
* return a > compare;
* }
* };
*
* // Declare, allocate, and initialize device-accessible pointers for
* // input and output
* int num_items; // e.g., 8
* int *d_in; // e.g., [0, 2, 3, 9, 5, 2, 81, 8]
* int *d_large_and_unselected_out; // e.g., [ , , , , , , , ]
* int *d_small_out; // e.g., [ , , , , , , , ]
* int *d_num_selected_out; // e.g., [ , ]
* thrust::reverse_iterator<T> unselected_out(d_large_and_unselected_out + num_items);
* LessThan small_items_selector(7);
* GreaterThan large_items_selector(50);
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = nullptr;
* std::size_t temp_storage_bytes = 0;
* cub::DevicePartition::If(
* d_temp_storage, temp_storage_bytes,
* d_in, d_large_and_medium_out, d_small_out, unselected_out,
* d_num_selected_out, num_items,
* large_items_selector, small_items_selector);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DevicePartition::If(
* d_temp_storage, temp_storage_bytes,
* d_in, d_large_and_medium_out, d_small_out, unselected_out,
* d_num_selected_out, num_items,
* large_items_selector, small_items_selector);
*
* // d_large_and_unselected_out <-- [ 81, , , , , , 8, 9 ]
* // d_small_out <-- [ 0, 2, 3, 5, 2, , , ]
* // d_num_selected_out <-- [ 1, 5 ]
* @endcode
*
* @tparam InputIteratorT
* **[inferred]** Random-access input iterator type for reading
* input items \iterator
*
* @tparam FirstOutputIteratorT
* **[inferred]** Random-access output iterator type for writing output
* items selected by first operator \iterator
*
* @tparam SecondOutputIteratorT
* **[inferred]** Random-access output iterator type for writing output
* items selected by second operator \iterator
*
* @tparam UnselectedOutputIteratorT
* **[inferred]** Random-access output iterator type for writing
* unselected items \iterator
*
* @tparam NumSelectedIteratorT
* **[inferred]** Output iterator type for recording the number of items
* selected \iterator
*
* @tparam SelectFirstPartOp
* **[inferred]** Selection functor type having member
* `bool operator()(const T &a)`
*
* @tparam SelectSecondPartOp
* **[inferred]** Selection functor type having member
* `bool operator()(const T &a)`
*
* @param[in] d_temp_storage
* Device-accessible allocation of temporary storage. When `nullptr`, the
* required allocation size is written to @p temp_storage_bytes and
* no work is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of @p d_temp_storage allocation
*
* @param[in] d_in
* Pointer to the input sequence of data items
*
* @param[out] d_first_part_out
* Pointer to the output sequence of data items selected by
* @p select_first_part_op
*
* @param[out] d_second_part_out
* Pointer to the output sequence of data items selected by
* @p select_second_part_op
*
* @param[out] d_unselected_out
* Pointer to the output sequence of unselected data items
*
* @param[out] d_num_selected_out
* Pointer to the output array with two elements, where total number of
* items selected by @p select_first_part_op is stored as
* `d_num_selected_out[0]` and total number of items selected by
* @p select_second_part_op is stored as `d_num_selected_out[1]`,
* respectively
*
* @param[in] num_items
* Total number of items to select from
*
* @param[in] select_first_part_op
* Unary selection operator to select @p d_first_part_out
*
* @param[in] select_second_part_op
* Unary selection operator to select @p d_second_part_out
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename InputIteratorT,
typename FirstOutputIteratorT,
typename SecondOutputIteratorT,
typename UnselectedOutputIteratorT,
typename NumSelectedIteratorT,
typename SelectFirstPartOp,
typename SelectSecondPartOp>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
If(void *d_temp_storage,
std::size_t &temp_storage_bytes,
InputIteratorT d_in,
FirstOutputIteratorT d_first_part_out,
SecondOutputIteratorT d_second_part_out,
UnselectedOutputIteratorT d_unselected_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
SelectFirstPartOp select_first_part_op,
SelectSecondPartOp select_second_part_op,
cudaStream_t stream = 0)
{
using OffsetT = int;
using DispatchThreeWayPartitionIfT =
DispatchThreeWayPartitionIf<InputIteratorT,
FirstOutputIteratorT,
SecondOutputIteratorT,
UnselectedOutputIteratorT,
NumSelectedIteratorT,
SelectFirstPartOp,
SelectSecondPartOp,
OffsetT>;
return DispatchThreeWayPartitionIfT::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
d_first_part_out,
d_second_part_out,
d_unselected_out,
d_num_selected_out,
select_first_part_op,
select_second_part_op,
num_items,
stream);
}
template <typename InputIteratorT,
typename FirstOutputIteratorT,
typename SecondOutputIteratorT,
typename UnselectedOutputIteratorT,
typename NumSelectedIteratorT,
typename SelectFirstPartOp,
typename SelectSecondPartOp>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
If(void *d_temp_storage,
std::size_t &temp_storage_bytes,
InputIteratorT d_in,
FirstOutputIteratorT d_first_part_out,
SecondOutputIteratorT d_second_part_out,
UnselectedOutputIteratorT d_unselected_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
SelectFirstPartOp select_first_part_op,
SelectSecondPartOp select_second_part_op,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return If<InputIteratorT,
FirstOutputIteratorT,
SecondOutputIteratorT,
UnselectedOutputIteratorT,
NumSelectedIteratorT,
SelectFirstPartOp,
SelectSecondPartOp>(d_temp_storage,
temp_storage_bytes,
d_in,
d_first_part_out,
d_second_part_out,
d_unselected_out,
d_num_selected_out,
num_items,
select_first_part_op,
select_second_part_op,
stream);
}
};
/**
* @example example_device_partition_flagged.cu
* @example example_device_partition_if.cu
*/
CUB_NAMESPACE_END
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