thrust / install /include /cub /device /device_select.cuh
camenduru's picture
thanks to nvidia ❤
0dc1b04
/******************************************************************************
* 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::DeviceSelect provides device-wide, parallel operations for
* compacting selected items from sequences of data items residing within
* device-accessible memory.
*/
#pragma once
#include <iterator>
#include <stdio.h>
#include <cub/config.cuh>
#include <cub/device/dispatch/dispatch_select_if.cuh>
#include <cub/device/dispatch/dispatch_unique_by_key.cuh>
#include <cub/util_deprecated.cuh>
CUB_NAMESPACE_BEGIN
/**
* @brief DeviceSelect provides device-wide, parallel operations for compacting
* selected items from sequences of data items residing within
* device-accessible memory. ![](select_logo.png)
* @ingroup SingleModule
*
* @par Overview
* These operations apply a selection criterion to selectively copy
* items from a specified input sequence to a compact output sequence.
*
* @par Usage Considerations
* @cdp_class{DeviceSelect}
*
* @par Performance
* @linear_performance{select-flagged, select-if, and select-unique}
*
* @par
* The following chart illustrates DeviceSelect::If performance across
* different CUDA architectures for `int32` items, where 50% of the items are
* randomly selected.
*
* @image html select_if_int32_50_percent.png
*
* @par
* The following chart illustrates DeviceSelect::Unique performance across
* different CUDA architectures for `int32` items where segments have lengths
* uniformly sampled from `[1, 1000]`.
*
* @image html select_unique_int32_len_500.png
*
* @par
* @plots_below
*
*/
struct DeviceSelect
{
/**
* @brief Uses the `d_flags` sequence to selectively copy the corresponding
* items from `d_in` into `d_out`. The total number of items selected
* is written to `d_num_selected_out`. ![](select_flags_logo.png)
*
* @par
* - The value type of `d_flags` must be castable to `bool` (e.g., `bool`,
* `char`, `int`, etc.).
* - Copies of the selected items are compacted into `d_out` and maintain
* their original relative ordering.
* - The range `[d_out, d_out + *d_num_selected_out)` shall not overlap
* `[d_in, d_in + num_items)`, `[d_flags, d_flags + num_items)` nor
* `d_num_selected_out` in any way.
* - @devicestorage
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an `int` device vector.
* @par
* @code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_select.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 = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSelect::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::DeviceSelect::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]
* // 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 selected
* 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 `temp_storage_bytes` and no work
* is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of `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 selected data items
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected
* (i.e., length of `d_out`)
*
* @param[in] num_items
* Total number of input items (i.e., length of `d_in`)
*
* @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)
return DispatchSelectIf<InputIteratorT,
FlagIterator,
OutputIteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
false>::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 `d_flags` sequence to selectively compact the items in
* `d_data`. The total number of items selected is written to
* `d_num_selected_out`. ![](select_flags_logo.png)
*
* @par
* - The value type of `d_flags` must be castable to `bool` (e.g., `bool`,
* `char`, `int`, etc.).
* - Copies of the selected items are compacted in-place and maintain
* their original relative ordering.
* - The `d_data` may equal `d_flags`. The range
* `[d_data, d_data + num_items)` shall not overlap
* `[d_flags, d_flags + num_items)` in any other way.
* - @devicestorage
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an `int` device vector.
* @par
* @code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_select.cuh>
*
* // Declare, allocate, and initialize device-accessible pointers for input,
* // flags, and output
* int num_items; // e.g., 8
* int *d_data; // 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_num_selected_out; // e.g., [ ]
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSelect::Flagged(
* d_temp_storage, temp_storage_bytes,
* d_in, d_flags, d_num_selected_out, num_items);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DeviceSelect::Flagged(
* d_temp_storage, temp_storage_bytes,
* d_in, d_flags, d_num_selected_out, num_items);
*
* // d_data <-- [1, 4, 6, 7]
* // d_num_selected_out <-- [4]
*
* @endcode
*
* @tparam IteratorT
* **[inferred]** Random-access iterator type for reading and writing
* selected items \iterator
*
* @tparam FlagIterator
* **[inferred]** Random-access input iterator type for reading selection
* flags \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 `temp_storage_bytes` and no work
* is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of `d_temp_storage` allocation
*
* @param[in,out] d_data
* Pointer to the sequence of data items
*
* @param[in] d_flags
* Pointer to the input sequence of selection flags
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected
*
* @param[in] num_items
* Total number of input items (i.e., length of `d_data`)
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename IteratorT,
typename FlagIterator,
typename NumSelectedIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Flagged(void *d_temp_storage,
size_t &temp_storage_bytes,
IteratorT d_data,
FlagIterator d_flags,
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)
constexpr bool may_alias = true;
return DispatchSelectIf<IteratorT,
FlagIterator,
IteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
false,
may_alias>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_data, // in
d_flags,
d_data, // out
d_num_selected_out,
SelectOp(),
EqualityOp(),
num_items,
stream);
}
template <typename IteratorT,
typename FlagIterator,
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,
IteratorT d_data,
FlagIterator d_flags,
NumSelectedIteratorT d_num_selected_out,
int num_items,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return Flagged<IteratorT, FlagIterator, NumSelectedIteratorT>(
d_temp_storage,
temp_storage_bytes,
d_data,
d_flags,
d_num_selected_out,
num_items,
stream);
}
/**
* @brief Uses the `select_op` functor to selectively copy items from `d_in`
* into `d_out`. The total number of items selected is written to
* `d_num_selected_out`. ![](select_logo.png)
*
* @par
* - Copies of the selected items are compacted into `d_out` and maintain
* their original relative ordering.
* - The range `[d_out, d_out + *d_num_selected_out)` shall not overlap
* `[d_in, d_in + num_items)` nor `d_num_selected_out` in any way.
* - @devicestorage
*
* @par Performance
* The following charts illustrate saturated select-if performance across
* different CUDA architectures for `int32` and `int64` items, respectively.
* Items are selected with 50% probability.
*
* @image html select_if_int32_50_percent.png
* @image html select_if_int64_50_percent.png
*
* @par
* The following charts are similar, but 5% selection probability:
*
* @image html select_if_int32_5_percent.png
* @image html select_if_int64_5_percent.png
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an `int` device vector.
* @par
* @code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_select.cuh>
*
* // Functor type for selecting values less than some criteria
* struct LessThan
* {
* int compare;
*
* CUB_RUNTIME_FUNCTION __forceinline__
* 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 = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSelect::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::DeviceSelect::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]
* // 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 selected
* items \iterator
*
* @tparam NumSelectedIteratorT
* **[inferred]** Output iterator type for recording the number of items
* selected \iterator
*
* @tparam SelectOp
* **[inferred]** Selection operator 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 `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 selected data items
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected
* (i.e., length of `d_out`)
*
* @param[in] num_items
* Total number of input items (i.e., length of `d_in`)
*
* @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)
return DispatchSelectIf<InputIteratorT,
FlagIterator,
OutputIteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
false>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
NULL,
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 the `select_op` functor to selectively compact items in
* `d_data`. The total number of items selected is written to
* `d_num_selected_out`. ![](select_logo.png)
*
* @par
* - Copies of the selected items are compacted in `d_data` and maintain
* their original relative ordering.
* - @devicestorage
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an `int` device vector.
* @par
* @code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_select.cuh>
*
* // Functor type for selecting values less than some criteria
* struct LessThan
* {
* int compare;
*
* CUB_RUNTIME_FUNCTION __forceinline__
* 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_data; // e.g., [0, 2, 3, 9, 5, 2, 81, 8]
* int *d_num_selected_out; // e.g., [ ]
* LessThan select_op(7);
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSelect::If(
* d_temp_storage, temp_storage_bytes,
* d_data, d_num_selected_out, num_items, select_op);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DeviceSelect::If(
* d_temp_storage, temp_storage_bytes,
* d_data, d_num_selected_out, num_items, select_op);
*
* // d_data <-- [0, 2, 3, 5, 2]
* // d_num_selected_out <-- [5]
* @endcode
*
* @tparam IteratorT
* **[inferred]** Random-access input iterator type for reading and
* writing items \iterator
*
* @tparam NumSelectedIteratorT
* **[inferred]** Output iterator type for recording the number of items
* selected \iterator
*
* @tparam SelectOp
* **[inferred]** Selection operator 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 `d_temp_storage` allocation
*
* @param[in,out] d_data
* Pointer to the sequence of data items
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected
*
* @param[in] num_items
* Total number of input items (i.e., length of `d_data`)
*
* @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 IteratorT,
typename NumSelectedIteratorT,
typename SelectOp>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
If(void *d_temp_storage,
size_t &temp_storage_bytes,
IteratorT d_data,
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)
constexpr bool may_alias = true;
return DispatchSelectIf<IteratorT,
FlagIterator,
IteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
false,
may_alias>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_data, // in
NULL,
d_data, // out
d_num_selected_out,
select_op,
EqualityOp(),
num_items,
stream);
}
template <typename IteratorT,
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,
IteratorT d_data,
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<IteratorT, NumSelectedIteratorT, SelectOp>(d_temp_storage,
temp_storage_bytes,
d_data,
d_num_selected_out,
num_items,
select_op,
stream);
}
/**
* @brief Given an input sequence `d_in` having runs of consecutive
* equal-valued keys, only the first key from each run is selectively
* copied to `d_out`. The total number of items selected is written to
* `d_num_selected_out`. ![](unique_logo.png)
*
* @par
* - The `==` equality operator is used to determine whether keys are
* equivalent
* - Copies of the selected items are compacted into `d_out` and maintain
* their original relative ordering.
* - The range `[d_out, d_out + *d_num_selected_out)` shall not overlap
* `[d_in, d_in + num_items)` nor `d_num_selected_out` in any way.
* - @devicestorage
*
* @par Performance
* The following charts illustrate saturated select-unique performance across different
* CUDA architectures for `int32` and `int64` items, respectively. Segments
* have lengths uniformly sampled from `[1, 1000]`.
*
* @image html select_unique_int32_len_500.png
* @image html select_unique_int64_len_500.png
*
* @par
* The following charts are similar, but with segment lengths uniformly
* sampled from `[1, 10]`:
*
* @image html select_unique_int32_len_5.png
* @image html select_unique_int64_len_5.png
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an `int` device vector.
* @par
* @code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_select.cuh>
*
* // Declare, allocate, and initialize device-accessible pointers
* // for input and output
* int num_items; // e.g., 8
* int *d_in; // e.g., [0, 2, 2, 9, 5, 5, 5, 8]
* int *d_out; // e.g., [ , , , , , , , ]
* int *d_num_selected_out; // e.g., [ ]
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSelect::Unique(
* d_temp_storage, temp_storage_bytes,
* d_in, d_out, d_num_selected_out, num_items);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DeviceSelect::Unique(
* d_temp_storage, temp_storage_bytes,
* d_in, d_out, d_num_selected_out, num_items);
*
* // d_out <-- [0, 2, 9, 5, 8]
* // 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 selected
* 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 `temp_storage_bytes` and no work
* is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of `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 selected data items
*
* @param[out] d_num_selected_out
* Pointer to the output total number of items selected
* (i.e., length of `d_out`)
*
* @param[in] num_items
* Total number of input items (i.e., length of `d_in`)
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename InputIteratorT,
typename OutputIteratorT,
typename NumSelectedIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Unique(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
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 FlagIterator = NullType *; // FlagT iterator type (not used)
using SelectOp = NullType; // Selection op (not used)
using EqualityOp = Equality; // Default == operator
return DispatchSelectIf<InputIteratorT,
FlagIterator,
OutputIteratorT,
NumSelectedIteratorT,
SelectOp,
EqualityOp,
OffsetT,
false>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
NULL,
d_out,
d_num_selected_out,
SelectOp(),
EqualityOp(),
num_items,
stream);
}
template <typename InputIteratorT,
typename OutputIteratorT,
typename NumSelectedIteratorT>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Unique(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
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 Unique<InputIteratorT, OutputIteratorT, NumSelectedIteratorT>(
d_temp_storage,
temp_storage_bytes,
d_in,
d_out,
d_num_selected_out,
num_items,
stream);
}
/**
* @brief Given an input sequence `d_keys_in` and `d_values_in` with runs of
* key-value pairs with consecutive equal-valued keys, only the first
* key and its value from each run is selectively copied to
* `d_keys_out` and `d_values_out`. The total number of items selected
* is written to `d_num_selected_out`. ![](unique_logo.png)
*
* @par
* - The `==` equality operator is used to determine whether keys are
* equivalent
* - Copies of the selected items are compacted into `d_out` and maintain
* their original relative ordering.
* - In-place operations are not supported. There must be no overlap between
* any of the provided ranges:
* - `[d_keys_in, d_keys_in + num_items)`
* - `[d_keys_out, d_keys_out + *d_num_selected_out)`
* - `[d_values_in, d_values_in + num_items)`
* - `[d_values_out, d_values_out + *d_num_selected_out)`
* - `[d_num_selected_out, d_num_selected_out + 1)`
* - @devicestorage
*
* @par Snippet
* The code snippet below illustrates the compaction of items selected from
* an `int` device vector.
* @par
* @code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_select.cuh>
*
* // Declare, allocate, and initialize device-accessible pointers
* // for input and output
* int num_items; // e.g., 8
* int *d_keys_in; // e.g., [0, 2, 2, 9, 5, 5, 5, 8]
* int *d_values_in; // e.g., [1, 2, 3, 4, 5, 6, 7, 8]
* int *d_keys_out; // e.g., [ , , , , , , , ]
* int *d_values_out; // e.g., [ , , , , , , , ]
* int *d_num_selected_out; // e.g., [ ]
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceSelect::UniqueByKey(
* d_temp_storage, temp_storage_bytes,
* d_keys_in, d_values_in,
* d_keys_out, d_values_out, d_num_selected_out, num_items);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run selection
* cub::DeviceSelect::UniqueByKey(
* d_temp_storage, temp_storage_bytes,
* d_keys_in, d_values_in,
* d_keys_out, d_values_out, d_num_selected_out, num_items);
*
* // d_keys_out <-- [0, 2, 9, 5, 8]
* // d_values_out <-- [1, 2, 4, 5, 8]
* // d_num_selected_out <-- [5]
* @endcode
*
* @tparam KeyInputIteratorT
* **[inferred]** Random-access input iterator type for reading input
* keys \iterator
*
* @tparam ValueInputIteratorT
* **[inferred]** Random-access input iterator type for reading input
* values \iterator
*
* @tparam KeyOutputIteratorT
* **[inferred]** Random-access output iterator type for writing selected
* keys \iterator
*
* @tparam ValueOutputIteratorT
* **[inferred]** Random-access output iterator type for writing selected
* values \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 `temp_storage_bytes` and no work
* is done.
*
* @param[in,out] temp_storage_bytes
* Reference to size in bytes of `d_temp_storage` allocation
*
* @param[in] d_keys_in
* Pointer to the input sequence of keys
*
* @param[in] d_values_in
* Pointer to the input sequence of values
*
* @param[out] d_keys_out
* Pointer to the output sequence of selected keys
*
* @param[out] d_values_out
* Pointer to the output sequence of selected values
*
* @param[out] d_num_selected_out
* Pointer to the total number of items selected (i.e., length of
* `d_keys_out` or `d_values_out`)
*
* @param[in] num_items
* Total number of input items (i.e., length of `d_keys_in` or
* `d_values_in`)
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename KeyInputIteratorT,
typename ValueInputIteratorT,
typename KeyOutputIteratorT,
typename ValueOutputIteratorT,
typename NumSelectedIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
UniqueByKey(void *d_temp_storage,
size_t &temp_storage_bytes,
KeyInputIteratorT d_keys_in,
ValueInputIteratorT d_values_in,
KeyOutputIteratorT d_keys_out,
ValueOutputIteratorT d_values_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
cudaStream_t stream = 0)
{
using OffsetT = int;
using EqualityOp = Equality;
return DispatchUniqueByKey<KeyInputIteratorT,
ValueInputIteratorT,
KeyOutputIteratorT,
ValueOutputIteratorT,
NumSelectedIteratorT,
EqualityOp,
OffsetT>::Dispatch(d_temp_storage,
temp_storage_bytes,
d_keys_in,
d_values_in,
d_keys_out,
d_values_out,
d_num_selected_out,
EqualityOp(),
num_items,
stream);
}
template <typename KeyInputIteratorT,
typename ValueInputIteratorT,
typename KeyOutputIteratorT,
typename ValueOutputIteratorT,
typename NumSelectedIteratorT>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
UniqueByKey(void *d_temp_storage,
size_t &temp_storage_bytes,
KeyInputIteratorT d_keys_in,
ValueInputIteratorT d_values_in,
KeyOutputIteratorT d_keys_out,
ValueOutputIteratorT d_values_out,
NumSelectedIteratorT d_num_selected_out,
int num_items,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return UniqueByKey<KeyInputIteratorT,
ValueInputIteratorT,
KeyOutputIteratorT,
ValueOutputIteratorT,
NumSelectedIteratorT>(d_temp_storage,
temp_storage_bytes,
d_keys_in,
d_values_in,
d_keys_out,
d_values_out,
d_num_selected_out,
num_items,
stream);
}
};
/**
* @example example_device_select_flagged.cu
* @example example_device_select_if.cu
* @example example_device_select_unique.cu
*/
CUB_NAMESPACE_END