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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::DeviceRle provides device-wide, parallel operations for run-length-encoding sequences of
* data items residing within device-accessible memory.
*/
#pragma once
#include <cub/agent/agent_rle.cuh>
#include <cub/config.cuh>
#include <cub/device/dispatch/dispatch_scan.cuh>
#include <cub/grid/grid_queue.cuh>
#include <cub/thread/thread_operators.cuh>
#include <cub/util_device.cuh>
#include <cub/util_math.cuh>
#include <thrust/system/cuda/detail/core/triple_chevron_launch.h>
#include <cstdio>
#include <iterator>
#include <nv/target>
CUB_NAMESPACE_BEGIN
/******************************************************************************
* Kernel entry points
*****************************************************************************/
/**
* Select kernel entry point (multi-block)
*
* Performs functor-based selection if SelectOp functor type != NullType
* Otherwise performs flag-based selection if FlagIterator's value type != NullType
* Otherwise performs discontinuity selection (keep unique)
*
* @tparam AgentRlePolicyT
* Parameterized AgentRlePolicyT tuning policy type
*
* @tparam InputIteratorT
* Random-access input iterator type for reading input items \iterator
*
* @tparam OffsetsOutputIteratorT
* Random-access output iterator type for writing run-offset values \iterator
*
* @tparam LengthsOutputIteratorT
* Random-access output iterator type for writing run-length values \iterator
*
* @tparam NumRunsOutputIteratorT
* Output iterator type for recording the number of runs encountered \iterator
*
* @tparam ScanTileStateT
* Tile status interface type
*
* @tparam EqualityOpT
* T equality operator type
*
* @tparam OffsetT
* Signed integer type for global offsets
*
* @param d_in
* Pointer to input sequence of data items
*
* @param d_offsets_out
* Pointer to output sequence of run-offsets
*
* @param d_lengths_out
* Pointer to output sequence of run-lengths
*
* @param d_num_runs_out
* Pointer to total number of runs (i.e., length of `d_offsets_out`)
*
* @param tile_status
* Tile status interface
*
* @param equality_op
* Equality operator for input items
*
* @param num_items
* Total number of input items (i.e., length of `d_in`)
*
* @param num_tiles
* Total number of tiles for the entire problem
*/
template <typename ChainedPolicyT,
typename InputIteratorT,
typename OffsetsOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT,
typename ScanTileStateT,
typename EqualityOpT,
typename OffsetT>
__launch_bounds__(int(ChainedPolicyT::ActivePolicy::RleSweepPolicyT::BLOCK_THREADS)) __global__
void DeviceRleSweepKernel(InputIteratorT d_in,
OffsetsOutputIteratorT d_offsets_out,
LengthsOutputIteratorT d_lengths_out,
NumRunsOutputIteratorT d_num_runs_out,
ScanTileStateT tile_status,
EqualityOpT equality_op,
OffsetT num_items,
int num_tiles)
{
using AgentRlePolicyT = typename ChainedPolicyT::ActivePolicy::RleSweepPolicyT;
// Thread block type for selecting data from input tiles
using AgentRleT = AgentRle<AgentRlePolicyT,
InputIteratorT,
OffsetsOutputIteratorT,
LengthsOutputIteratorT,
EqualityOpT,
OffsetT>;
// Shared memory for AgentRle
__shared__ typename AgentRleT::TempStorage temp_storage;
// Process tiles
AgentRleT(temp_storage, d_in, d_offsets_out, d_lengths_out, equality_op, num_items)
.ConsumeRange(num_tiles, tile_status, d_num_runs_out);
}
/******************************************************************************
* Dispatch
******************************************************************************/
namespace detail
{
template <class T>
struct device_rle_policy_hub
{
/// SM35
struct Policy350 : ChainedPolicy<350, Policy350, Policy350>
{
enum
{
NOMINAL_4B_ITEMS_PER_THREAD = 15,
ITEMS_PER_THREAD = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD,
CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
};
using RleSweepPolicyT =
AgentRlePolicy<96,
ITEMS_PER_THREAD,
BLOCK_LOAD_DIRECT,
LOAD_LDG,
true,
BLOCK_SCAN_WARP_SCANS,
detail::default_reduce_by_key_delay_constructor_t<int, int>>;
};
using MaxPolicy = Policy350;
};
} // namespace detail
/**
* Utility class for dispatching the appropriately-tuned kernels for DeviceRle
*
* @tparam InputIteratorT
* Random-access input iterator type for reading input items \iterator
*
* @tparam OffsetsOutputIteratorT
* Random-access output iterator type for writing run-offset values \iterator
*
* @tparam LengthsOutputIteratorT
* Random-access output iterator type for writing run-length values \iterator
*
* @tparam NumRunsOutputIteratorT
* Output iterator type for recording the number of runs encountered \iterator
*
* @tparam EqualityOpT
* T equality operator type
*
* @tparam OffsetT
* Signed integer type for global offsets
*
* @tparam SelectedPolicy
* Implementation detail, do not specify directly, requirements on the
* content of this type are subject to breaking change.
*/
template <typename InputIteratorT,
typename OffsetsOutputIteratorT,
typename LengthsOutputIteratorT,
typename NumRunsOutputIteratorT,
typename EqualityOpT,
typename OffsetT,
typename SelectedPolicy =
detail::device_rle_policy_hub<cub::detail::value_t<InputIteratorT>>>
struct DeviceRleDispatch
{
/******************************************************************************
* Types and constants
******************************************************************************/
// The lengths output value type
using LengthT = cub::detail::non_void_value_t<LengthsOutputIteratorT, OffsetT>;
enum
{
INIT_KERNEL_THREADS = 128,
};
// Tile status descriptor interface type
using ScanTileStateT = ReduceByKeyScanTileState<LengthT, OffsetT>;
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;
EqualityOpT equality_op;
OffsetT num_items;
cudaStream_t stream;
CUB_RUNTIME_FUNCTION __forceinline__ DeviceRleDispatch(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,
EqualityOpT equality_op,
OffsetT num_items,
cudaStream_t stream)
: d_temp_storage(d_temp_storage)
, temp_storage_bytes(temp_storage_bytes)
, d_in(d_in)
, d_offsets_out(d_offsets_out)
, d_lengths_out(d_lengths_out)
, d_num_runs_out(d_num_runs_out)
, equality_op(equality_op)
, num_items(num_items)
, stream(stream)
{}
/******************************************************************************
* Dispatch entrypoints
******************************************************************************/
/**
* Internal dispatch routine for computing a device-wide run-length-encode using the
* specified kernel functions.
*
* @tparam DeviceScanInitKernelPtr
* Function type of cub::DeviceScanInitKernel
*
* @tparam DeviceRleSweepKernelPtr
* Function type of cub::DeviceRleSweepKernelPtr
*
* @param d_temp_storage
* Device-accessible allocation of temporary storage.
* When NULL, the required allocation size is written to
* `temp_storage_bytes` and no work is done.
*
* @param temp_storage_bytes
* Reference to size in bytes of `d_temp_storage` allocation
*
* @param d_in
* Pointer to the input sequence of data items
*
* @param d_offsets_out
* Pointer to the output sequence of run-offsets
*
* @param d_lengths_out
* Pointer to the output sequence of run-lengths
*
* @param d_num_runs_out
* Pointer to the total number of runs encountered (i.e., length of `d_offsets_out`)
*
* @param equality_op
* Equality operator for input items
*
* @param num_items
* Total number of input items (i.e., length of `d_in`)
*
* @param stream
* CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
*
* @param ptx_version
* PTX version of dispatch kernels
*
* @param device_scan_init_kernel
* Kernel function pointer to parameterization of cub::DeviceScanInitKernel
*
* @param device_rle_sweep_kernel
* Kernel function pointer to parameterization of cub::DeviceRleSweepKernel
*/
template <typename ActivePolicyT, typename DeviceScanInitKernelPtr, typename DeviceRleSweepKernelPtr>
CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t
Invoke(DeviceScanInitKernelPtr device_scan_init_kernel,
DeviceRleSweepKernelPtr device_rle_sweep_kernel)
{
cudaError error = cudaSuccess;
const int block_threads = ActivePolicyT::RleSweepPolicyT::BLOCK_THREADS;
const int items_per_thread = ActivePolicyT::RleSweepPolicyT::ITEMS_PER_THREAD;
do
{
// Get device ordinal
int device_ordinal;
if (CubDebug(error = cudaGetDevice(&device_ordinal)))
break;
// Number of input tiles
int tile_size = block_threads * items_per_thread;
int num_tiles = static_cast<int>(cub::DivideAndRoundUp(num_items, tile_size));
// Specify temporary storage allocation requirements
size_t allocation_sizes[1];
if (CubDebug(error = ScanTileStateT::AllocationSize(num_tiles, allocation_sizes[0])))
{
break; // bytes needed for tile status descriptors
}
// Compute allocation pointers into the single storage blob (or compute the necessary size of
// the blob)
void *allocations[1] = {};
if (CubDebug(
error =
AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes)))
{
break;
}
if (d_temp_storage == nullptr)
{
// Return if the caller is simply requesting the size of the storage allocation
break;
}
// Construct the tile status interface
ScanTileStateT tile_status;
if (CubDebug(error = tile_status.Init(num_tiles, allocations[0], allocation_sizes[0])))
{
break;
}
// Log device_scan_init_kernel configuration
int init_grid_size = CUB_MAX(1, cub::DivideAndRoundUp(num_tiles, INIT_KERNEL_THREADS));
#ifdef CUB_DETAIL_DEBUG_ENABLE_LOG
_CubLog("Invoking device_scan_init_kernel<<<%d, %d, 0, %lld>>>()\n",
init_grid_size,
INIT_KERNEL_THREADS,
(long long)stream);
#endif
// Invoke device_scan_init_kernel to initialize tile descriptors and queue descriptors
THRUST_NS_QUALIFIER::cuda_cub::launcher::triple_chevron(init_grid_size,
INIT_KERNEL_THREADS,
0,
stream)
.doit(device_scan_init_kernel, tile_status, num_tiles, d_num_runs_out);
// Check for failure to launch
if (CubDebug(error = cudaPeekAtLastError()))
{
break;
}
// Sync the stream if specified to flush runtime errors
error = detail::DebugSyncStream(stream);
if (CubDebug(error))
{
break;
}
// Return if empty problem
if (num_items == 0)
{
break;
}
// Get SM occupancy for device_rle_sweep_kernel
int device_rle_kernel_sm_occupancy;
if (CubDebug(error = MaxSmOccupancy(device_rle_kernel_sm_occupancy, // out
device_rle_sweep_kernel,
block_threads)))
{
break;
}
// Get max x-dimension of grid
int max_dim_x;
if (CubDebug(
error = cudaDeviceGetAttribute(&max_dim_x, cudaDevAttrMaxGridDimX, device_ordinal)))
{
break;
}
// Get grid size for scanning tiles
dim3 scan_grid_size;
scan_grid_size.z = 1;
scan_grid_size.y = cub::DivideAndRoundUp(num_tiles, max_dim_x);
scan_grid_size.x = CUB_MIN(num_tiles, max_dim_x);
// Log device_rle_sweep_kernel configuration
#ifdef CUB_DETAIL_DEBUG_ENABLE_LOG
_CubLog("Invoking device_rle_sweep_kernel<<<{%d,%d,%d}, %d, 0, %lld>>>(), %d items per "
"thread, %d SM occupancy\n",
scan_grid_size.x,
scan_grid_size.y,
scan_grid_size.z,
block_threads,
(long long)stream,
items_per_thread,
device_rle_kernel_sm_occupancy);
#endif
// Invoke device_rle_sweep_kernel
THRUST_NS_QUALIFIER::cuda_cub::launcher::triple_chevron(scan_grid_size,
block_threads,
0,
stream)
.doit(device_rle_sweep_kernel,
d_in,
d_offsets_out,
d_lengths_out,
d_num_runs_out,
tile_status,
equality_op,
num_items,
num_tiles);
// Check for failure to launch
if (CubDebug(error = cudaPeekAtLastError()))
{
break;
}
// Sync the stream if specified to flush runtime errors
error = detail::DebugSyncStream(stream);
if (CubDebug(error))
{
break;
}
} while (0);
return error;
}
template <class ActivePolicyT>
CUB_RUNTIME_FUNCTION __forceinline__ cudaError_t Invoke()
{
using MaxPolicyT = typename SelectedPolicy::MaxPolicy;
return Invoke<ActivePolicyT>(DeviceCompactInitKernel<ScanTileStateT, NumRunsOutputIteratorT>,
DeviceRleSweepKernel<MaxPolicyT,
InputIteratorT,
OffsetsOutputIteratorT,
LengthsOutputIteratorT,
NumRunsOutputIteratorT,
ScanTileStateT,
EqualityOpT,
OffsetT>);
}
/**
* Internal dispatch routine
*
* @param d_temp_storage
* Device-accessible allocation of temporary storage.
* When NULL, the required allocation size is written to
* `temp_storage_bytes` and no work is done.
*
* @param temp_storage_bytes
* Reference to size in bytes of `d_temp_storage` allocation
*
* @param d_in
* Pointer to input sequence of data items
*
* @param d_offsets_out
* Pointer to output sequence of run-offsets
*
* @param d_lengths_out
* Pointer to output sequence of run-lengths
*
* @param d_num_runs_out
* Pointer to total number of runs (i.e., length of `d_offsets_out`)
*
* @param equality_op
* Equality operator for input items
*
* @param num_items
* Total number of input items (i.e., length of `d_in`)
*
* @param stream
* <b>[optional]</b> CUDA stream to launch kernels within.
* Default is stream<sub>0</sub>.
*/
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Dispatch(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,
EqualityOpT equality_op,
OffsetT num_items,
cudaStream_t stream)
{
using MaxPolicyT = typename SelectedPolicy::MaxPolicy;
cudaError error = cudaSuccess;
do
{
// Get PTX version
int ptx_version = 0;
if (CubDebug(error = PtxVersion(ptx_version)))
{
break;
}
DeviceRleDispatch dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
d_offsets_out,
d_lengths_out,
d_num_runs_out,
equality_op,
num_items,
stream);
// Dispatch
if (CubDebug(error = MaxPolicyT::Invoke(ptx_version, dispatch)))
{
break;
}
} while (0);
return error;
}
CUB_RUNTIME_FUNCTION __forceinline__ static cudaError_t
Dispatch(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,
EqualityOpT equality_op,
OffsetT num_items,
cudaStream_t stream,
bool debug_synchronous)
{
CUB_DETAIL_RUNTIME_DEBUG_SYNC_USAGE_LOG
return Dispatch(d_temp_storage,
temp_storage_bytes,
d_in,
d_offsets_out,
d_lengths_out,
d_num_runs_out,
equality_op,
num_items,
stream);
}
};
CUB_NAMESPACE_END
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