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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::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 <iterator>
#include <stdio.h>
#include <cub/config.cuh>
#include <cub/device/dispatch/dispatch_reduce_by_key.cuh>
#include <cub/device/dispatch/dispatch_rle.cuh>
#include <cub/util_deprecated.cuh>
CUB_NAMESPACE_BEGIN
/**
* @brief DeviceRunLengthEncode provides device-wide, parallel operations for
* demarcating "runs" of same-valued items within a sequence residing
* within device-accessible memory. 
* @ingroup SingleModule
*
* @par Overview
* A <a href="http://en.wikipedia.org/wiki/Run-length_encoding">*run-length encoding*</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 `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 *i*<sup>th</sup> run encountered, the first key of the run and
* its length are written to `d_unique_out[i]` and `d_counts_out[i]`,
* respectively.
* - The total number of runs encountered is written to `d_num_runs_out`.
* - The `==` equality operator is used to determine whether values are
* equivalent
* - In-place operations are not supported. There must be no overlap between
* any of the provided ranges:
* - `[d_unique_out, d_unique_out + *d_num_runs_out)`
* - `[d_counts_out, d_counts_out + *d_num_runs_out)`
* - `[d_num_runs_out, d_num_runs_out + 1)`
* - `[d_in, d_in + num_items)`
* - @devicestorage
*
* @par Performance
* The following charts illustrate saturated encode performance across
* different CUDA architectures for `int32` and `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 `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
* **[inferred]** Random-access input iterator type for reading input
* items \iterator
*
* @tparam UniqueOutputIteratorT
* **[inferred]** Random-access output iterator type for writing unique
* output items \iterator
*
* @tparam LengthsOutputIteratorT
* **[inferred]** Random-access output iterator type for writing output
* counts \iterator
*
* @tparam NumRunsOutputIteratorT
* **[inferred]** Output iterator type for recording the number of runs
* encountered \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 keys
*
* @param[out] d_unique_out
* Pointer to the output sequence of unique keys (one key per run)
*
* @param[out] d_counts_out
* Pointer to the output sequence of run-lengths (one count per run)
*
* @param[out] d_num_runs_out
* Pointer to total number of runs
*
* @param[in] num_items
* Total number of associated key+value pairs (i.e., the length of
* `d_in_keys` and `d_in_values`)
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename InputIteratorT,
typename UniqueOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Encode(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
UniqueOutputIteratorT d_unique_out,
LengthsOutputIteratorT d_counts_out,
NumRunsOutputIteratorT d_num_runs_out,
int num_items,
cudaStream_t stream = 0)
{
using OffsetT = int; // Signed integer type for global offsets
using EqualityOp = Equality; // Default == operator
using ReductionOp = cub::Sum; // Value reduction operator
// The lengths output value type
using LengthT =
cub::detail::non_void_value_t<LengthsOutputIteratorT, OffsetT>;
// Generator type for providing 1s values for run-length reduction
using LengthsInputIteratorT = ConstantInputIterator<LengthT, OffsetT>;
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);
}
template <typename InputIteratorT,
typename UniqueOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Encode(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
UniqueOutputIteratorT d_unique_out,
LengthsOutputIteratorT d_counts_out,
NumRunsOutputIteratorT d_num_runs_out,
int num_items,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return Encode<InputIteratorT,
UniqueOutputIteratorT,
LengthsOutputIteratorT,
NumRunsOutputIteratorT>(d_temp_storage,
temp_storage_bytes,
d_in,
d_unique_out,
d_counts_out,
d_num_runs_out,
num_items,
stream);
}
/**
* @brief Enumerates the starting offsets and lengths of all non-trivial runs
* (of `length > 1`) of same-valued keys in the sequence `d_in`.
*
* @par
* - For the *i*<sup>th</sup> non-trivial run, the run's starting offset and
* its length are written to `d_offsets_out[i]` and `d_lengths_out[i]`,
* respectively.
* - The total number of runs encountered is written to `d_num_runs_out`.
* - The `==` equality operator is used to determine whether values are
* equivalent
* - In-place operations are not supported. There must be no overlap between
* any of the provided ranges:
* - `[d_offsets_out, d_offsets_out + *d_num_runs_out)`
* - `[d_lengths_out, d_lengths_out + *d_num_runs_out)`
* - `[d_num_runs_out, d_num_runs_out + 1)`
* - `[d_in, d_in + num_items)`
* - @devicestorage
*
* @par Performance
*
* @par Snippet
* The code snippet below illustrates the identification of non-trivial runs
* within a sequence of `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
* **[inferred]** Random-access input iterator type for reading input
* items \iterator
*
* @tparam OffsetsOutputIteratorT
* **[inferred]** Random-access output iterator type for writing run-offset
* values \iterator
*
* @tparam LengthsOutputIteratorT
* **[inferred]** Random-access output iterator type for writing run-length
* values \iterator
*
* @tparam NumRunsOutputIteratorT
* **[inferred]** Output iterator type for recording the number of runs
* encountered \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 input sequence of data items
*
* @param[out] d_offsets_out
* Pointer to output sequence of run-offsets
* (one offset per non-trivial run)
*
* @param[out] d_lengths_out
* Pointer to output sequence of run-lengths
* (one count per non-trivial run)
*
* @param[out] d_num_runs_out
* Pointer to total number of runs (i.e., length of `d_offsets_out`)
*
* @param[in] num_items
* Total number of associated key+value pairs (i.e., the length of
* `d_in_keys` and `d_in_values`)
*
* @param[in] stream
* **[optional]** CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
template <typename InputIteratorT,
typename OffsetsOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT>
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
NonTrivialRuns(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
OffsetsOutputIteratorT d_offsets_out,
LengthsOutputIteratorT d_lengths_out,
NumRunsOutputIteratorT d_num_runs_out,
int num_items,
cudaStream_t stream = 0)
{
using OffsetT = int; // Signed integer type for global offsets
using EqualityOp = Equality; // 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);
}
template <typename InputIteratorT,
typename OffsetsOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT>
CUB_DETAIL_RUNTIME_DEBUG_SYNC_IS_NOT_SUPPORTED
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
NonTrivialRuns(void *d_temp_storage,
size_t &temp_storage_bytes,
InputIteratorT d_in,
OffsetsOutputIteratorT d_offsets_out,
LengthsOutputIteratorT d_lengths_out,
NumRunsOutputIteratorT d_num_runs_out,
int num_items,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return NonTrivialRuns<InputIteratorT,
OffsetsOutputIteratorT,
LengthsOutputIteratorT,
NumRunsOutputIteratorT>(d_temp_storage,
temp_storage_bytes,
d_in,
d_offsets_out,
d_lengths_out,
d_num_runs_out,
num_items,
stream);
}
};
CUB_NAMESPACE_END
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