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lammps/lib/kokkos/TPL/cub/block/specializations/block_histogram_sort.cuh
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block_histogram_sort.cuh
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/******************************************************************************
* 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
* The cub::BlockHistogramSort class provides sorting-based methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
*/
#pragma once
#include "../../block/block_radix_sort.cuh"
#include "../../block/block_discontinuity.cuh"
#include "../../util_namespace.cuh"
/// Optional outer namespace(s)
CUB_NS_PREFIX
/// CUB namespace
namespace cub {
/**
* \brief The BlockHistogramSort class provides sorting-based methods for constructing block-wide histograms from data samples partitioned across a CUDA thread block.
*/
template <
typename T,
int BLOCK_THREADS,
int ITEMS_PER_THREAD,
int BINS>
struct BlockHistogramSort
{
// Parameterize BlockRadixSort type for our thread block
typedef BlockRadixSort<T, BLOCK_THREADS, ITEMS_PER_THREAD> BlockRadixSortT;
// Parameterize BlockDiscontinuity type for our thread block
typedef BlockDiscontinuity<T, BLOCK_THREADS> BlockDiscontinuityT;
// Shared memory
union _TempStorage
{
// Storage for sorting bin values
typename BlockRadixSortT::TempStorage sort;
struct
{
// Storage for detecting discontinuities in the tile of sorted bin values
typename BlockDiscontinuityT::TempStorage flag;
// Storage for noting begin/end offsets of bin runs in the tile of sorted bin values
unsigned int run_begin[BINS];
unsigned int run_end[BINS];
};
};
/// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
// Thread fields
_TempStorage &temp_storage;
int linear_tid;
/// Constructor
__device__ __forceinline__ BlockHistogramSort(
TempStorage &temp_storage,
int linear_tid)
:
temp_storage(temp_storage.Alias()),
linear_tid(linear_tid)
{}
// Discontinuity functor
struct DiscontinuityOp
{
// Reference to temp_storage
_TempStorage &temp_storage;
// Constructor
__device__ __forceinline__ DiscontinuityOp(_TempStorage &temp_storage) :
temp_storage(temp_storage)
{}
// Discontinuity predicate
__device__ __forceinline__ bool operator()(const T &a, const T &b, unsigned int b_index)
{
if (a != b)
{
// Note the begin/end offsets in shared storage
temp_storage.run_begin[b] = b_index;
temp_storage.run_end[a] = b_index;
return true;
}
else
{
return false;
}
}
};
// Composite data onto an existing histogram
template <
typename HistoCounter>
__device__ __forceinline__ void Composite(
T (&items)[ITEMS_PER_THREAD], ///< [in] Calling thread's input values to histogram
HistoCounter histogram[BINS]) ///< [out] Reference to shared/global memory histogram
{
enum { TILE_SIZE = BLOCK_THREADS * ITEMS_PER_THREAD };
// Sort bytes in blocked arrangement
BlockRadixSortT(temp_storage.sort, linear_tid).Sort(items);
__syncthreads();
// Initialize the shared memory's run_begin and run_end for each bin
int histo_offset = 0;
#pragma unroll
for(; histo_offset + BLOCK_THREADS <= BINS; histo_offset += BLOCK_THREADS)
{
temp_storage.run_begin[histo_offset + linear_tid] = TILE_SIZE;
temp_storage.run_end[histo_offset + linear_tid] = TILE_SIZE;
}
// Finish up with guarded initialization if necessary
if ((BINS % BLOCK_THREADS != 0) && (histo_offset + linear_tid < BINS))
{
temp_storage.run_begin[histo_offset + linear_tid] = TILE_SIZE;
temp_storage.run_end[histo_offset + linear_tid] = TILE_SIZE;
}
__syncthreads();
int flags[ITEMS_PER_THREAD]; // unused
// Compute head flags to demarcate contiguous runs of the same bin in the sorted tile
DiscontinuityOp flag_op(temp_storage);
BlockDiscontinuityT(temp_storage.flag, linear_tid).FlagHeads(flags, items, flag_op);
// Update begin for first item
if (linear_tid == 0) temp_storage.run_begin[items[0]] = 0;
__syncthreads();
// Composite into histogram
histo_offset = 0;
#pragma unroll
for(; histo_offset + BLOCK_THREADS <= BINS; histo_offset += BLOCK_THREADS)
{
int thread_offset = histo_offset + linear_tid;
HistoCounter count = temp_storage.run_end[thread_offset] - temp_storage.run_begin[thread_offset];
histogram[thread_offset] += count;
}
// Finish up with guarded composition if necessary
if ((BINS % BLOCK_THREADS != 0) && (histo_offset + linear_tid < BINS))
{
int thread_offset = histo_offset + linear_tid;
HistoCounter count = temp_storage.run_end[thread_offset] - temp_storage.run_begin[thread_offset];
histogram[thread_offset] += count;
}
}
};
} // CUB namespace
CUB_NS_POSTFIX // Optional outer namespace(s)
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