device_run_length_encode.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::DeviceRunLengthEncode provides device-wide, parallel operations for computing a run-length encoding across a sequence of data items residing within device-accessible memory.
*/
#pragma once
#include <stdio.h>
#include <iterator>
#include "dispatch/dispatch_rle.cuh"
#include "dispatch/dispatch_reduce_by_key.cuh"
#include "../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/**
* \brief DeviceRunLengthEncode provides device-wide, parallel operations for demarcating "runs" of same-valued items within a sequence residing within device-accessible memory. ![](run_length_encode_logo.png)
* \ingroup SingleModule
*
* \par Overview
* A <a href="http://en.wikipedia.org/wiki/Run-length_encoding"><em>run-length encoding</em></a>
* computes a simple compressed representation of a sequence of input elements such that each
* maximal "run" of consecutive same-valued data items is encoded as a single data value along with a
* count of the elements in that run.
*
* \par Usage Considerations
* \cdp_class{DeviceRunLengthEncode}
*
* \par Performance
* \linear_performance{run-length encode}
*
* \par
* The following chart illustrates DeviceRunLengthEncode::RunLengthEncode performance across
* different CUDA architectures for \p int32 items.
* Segments have lengths uniformly sampled from [1,1000].
*
* \image html rle_int32_len_500.png
*
* \par
* \plots_below
*
*/
struct DeviceRunLengthEncode
{
/**
* \brief Computes a run-length encoding of the sequence \p d_in.
*
* \par
* - For the <em>i</em><sup>th</sup> run encountered, the first key of the run and its length are written to
* <tt>d_unique_out[<em>i</em>]</tt> and <tt>d_counts_out[<em>i</em>]</tt>,
* respectively.
* - The total number of runs encountered is written to \p d_num_runs_out.
* - The <tt>==</tt> equality operator is used to determine whether values are equivalent
* - \devicestorage
*
* \par Performance
* The following charts illustrate saturated encode performance across different
* CUDA architectures for \p int32 and \p int64 items, respectively. Segments have
* lengths uniformly sampled from [1,1000].
*
* \image html rle_int32_len_500.png
* \image html rle_int64_len_500.png
*
* \par
* The following charts are similar, but with segment lengths uniformly sampled from [1,10]:
*
* \image html rle_int32_len_5.png
* \image html rle_int64_len_5.png
*
* \par Snippet
* The code snippet below illustrates the run-length encoding of a sequence of \p int values.
* \par
* \code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_run_length_encode.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_unique_out; // e.g., [ , , , , , , , ]
* int *d_counts_out; // e.g., [ , , , , , , , ]
* int *d_num_runs_out; // e.g., [ ]
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceRunLengthEncode::Encode(d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_counts_out, d_num_runs_out, num_items);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run encoding
* cub::DeviceRunLengthEncode::Encode(d_temp_storage, temp_storage_bytes, d_in, d_unique_out, d_counts_out, d_num_runs_out, num_items);
*
* // d_unique_out <-- [0, 2, 9, 5, 8]
* // d_counts_out <-- [1, 2, 1, 3, 1]
* // d_num_runs_out <-- [5]
*
* \endcode
*
* \tparam InputIteratorT <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
* \tparam UniqueOutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing unique output items \iterator
* \tparam LengthsOutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing output counts \iterator
* \tparam NumRunsOutputIteratorT <b>[inferred]</b> Output iterator type for recording the number of runs encountered \iterator
*/
template <
typename InputIteratorT,
typename UniqueOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
static cudaError_t Encode(
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 keys
UniqueOutputIteratorT d_unique_out, ///< [out] Pointer to the output sequence of unique keys (one key per run)
LengthsOutputIteratorT d_counts_out, ///< [out] Pointer to the output sequence of run-lengths (one count per run)
NumRunsOutputIteratorT d_num_runs_out, ///< [out] Pointer to total number of runs
int num_items, ///< [in] Total number of associated key+value pairs (i.e., the length of \p d_in_keys and \p d_in_values)
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 SelectOp; // Selection op (not used)
typedef Equality EqualityOp; // Default == operator
typedef cub::Sum ReductionOp; // Value reduction operator
// The lengths output value type
typedef typename If<(Equals<typename std::iterator_traits<LengthsOutputIteratorT>::value_type, void>::VALUE), // LengthT = (if output iterator's value type is void) ?
OffsetT, // ... then the OffsetT type,
typename std::iterator_traits<LengthsOutputIteratorT>::value_type>::Type LengthT; // ... else the output iterator's value type
// Generator type for providing 1s values for run-length reduction
typedef ConstantInputIterator<LengthT, OffsetT> LengthsInputIteratorT;
return DispatchReduceByKey<InputIteratorT, UniqueOutputIteratorT, LengthsInputIteratorT, LengthsOutputIteratorT, NumRunsOutputIteratorT, EqualityOp, ReductionOp, OffsetT>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_unique_out,
LengthsInputIteratorT((LengthT) 1),
d_counts_out,
d_num_runs_out,
EqualityOp(),
ReductionOp(),
num_items,
stream,
debug_synchronous);
}
/**
* \brief Enumerates the starting offsets and lengths of all non-trivial runs (of length > 1) of same-valued keys in the sequence \p d_in.
*
* \par
* - For the <em>i</em><sup>th</sup> non-trivial run, the run's starting offset
* and its length are written to <tt>d_offsets_out[<em>i</em>]</tt> and
* <tt>d_lengths_out[<em>i</em>]</tt>, respectively.
* - The total number of runs encountered is written to \p d_num_runs_out.
* - The <tt>==</tt> equality operator is used to determine whether values are equivalent
* - \devicestorage
*
* \par Performance
*
* \par Snippet
* The code snippet below illustrates the identification of non-trivial runs within a sequence of \p int values.
* \par
* \code
* #include <cub/cub.cuh> // or equivalently <cub/device/device_run_length_encode.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_offsets_out; // e.g., [ , , , , , , , ]
* int *d_lengths_out; // e.g., [ , , , , , , , ]
* int *d_num_runs_out; // e.g., [ ]
* ...
*
* // Determine temporary device storage requirements
* void *d_temp_storage = NULL;
* size_t temp_storage_bytes = 0;
* cub::DeviceRunLengthEncode::NonTrivialRuns(d_temp_storage, temp_storage_bytes, d_in, d_offsets_out, d_lengths_out, d_num_runs_out, num_items);
*
* // Allocate temporary storage
* cudaMalloc(&d_temp_storage, temp_storage_bytes);
*
* // Run encoding
* cub::DeviceRunLengthEncode::NonTrivialRuns(d_temp_storage, temp_storage_bytes, d_in, d_offsets_out, d_lengths_out, d_num_runs_out, num_items);
*
* // d_offsets_out <-- [1, 4]
* // d_lengths_out <-- [2, 3]
* // d_num_runs_out <-- [2]
*
* \endcode
*
* \tparam InputIteratorT <b>[inferred]</b> Random-access input iterator type for reading input items \iterator
* \tparam OffsetsOutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing run-offset values \iterator
* \tparam LengthsOutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing run-length values \iterator
* \tparam NumRunsOutputIteratorT <b>[inferred]</b> Output iterator type for recording the number of runs encountered \iterator
*/
template <
typename InputIteratorT,
typename OffsetsOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__
static cudaError_t NonTrivialRuns(
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 input sequence of data items
OffsetsOutputIteratorT d_offsets_out, ///< [out] Pointer to output sequence of run-offsets (one offset per non-trivial run)
LengthsOutputIteratorT d_lengths_out, ///< [out] Pointer to output sequence of run-lengths (one count per non-trivial run)
NumRunsOutputIteratorT d_num_runs_out, ///< [out] Pointer to total number of runs (i.e., length of \p d_offsets_out)
int num_items, ///< [in] Total number of associated key+value pairs (i.e., the length of \p d_in_keys and \p d_in_values)
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 Equality EqualityOp; // Default == operator
return DeviceRleDispatch<InputIteratorT, OffsetsOutputIteratorT, LengthsOutputIteratorT, NumRunsOutputIteratorT, EqualityOp, OffsetT>::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_in,
d_offsets_out,
d_lengths_out,
d_num_runs_out,
EqualityOp(),
num_items,
stream,
debug_synchronous);
}
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)