device_partition.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::DevicePartition provides device-wide, parallel operations for partitioning sequences of data items residing within device-accessible memory.
*/
#pragma once
#include <stdio.h>
#include <iterator>
#include "dispatch/dispatch_select_if.cuh"
#include "../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/**
* \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.
* - \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 = NULL;
* 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 <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
* \tparam FlagIterator <b>[inferred]</b> Random-access input iterator type for reading selection flags \iterator
* \tparam OutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing output items \iterator
* \tparam NumSelectedIteratorT <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
*/
template <
typename InputIteratorT,
typename FlagIterator,
typename OutputIteratorT,
typename NumSelectedIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
static cudaError_t Flagged(
void* d_temp_storage, ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
FlagIterator d_flags, ///< [in] Pointer to the input sequence of selection flags
OutputIteratorT d_out, ///< [out] Pointer to the output sequence of partitioned data items
NumSelectedIteratorT d_num_selected_out, ///< [out] Pointer to the output total number of items selected (i.e., the offset of the unselected partition)
int num_items, ///< [in] Total number of items to select from
cudaStream_t stream = 0, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous = false) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. May cause significant slowdown. Default is \p false.
{
typedef int OffsetT; // Signed integer type for global offsets
typedef NullType SelectOp; // Selection op (not used)
typedef NullType EqualityOp; // Equality operator (not used)
return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, true>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_flags,
d_out,
d_num_selected_out,
SelectOp(),
EqualityOp(),
num_items,
stream,
debug_synchronous);
}
/**
* \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.
* - \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__
* 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, 8, 81, 9]
* // d_num_selected_out <-- [5]
*
* \endcode
*
* \tparam InputIteratorT <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
* \tparam OutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing output items \iterator
* \tparam NumSelectedIteratorT <b>[inferred]</b> Output iterator type for recording the number of items selected \iterator
* \tparam SelectOp <b>[inferred]</b> Selection functor type having member <tt>bool operator()(const T &a)</tt>
*/
template <
typename InputIteratorT,
typename OutputIteratorT,
typename NumSelectedIteratorT,
typename SelectOp>
CUB_RUNTIME_FUNCTION __forceinline__
static cudaError_t If(
void* d_temp_storage, ///< [in] %Device-accessible allocation of temporary storage. When NULL, the required allocation size is written to \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Reference to size in bytes of \p d_temp_storage allocation
InputIteratorT d_in, ///< [in] Pointer to the input sequence of data items
OutputIteratorT d_out, ///< [out] Pointer to the output sequence of partitioned data items
NumSelectedIteratorT d_num_selected_out, ///< [out] Pointer to the output total number of items selected (i.e., the offset of the unselected partition)
int num_items, ///< [in] Total number of items to select from
SelectOp select_op, ///< [in] Unary selection operator
cudaStream_t stream = 0, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous = false) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. May cause significant slowdown. Default is \p false.
{
typedef int OffsetT; // Signed integer type for global offsets
typedef NullType* FlagIterator; // FlagT iterator type (not used)
typedef NullType EqualityOp; // Equality operator (not used)
return DispatchSelectIf<InputIteratorT, FlagIterator, OutputIteratorT, NumSelectedIteratorT, SelectOp, EqualityOp, OffsetT, true>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
NULL,
d_out,
d_num_selected_out,
select_op,
EqualityOp(),
num_items,
stream,
debug_synchronous);
}
};
/**
* \example example_device_partition_flagged.cu
* \example example_device_partition_if.cu
*/
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)