Homec4science
Diffusion lammps (lammm-master) Browse

lammps/lib/kokkos/TPL/cub/device/device_reorder.cuh
895ca766eeeclammm-master

Last Change
Raw File

device_reorder.cuh
View Options

/******************************************************************************
* Copyright (c) 2011, Duane Merrill. All rights reserved.
* Copyright (c) 2011-2013, 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::DeviceReorder provides device-wide operations for partitioning and filtering lists of items residing within global memory.
*/
#pragma once
#include <stdio.h>
#include <iterator>
#include "device_scan.cuh"
#include "block/block_partition_tiles.cuh"
#include "../grid/grid_queue.cuh"
#include "../util_debug.cuh"
#include "../util_device.cuh"
#include "../util_vector.cuh"
#include "../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/******************************************************************************
* Kernel entry points
*****************************************************************************/
#ifndef DOXYGEN_SHOULD_SKIP_THIS // Do not document
/**
* Partition kernel entry point (multi-block)
*/
template <
typename BlockPartitionTilesPolicy, ///< Tuning policy for cub::BlockPartitionTiles abstraction
typename InputIteratorRA, ///< Random-access iterator type for input (may be a simple pointer type)
typename OutputIteratorRA, ///< Random-access iterator type for output (may be a simple pointer type)
typename LengthOutputIterator, ///< Output iterator type for recording the length of the first partition (may be a simple pointer type)
typename PredicateOp, ///< Unary predicate operator indicating membership in the first partition type having member <tt>bool operator()(const T &val)</tt>
typename SizeT> ///< Integer type used for global array indexing
__launch_bounds__ (int(BlockPartitionTilesPolicy::BLOCK_THREADS))
__global__ void PartitionKernel(
InputIteratorRA d_in, ///< Input data
OutputIteratorRA d_out, ///< Output data
LengthOutputIterator d_partition_length, ///< Number of items in the first partition
ScanTileDescriptor<PartitionScanTuple<SizeT, BlockPartitionTilesPolicy::PARTITOINS> > *d_tile_status, ///< Global list of tile status
PredicateOp pred_op, ///< Unary predicate operator indicating membership in the first partition
SizeT num_items, ///< Total number of input items for the entire problem
int num_tiles, ///< Totla number of intut tiles for the entire problem
GridQueue<int> queue) ///< Descriptor for performing dynamic mapping of tile data to thread blocks
{
enum
{
TILE_STATUS_PADDING = PtxArchProps::WARP_THREADS,
};
typedef PartitionScanTuple<SizeT, BlockPartitionTilesPolicy::PARTITOINS> PartitionScanTuple;
// Thread block type for scanning input tiles
typedef BlockPartitionTiles<
BlockPartitionTilesPolicy,
InputIteratorRA,
OutputIteratorRA,
PredicateOp,
SizeT> BlockPartitionTilesT;
// Shared memory for BlockPartitionTiles
__shared__ typename BlockPartitionTilesT::TempStorage temp_storage;
// Process tiles
PartitionScanTuple partition_ends; // Ending offsets for partitions (one-after)
bool is_last_tile; // Whether or not this block handled the last tile (i.e., partition_ends is valid for the entire input)
BlockPartitionTilesT(temp_storage, d_in, d_out, d_tile_status + TILE_STATUS_PADDING, pred_op, num_items).ConsumeTiles(
queue,
num_tiles,
partition_ends,
is_last_tile);
// Record the length of the first partition
if (is_last_tile && (threadIdx.x == 0))
{
*d_partition_length = partition_ends.x;
}
}
#endif // DOXYGEN_SHOULD_SKIP_THIS
/******************************************************************************
* DeviceReorder
*****************************************************************************/
/**
* \addtogroup DeviceModule
* @{
*/
/**
* \brief DeviceReorder provides device-wide operations for partitioning and filtering lists of items residing within global memory
*/
struct DeviceReorder
{
#ifndef DOXYGEN_SHOULD_SKIP_THIS // Do not document
/******************************************************************************
* Constants and typedefs
******************************************************************************/
/// Generic structure for encapsulating dispatch properties. Mirrors the constants within BlockPartitionTilesPolicy.
struct KernelDispachParams
{
int block_threads;
int items_per_thread;
BlockScanAlgorithm scan_algorithm;
int tile_size;
template <typename BlockPartitionTilesPolicy>
__host__ __device__ __forceinline__
void Init()
{
block_threads = BlockPartitionTilesPolicy::BLOCK_THREADS;
items_per_thread = BlockPartitionTilesPolicy::ITEMS_PER_THREAD;
scan_algorithm = BlockPartitionTilesPolicy::SCAN_ALGORITHM;
tile_size = block_threads * items_per_thread;
}
};
/******************************************************************************
* Tuning policies
******************************************************************************/
/// Specializations of tuned policy types for different PTX architectures
template <
int PARTITIONS,
typename T,
typename SizeT,
int ARCH>
struct TunedPolicies;
/// SM35 tune
template <int PARTITIONS, typename T, typename SizeT>
struct TunedPolicies<PARTITIONS, T, SizeT, 350>
{
enum {
NOMINAL_4B_ITEMS_PER_THREAD = 16,
ITEMS_PER_THREAD = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
};
typedef BlockPartitionTilesPolicy<PARTITIONS, 128, ITEMS_PER_THREAD, LOAD_LDG, BLOCK_SCAN_RAKING_MEMOIZE> PartitionPolicy;
};
/// SM30 tune
template <int PARTITIONS, typename T, typename SizeT>
struct TunedPolicies<PARTITIONS, T, SizeT, 300>
{
enum {
NOMINAL_4B_ITEMS_PER_THREAD = 9,
ITEMS_PER_THREAD = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
};
typedef BlockPartitionTilesPolicy<PARTITIONS, 256, ITEMS_PER_THREAD, LOAD_DEFAULT, BLOCK_SCAN_RAKING_MEMOIZE> PartitionPolicy;
};
/// SM20 tune
template <int PARTITIONS, typename T, typename SizeT>
struct TunedPolicies<PARTITIONS, T, SizeT, 200>
{
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)))),
};
typedef BlockPartitionTilesPolicy<PARTITIONS, 128, ITEMS_PER_THREAD, LOAD_DEFAULT, BLOCK_SCAN_RAKING_MEMOIZE> PartitionPolicy;
};
/// SM10 tune
template <int PARTITIONS, typename T, typename SizeT>
struct TunedPolicies<PARTITIONS, T, SizeT, 100>
{
enum {
NOMINAL_4B_ITEMS_PER_THREAD = 7,
ITEMS_PER_THREAD = CUB_MIN(NOMINAL_4B_ITEMS_PER_THREAD, CUB_MAX(1, (NOMINAL_4B_ITEMS_PER_THREAD * 4 / sizeof(T)))),
};
typedef BlockPartitionTilesPolicy<PARTITIONS, 128, ITEMS_PER_THREAD, LOAD_DEFAULT, BLOCK_SCAN_RAKING> PartitionPolicy;
};
/// Tuning policy for the PTX architecture that DevicePartition operations will get dispatched to
template <int PARTITIONS, typename T, typename SizeT>
struct PtxDefaultPolicies
{
static const int PTX_TUNE_ARCH = (CUB_PTX_ARCH >= 350) ?
350 :
(CUB_PTX_ARCH >= 300) ?
300 :
(CUB_PTX_ARCH >= 200) ?
200 :
100;
// Tuned policy set for the current PTX compiler pass
typedef TunedPolicies<PARTITIONS, T, SizeT, PTX_TUNE_ARCH> PtxTunedPolicies;
// PartitionPolicy that opaquely derives from the specialization corresponding to the current PTX compiler pass
struct PartitionPolicy : PtxTunedPolicies::PartitionPolicy {};
/**
* Initialize dispatch params with the policies corresponding to the PTX assembly we will use
*/
static void InitDispatchParams(int ptx_version, KernelDispachParams &scan_dispatch_params)
{
if (ptx_version >= 350)
{
typedef TunedPolicies<PARTITIONS, T, SizeT, 350> TunedPolicies;
scan_dispatch_params.Init<typename TunedPolicies::PartitionPolicy>();
}
else if (ptx_version >= 300)
{
typedef TunedPolicies<PARTITIONS, T, SizeT, 300> TunedPolicies;
scan_dispatch_params.Init<typename TunedPolicies::PartitionPolicy>();
}
else if (ptx_version >= 200)
{
typedef TunedPolicies<PARTITIONS, T, SizeT, 200> TunedPolicies;
scan_dispatch_params.Init<typename TunedPolicies::PartitionPolicy>();
}
else
{
typedef TunedPolicies<PARTITIONS, T, SizeT, 100> TunedPolicies;
scan_dispatch_params.Init<typename TunedPolicies::PartitionPolicy>();
}
}
};
/******************************************************************************
* Utility methods
******************************************************************************/
/**
* Internal dispatch routine
*/
template <
typename ScanInitKernelPtr, ///< Function type of cub::ScanInitKernel
typename PartitionKernelPtr, ///< Function type of cub::PartitionKernel
typename InputIteratorRA, ///< Random-access iterator type for input (may be a simple pointer type)
typename OutputIteratorRA, ///< Random-access iterator type for output (may be a simple pointer type)
typename LengthOutputIterator, ///< Output iterator type for recording the length of the first partition (may be a simple pointer type)
typename PredicateOp, ///< Unary predicate operator indicating membership in the first partition type having member <tt>bool operator()(const T &val)</tt>
typename SizeT> ///< Integer type used for global array indexing
__host__ __device__ __forceinline__
static cudaError_t Dispatch(
int ptx_version, ///< [in] PTX version
void *d_temp_storage, ///< [in] %Device allocation of temporary storage. When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Size in bytes of \p d_temp_storage allocation.
ScanInitKernelPtr init_kernel, ///< [in] Kernel function pointer to parameterization of cub::PartitionInitKernel
PartitionKernelPtr partition_kernel, ///< [in] Kernel function pointer to parameterization of cub::PartitionKernel
KernelDispachParams &scan_dispatch_params, ///< [in] Dispatch parameters that match the policy that \p partition_kernel was compiled for
InputIteratorRA d_in, ///< [in] Iterator pointing to scan input
OutputIteratorRA d_out, ///< [in] Iterator pointing to scan output
LengthOutputIterator d_partition_length, ///< [out] Output iterator referencing the location where the pivot offset (i.e., the length of the first partition) is to be recorded
PredicateOp pred_op, ///< [in] Unary predicate operator indicating membership in the first partition
SizeT num_items, ///< [in] Total number of items to partition
cudaStream_t stream = 0, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool stream_synchronous = false) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. Default is \p false.
{
#ifndef CUB_RUNTIME_ENABLED
// Kernel launch not supported from this device
return CubDebug(cudaErrorNotSupported);
#else
enum
{
TILE_STATUS_PADDING = 32,
};
// Data type
typedef typename std::iterator_traits<InputIteratorRA>::value_type T;
// Scan tuple type and tile status descriptor type
typedef typename VectorHelper<SizeT, 2>::Type ScanTuple;
typedef ScanTileDescriptor<ScanTuple> ScanTileDescriptorT;
cudaError error = cudaSuccess;
do
{
// Number of input tiles
int num_tiles = (num_items + scan_dispatch_params.tile_size - 1) / scan_dispatch_params.tile_size;
// Temporary storage allocation requirements
void* allocations[2];
size_t allocation_sizes[2] =
{
(num_tiles + TILE_STATUS_PADDING) * sizeof(ScanTileDescriptorT), // bytes needed for tile status descriptors
GridQueue<int>::AllocationSize() // bytes needed for grid queue descriptor
};
// Alias temporaries (or set the necessary size of the storage allocation)
if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
// Return if the caller is simply requesting the size of the storage allocation
if (d_temp_storage == NULL)
return cudaSuccess;
// Global list of tile status
ScanTileDescriptorT *d_tile_status = (ScanTileDescriptorT*) allocations[0];
// Grid queue descriptor
GridQueue<int> queue(allocations[1]);
// Log init_kernel configuration
int init_kernel_threads = 128;
int init_grid_size = (num_tiles + init_kernel_threads - 1) / init_kernel_threads;
if (stream_synchronous) CubLog("Invoking init_kernel<<<%d, %d, 0, %lld>>>()\n", init_grid_size, init_kernel_threads, (long long) stream);
// Invoke init_kernel to initialize tile descriptors and queue descriptors
init_kernel<<<init_grid_size, init_kernel_threads, 0, stream>>>(
queue,
d_tile_status,
num_tiles);
// Sync the stream if specified
if (stream_synchronous && (CubDebug(error = SyncStream(stream)))) break;
// Get grid size for multi-block kernel
int scan_grid_size;
int multi_sm_occupancy = -1;
if (ptx_version < 200)
{
// We don't have atomics (or don't have fast ones), so just assign one
// block per tile (limited to 65K tiles)
scan_grid_size = num_tiles;
}
else
{
// We have atomics and can thus reuse blocks across multiple tiles using a queue descriptor.
// Get GPU id
int device_ordinal;
if (CubDebug(error = cudaGetDevice(&device_ordinal))) break;
// Get SM count
int sm_count;
if (CubDebug(error = cudaDeviceGetAttribute (&sm_count, cudaDevAttrMultiProcessorCount, device_ordinal))) break;
// Get a rough estimate of partition_kernel SM occupancy based upon the maximum SM occupancy of the targeted PTX architecture
multi_sm_occupancy = CUB_MIN(
ArchProps<CUB_PTX_ARCH>::MAX_SM_THREADBLOCKS,
ArchProps<CUB_PTX_ARCH>::MAX_SM_THREADS / scan_dispatch_params.block_threads);
#ifndef __CUDA_ARCH__
// We're on the host, so come up with a
Device device_props;
if (CubDebug(error = device_props.Init(device_ordinal))) break;
if (CubDebug(error = device_props.MaxSmOccupancy(
multi_sm_occupancy,
partition_kernel,
scan_dispatch_params.block_threads))) break;
#endif
// Get device occupancy for partition_kernel
int scan_occupancy = multi_sm_occupancy * sm_count;
// Get grid size for partition_kernel
scan_grid_size = (num_tiles < scan_occupancy) ?
num_tiles : // Not enough to fill the device with threadblocks
scan_occupancy; // Fill the device with threadblocks
}
// Log partition_kernel configuration
if (stream_synchronous) CubLog("Invoking partition_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy\n",
scan_grid_size, scan_dispatch_params.block_threads, (long long) stream, scan_dispatch_params.items_per_thread, multi_sm_occupancy);
// Invoke partition_kernel
partition_kernel<<<scan_grid_size, scan_dispatch_params.block_threads, 0, stream>>>(
d_in,
d_out,
d_partition_length,
d_tile_status,
pred_op,
num_items,
num_tiles,
queue);
// Sync the stream if specified
if (stream_synchronous && (CubDebug(error = SyncStream(stream)))) break;
}
while (0);
return error;
#endif // CUB_RUNTIME_ENABLED
}
/**
* Internal partition dispatch routine for using default tuning policies
*/
template <
typename PARTITIONS, ///< Number of partitions we are keeping
typename InputIteratorRA, ///< Random-access iterator type for input (may be a simple pointer type)
typename OutputIteratorRA, ///< Random-access iterator type for output (may be a simple pointer type)
typename LengthOutputIterator, ///< Output iterator type for recording the length of the first partition (may be a simple pointer type)
typename PredicateOp, ///< Unary predicate operator indicating membership in the first partition type having member <tt>bool operator()(const T &val)</tt>
typename SizeT> ///< Integer type used for global array indexing
__host__ __device__ __forceinline__
static cudaError_t Dispatch(
void *d_temp_storage, ///< [in] %Device allocation of temporary storage. When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Size in bytes of \p d_temp_storage allocation.
InputIteratorRA d_in, ///< [in] Iterator pointing to input items
OutputIteratorRA d_out, ///< [in] Iterator pointing to output items
LengthOutputIterator d_partition_length, ///< [out] Output iterator referencing the location where the pivot offset (i.e., the length of the first partition) is to be recorded
PredicateOp pred_op, ///< [in] Unary predicate operator indicating membership in the first partition
SizeT num_items, ///< [in] Total number of items to partition
cudaStream_t stream = 0, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool stream_synchronous = false) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. Default is \p false.
{
// Data type
typedef typename std::iterator_traits<InputIteratorRA>::value_type T;
// Tuning polices
typedef PtxDefaultPolicies<PARTITIONS, T, SizeT> PtxDefaultPolicies; // Wrapper of default kernel policies
typedef typename PtxDefaultPolicies::PartitionPolicy PartitionPolicy; // Partition kernel policy
cudaError error = cudaSuccess;
do
{
// Declare dispatch parameters
KernelDispachParams scan_dispatch_params;
int ptx_version;
#ifdef __CUDA_ARCH__
// We're on the device, so initialize the dispatch parameters with the PtxDefaultPolicies directly
scan_dispatch_params.Init<PartitionPolicy>();
ptx_version = CUB_PTX_ARCH;
#else
// We're on the host, so lookup and initialize the dispatch parameters with the policies that match the device's PTX version
if (CubDebug(error = PtxVersion(ptx_version))) break;
PtxDefaultPolicies::InitDispatchParams(ptx_version, scan_dispatch_params);
#endif
Dispatch(
ptx_version,
d_temp_storage,
temp_storage_bytes,
ScanInitKernel<T, SizeT>,
PartitionKernel<PartitionPolicy, InputIteratorRA, OutputIteratorRA, LengthOutputIterator, PredicateOp, SizeT>,
scan_dispatch_params,
d_in,
d_out,
d_partition_length,
pred_op,
num_items,
stream,
stream_synchronous);
if (CubDebug(error)) break;
}
while (0);
return error;
}
#endif // DOXYGEN_SHOULD_SKIP_THIS
/**
* \brief Splits a list of input items into two partitions within the given output list using the specified predicate. The relative ordering of inputs is not necessarily preserved.
*
* An item \p val is placed in the first partition if <tt>pred_op(val) == true</tt>, otherwise
* it is placed in the second partition. The offset of the partitioning pivot (equivalent to
* the total length of the first partition as well as the starting offset of the second), is
* recorded to \p d_partition_length.
*
* The length of the output referenced by \p d_out is assumed to be the same as that of \p d_in.
*
* \devicestorage
*
* \tparam InputIteratorRA <b>[inferred]</b> Random-access iterator type for input (may be a simple pointer type)
* \tparam OutputIteratorRA <b>[inferred]</b> Random-access iterator type for output (may be a simple pointer type)
* \tparam LengthOutputIterator <b>[inferred]</b> Random-access iterator type for output (may be a simple pointer type)
* \tparam PredicateOp <b>[inferred]</b> Unary predicate operator indicating membership in the first partition type having member <tt>bool operator()(const T &val)</tt>
*/
template <
typename InputIteratorRA,
typename OutputIteratorRA,
typename LengthOutputIterator,
typename PredicateOp>
__host__ __device__ __forceinline__
static cudaError_t Partition(
void *d_temp_storage, ///< [in] %Device allocation of temporary storage. When NULL, the required allocation size is returned in \p temp_storage_bytes and no work is done.
size_t &temp_storage_bytes, ///< [in,out] Size in bytes of \p d_temp_storage allocation.
InputIteratorRA d_in, ///< [in] Iterator pointing to input items
OutputIteratorRA d_out, ///< [in] Iterator pointing to output items
LengthOutputIterator d_pivot_offset, ///< [out] Output iterator referencing the location where the pivot offset is to be recorded
PredicateOp pred_op, ///< [in] Unary predicate operator indicating membership in the first partition
int num_items, ///< [in] Total number of items to partition
cudaStream_t stream = 0, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool stream_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 typename std::iterator_traits<InputIteratorRA>::value_type T;
return Dispatch(d_temp_storage, temp_storage_bytes, d_in, d_out, Sum(), T(), num_items, stream, stream_synchronous);
}
};
/** @} */ // DeviceModule
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
c4science · Help