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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.
*
******************************************************************************/
/******************************************************************************
* An implementation of segmented reduction using a load-balanced parallelization
* strategy based on the MergePath decision path.
******************************************************************************/
// Ensure printing of CUDA runtime errors to console
#define CUB_STDERR
#include <iterator>
#include <vector>
#include <string>
#include <algorithm>
#include <stdio.h>
#include <cub/cub.cuh>
#include "test_util.h"
using namespace cub;
using namespace std;
/******************************************************************************
* Globals, constants, and typedefs
******************************************************************************/
bool g_verbose = false;
int g_timing_iterations = 1;
CachingDeviceAllocator g_allocator(true);
/******************************************************************************
* Utility routines
******************************************************************************/
/**
* An pair of index offsets
*/
template <typename OffsetT>
struct IndexPair
{
OffsetT a_idx;
OffsetT b_idx;
};
/**
* Computes the begin offsets into A and B for the specified
* location (diagonal) along the merge decision path
*/
template <
int BLOCK_THREADS,
typename IteratorA,
typename IteratorB,
typename OffsetT>
__device__ __forceinline__ void ParallelMergePathSearch(
OffsetT diagonal,
IteratorA a,
IteratorB b,
IndexPair<OffsetT> begin, // Begin offsets into a and b
IndexPair<OffsetT> end, // End offsets into a and b
IndexPair<OffsetT> &intersection) // [out] Intersection offsets into a and b
{
OffsetT a_split_min = CUB_MAX(diagonal - end.b_idx, begin.a_idx);
OffsetT a_split_max = CUB_MIN(diagonal, end.a_idx);
while (a_split_min < a_split_max)
{
OffsetT a_distance = a_split_max - a_split_min;
OffsetT a_slice = (a_distance + BLOCK_THREADS - 1) >> Log2<BLOCK_THREADS>::VALUE;
OffsetT a_split_pivot = CUB_MIN(a_split_min + (threadIdx.x * a_slice), end.a_idx - 1);
int move_up = (a[a_split_pivot] <= b[diagonal - a_split_pivot - 1]);
int num_up = __syncthreads_count(move_up);
/*
_CubLog("a_split_min(%d), a_split_max(%d) a_distance(%d), a_slice(%d), a_split_pivot(%d), move_up(%d), num_up(%d), a_begin(%d), a_end(%d)
",
a_split_min, a_split_max, a_distance, a_slice, a_split_pivot, move_up, num_up, a_begin, a_end);
*/
a_split_max = CUB_MIN(num_up * a_slice, end.a_idx);
a_split_min = CUB_MAX(a_split_max - a_slice, begin.a_idx) + 1;
}
intersection.a_idx = CUB_MIN(a_split_min, end.a_idx);
intersection.b_idx = CUB_MIN(diagonal - a_split_min, end.b_idx);
}
/**
* Computes the begin offsets into A and B for the specified
* location (diagonal) along the merge decision path
*/
template <
typename IteratorA,
typename IteratorB,
typename OffsetT>
__device__ __forceinline__ void MergePathSearch(
OffsetT diagonal,
IteratorA a,
IteratorB b,
IndexPair<OffsetT> begin, // Begin offsets into a and b
IndexPair<OffsetT> end, // End offsets into a and b
IndexPair<OffsetT> &intersection) // [out] Intersection offsets into a and b
{
OffsetT split_min = CUB_MAX(diagonal - end.b_idx, begin.a_idx);
OffsetT split_max = CUB_MIN(diagonal, end.a_idx);
while (split_min < split_max)
{
OffsetT split_pivot = (split_min + split_max) >> 1;
if (a[split_pivot] <= b[diagonal - split_pivot - 1])
{
// Move candidate split range up A, down B
split_min = split_pivot + 1;
}
else
{
// Move candidate split range up B, down A
split_max = split_pivot;
}
}
intersection.a_idx = CUB_MIN(split_min, end.a_idx);
intersection.b_idx = CUB_MIN(diagonal - split_min, end.b_idx);
}
/******************************************************************************
* Tuning policy types
******************************************************************************/
/**
* Parameterizable tuning policy type for BlockSegReduceRegion
*/
template <
int _BLOCK_THREADS, ///< Threads per thread block
int _ITEMS_PER_THREAD, ///< Items per thread (per tile of input)
bool _USE_SMEM_SEGMENT_CACHE, ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
bool _USE_SMEM_VALUE_CACHE, ///< Whether or not to cache incoming values in shared memory before reducing each tile
CacheLoadModifier _LOAD_MODIFIER_SEGMENTS, ///< Cache load modifier for reading segment offsets
CacheLoadModifier _LOAD_MODIFIER_VALUES, ///< Cache load modifier for reading values
BlockReduceAlgorithm _REDUCE_ALGORITHM, ///< The BlockReduce algorithm to use
BlockScanAlgorithm _SCAN_ALGORITHM> ///< The BlockScan algorithm to use
struct BlockSegReduceRegionPolicy
{
enum
{
BLOCK_THREADS = _BLOCK_THREADS, ///< Threads per thread block
ITEMS_PER_THREAD = _ITEMS_PER_THREAD, ///< Items per thread (per tile of input)
USE_SMEM_SEGMENT_CACHE = _USE_SMEM_SEGMENT_CACHE, ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
USE_SMEM_VALUE_CACHE = _USE_SMEM_VALUE_CACHE, ///< Whether or not to cache incoming upcoming values in shared memory before reducing each tile
};
static const CacheLoadModifier LOAD_MODIFIER_SEGMENTS = _LOAD_MODIFIER_SEGMENTS; ///< Cache load modifier for reading segment offsets
static const CacheLoadModifier LOAD_MODIFIER_VALUES = _LOAD_MODIFIER_VALUES; ///< Cache load modifier for reading values
static const BlockReduceAlgorithm REDUCE_ALGORITHM = _REDUCE_ALGORITHM; ///< The BlockReduce algorithm to use
static const BlockScanAlgorithm SCAN_ALGORITHM = _SCAN_ALGORITHM; ///< The BlockScan algorithm to use
};
/******************************************************************************
* Persistent thread block types
******************************************************************************/
/**
* \brief BlockSegReduceTiles implements a stateful abstraction of CUDA thread blocks for participating in device-wide segmented reduction.
*/
template <
typename BlockSegReduceRegionPolicy, ///< Parameterized BlockSegReduceRegionPolicy tuning policy
typename SegmentOffsetIterator, ///< Random-access input iterator type for reading segment end-offsets
typename ValueIterator, ///< Random-access input iterator type for reading values
typename OutputIteratorT, ///< Random-access output iterator type for writing segment reductions
typename ReductionOp, ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
typename OffsetT> ///< Signed integer type for global offsets
struct BlockSegReduceRegion
{
//---------------------------------------------------------------------
// Types and constants
//---------------------------------------------------------------------
// Constants
enum
{
BLOCK_THREADS = BlockSegReduceRegionPolicy::BLOCK_THREADS,
ITEMS_PER_THREAD = BlockSegReduceRegionPolicy::ITEMS_PER_THREAD,
TILE_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD, /// Number of work items to be processed per tile
USE_SMEM_SEGMENT_CACHE = BlockSegReduceRegionPolicy::USE_SMEM_SEGMENT_CACHE, ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
USE_SMEM_VALUE_CACHE = BlockSegReduceRegionPolicy::USE_SMEM_VALUE_CACHE, ///< Whether or not to cache incoming upcoming values in shared memory before reducing each tile
SMEM_SEGMENT_CACHE_ITEMS = USE_SMEM_SEGMENT_CACHE ? TILE_ITEMS : 1,
SMEM_VALUE_CACHE_ITEMS = USE_SMEM_VALUE_CACHE ? TILE_ITEMS : 1,
};
// Segment offset type
typedef typename std::iterator_traits<SegmentOffsetIterator>::value_type SegmentOffset;
// Value type
typedef typename std::iterator_traits<ValueIterator>::value_type Value;
// Counting iterator type
typedef CountingInputIterator<SegmentOffsetT, OffsetT> CountingIterator;
// Segment offsets iterator wrapper type
typedef typename If<(IsPointer<SegmentOffsetIterator>::VALUE),
CacheModifiedInputIterator<BlockSegReduceRegionPolicy::LOAD_MODIFIER_SEGMENTS, SegmentOffsetT, OffsetT>, // Wrap the native input pointer with CacheModifiedInputIterator
SegmentOffsetIterator>::Type // Directly use the supplied input iterator type
WrappedSegmentOffsetIterator;
// Values iterator wrapper type
typedef typename If<(IsPointer<ValueIterator>::VALUE),
CacheModifiedInputIterator<BlockSegReduceRegionPolicy::LOAD_MODIFIER_VALUES, Value, OffsetT>, // Wrap the native input pointer with CacheModifiedInputIterator
ValueIterator>::Type // Directly use the supplied input iterator type
WrappedValueIterator;
// Tail flag type for marking segment discontinuities
typedef int TailFlag;
// Reduce-by-key data type tuple (segment-ID, value)
typedef KeyValuePair<OffsetT, Value> KeyValuePair;
// Index pair data type
typedef IndexPair<OffsetT> IndexPair;
// BlockScan scan operator for reduction-by-segment
typedef ReduceByKeyOp<ReductionOp> ReduceByKeyOp;
// Stateful BlockScan prefix callback type for managing a running total while scanning consecutive tiles
typedef RunningBlockPrefixCallbackOp<
KeyValuePair,
ReduceByKeyOp>
RunningPrefixCallbackOp;
// Parameterized BlockShift type for exchanging index pairs
typedef BlockShift<
IndexPair,
BLOCK_THREADS>
BlockShift;
// Parameterized BlockReduce type for block-wide reduction
typedef BlockReduce<
Value,
BLOCK_THREADS,
BlockSegReduceRegionPolicy::REDUCE_ALGORITHM>
BlockReduce;
// Parameterized BlockScan type for block-wide reduce-value-by-key
typedef BlockScan<
KeyValuePair,
BLOCK_THREADS,
BlockSegReduceRegionPolicy::SCAN_ALGORITHM>
BlockScan;
// Shared memory type for this thread block
struct _TempStorage
{
union
{
// Smem needed for BlockScan
typename BlockScan::TempStorage scan;
// Smem needed for BlockReduce
typename BlockReduce::TempStorage reduce;
struct
{
// Smem needed for communicating start/end indices between threads for a given work tile
typename BlockShift::TempStorage shift;
// Smem needed for caching segment end-offsets
SegmentOffset cached_segment_end_offsets[SMEM_SEGMENT_CACHE_ITEMS + 1];
};
// Smem needed for caching values
Value cached_values[SMEM_VALUE_CACHE_ITEMS];
};
IndexPair block_region_idx[2]; // The starting [0] and ending [1] pairs of segment and value indices for the thread block's region
// The first partial reduction tuple scattered by this thread block
KeyValuePair first_tuple;
};
// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
//---------------------------------------------------------------------
// Thread fields
//---------------------------------------------------------------------
_TempStorage &temp_storage; ///< Reference to shared storage
WrappedSegmentOffsetIterator d_segment_end_offsets; ///< A sequence of \p num_segments segment end-offsets
WrappedValueIterator d_values; ///< A sequence of \p num_values data to reduce
OutputIteratorT d_output; ///< A sequence of \p num_segments segment totals
CountingIterator d_value_offsets; ///< A sequence of \p num_values value-offsets
IndexPair *d_block_idx;
OffsetT num_values; ///< Total number of values to reduce
OffsetT num_segments; ///< Number of segments being reduced
Value identity; ///< Identity value (for zero-length segments)
ReductionOp reduction_op; ///< Reduction operator
ReduceByKeyOp scan_op; ///< Reduce-by-key scan operator
RunningPrefixCallbackOp prefix_op; ///< Stateful running total for block-wide prefix scan of partial reduction tuples
//---------------------------------------------------------------------
// Operations
//---------------------------------------------------------------------
/**
* Constructor
*/
__device__ __forceinline__
BlockSegReduceRegion(
TempStorage &temp_storage, ///< Reference to shared storage
SegmentOffsetIterator d_segment_end_offsets, ///< A sequence of \p num_segments segment end-offsets
ValueIterator d_values, ///< A sequence of \p num_values values
OutputIteratorT d_output, ///< A sequence of \p num_segments segment totals
IndexPair *d_block_idx,
OffsetT num_values, ///< Number of values to reduce
OffsetT num_segments, ///< Number of segments being reduced
Value identity, ///< Identity value (for zero-length segments)
ReductionOp reduction_op) ///< Reduction operator
:
temp_storage(temp_storage.Alias()),
d_segment_end_offsets(d_segment_end_offsets),
d_values(d_values),
d_value_offsets(0),
d_output(d_output),
d_block_idx(d_block_idx),
num_values(num_values),
num_segments(num_segments),
identity(identity),
reduction_op(reduction_op),
scan_op(reduction_op),
prefix_op(scan_op)
{}
/**
* Fast-path single-segment tile reduction. Perform a
* simple block-wide reduction and accumulate the result into
* the running total.
*/
__device__ __forceinline__ void SingleSegmentTile(
IndexPair next_tile_idx,
IndexPair block_idx)
{
OffsetT tile_values = next_tile_idx.b_idx - block_idx.b_idx;
// Load a tile's worth of values (using identity for out-of-bounds items)
Value values[ITEMS_PER_THREAD];
LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_values + block_idx.b_idx, values, tile_values, identity);
// Barrier for smem reuse
__syncthreads();
// Reduce the tile of values and update the running total in thread-0
KeyValuePair tile_aggregate;
tile_aggregate.key = block_idx.a_idx;
tile_aggregate.value = BlockReduce(temp_storage.reduce).Reduce(values, reduction_op);
if (threadIdx.x == 0)
{
prefix_op.running_total = scan_op(prefix_op.running_total, tile_aggregate);
}
}
/**
* Fast-path empty-segment tile reduction. Write out a tile of identity
* values to output.
*/
__device__ __forceinline__ void EmptySegmentsTile(
IndexPair next_tile_idx,
IndexPair block_idx)
{
Value segment_reductions[ITEMS_PER_THREAD];
if (threadIdx.x == 0)
{
// The first segment gets the running segment total
segment_reductions[0] = prefix_op.running_total.value;
// Update the running prefix
prefix_op.running_total.value = identity;
prefix_op.running_total.key = next_tile_idx.a_idx;
}
else
{
// Remainder of segments in this tile get identity
segment_reductions[0] = identity;
}
// Remainder of segments in this tile get identity
#pragma unroll
for (int ITEM = 1; ITEM < ITEMS_PER_THREAD; ++ITEM)
segment_reductions[ITEM] = identity;
// Store reductions
OffsetT tile_segments = next_tile_idx.a_idx - block_idx.a_idx;
StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, d_output + block_idx.a_idx, segment_reductions, tile_segments);
}
/**
* Multi-segment tile reduction.
*/
template <bool FULL_TILE>
__device__ __forceinline__ void MultiSegmentTile(
IndexPair block_idx,
IndexPair thread_idx,
IndexPair next_thread_idx,
IndexPair next_tile_idx)
{
IndexPair local_thread_idx;
local_thread_idx.a_idx = thread_idx.a_idx - block_idx.a_idx;
local_thread_idx.b_idx = thread_idx.b_idx - block_idx.b_idx;
// Check if first segment end-offset is in range
bool valid_segment = FULL_TILE || (thread_idx.a_idx < next_thread_idx.a_idx);
// Check if first value offset is in range
bool valid_value = FULL_TILE || (thread_idx.b_idx < next_thread_idx.b_idx);
// Load first segment end-offset
OffsetT segment_end_offset = (valid_segment) ?
(USE_SMEM_SEGMENT_CACHE)?
temp_storage.cached_segment_end_offsets[local_thread_idx.a_idx] :
d_segment_end_offsets[thread_idx.a_idx] :
-1;
OffsetT segment_ids[ITEMS_PER_THREAD];
OffsetT value_offsets[ITEMS_PER_THREAD];
KeyValuePair first_partial;
first_partial.key = thread_idx.a_idx;
first_partial.value = identity;
// Get segment IDs and gather-offsets for values
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
segment_ids[ITEM] = -1;
value_offsets[ITEM] = -1;
// Whether or not we slide (a) right along the segment path or (b) down the value path
if (valid_segment && (!valid_value || (segment_end_offset <= thread_idx.b_idx)))
{
// Consume this segment index
segment_ids[ITEM] = thread_idx.a_idx;
thread_idx.a_idx++;
local_thread_idx.a_idx++;
valid_segment = FULL_TILE || (thread_idx.a_idx < next_thread_idx.a_idx);
// Read next segment end-offset (if valid)
if (valid_segment)
{
if (USE_SMEM_SEGMENT_CACHE)
segment_end_offset = temp_storage.cached_segment_end_offsets[local_thread_idx.a_idx];
else
segment_end_offset = d_segment_end_offsets[thread_idx.a_idx];
}
}
else if (valid_value)
{
// Consume this value index
value_offsets[ITEM] = thread_idx.b_idx;
thread_idx.b_idx++;
local_thread_idx.b_idx++;
valid_value = FULL_TILE || (thread_idx.b_idx < next_thread_idx.b_idx);
}
}
// Load values
Value values[ITEMS_PER_THREAD];
if (USE_SMEM_VALUE_CACHE)
{
// Barrier for smem reuse
__syncthreads();
OffsetT tile_values = next_tile_idx.b_idx - block_idx.b_idx;
// Load a tile's worth of values (using identity for out-of-bounds items)
LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_values + block_idx.b_idx, values, tile_values, identity);
// Store to shared
StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, temp_storage.cached_values, values, tile_values);
// Barrier for smem reuse
__syncthreads();
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
values[ITEM] = (value_offsets[ITEM] == -1) ?
identity :
temp_storage.cached_values[value_offsets[ITEM] - block_idx.b_idx];
}
}
else
{
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
values[ITEM] = (value_offsets[ITEM] == -1) ?
identity :
d_values[value_offsets[ITEM]];
}
}
// Reduce within thread segments
KeyValuePair running_total = first_partial;
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
if (segment_ids[ITEM] != -1)
{
// Consume this segment index
d_output[segment_ids[ITEM]] = running_total.value;
// _CubLog("Updating segment %d with value %lld
", segment_ids[ITEM], running_total.value)
if (first_partial.key == segment_ids[ITEM])
first_partial.value = running_total.value;
running_total.key = segment_ids[ITEM];
running_total.value = identity;
}
running_total.value = reduction_op(running_total.value, values[ITEM]);
}
/*
// Barrier for smem reuse
__syncthreads();
// Use prefix scan to reduce values by segment-id. The segment-reductions end up in items flagged as segment-tails.
KeyValuePair block_aggregate;
BlockScan(temp_storage.scan).InclusiveScan(
pairs, // Scan input
pairs, // Scan output
scan_op, // Scan operator
block_aggregate, // Block-wide total (unused)
prefix_op); // Prefix operator for seeding the block-wide scan with the running total
*/
/*
// Check if first segment end-offset is in range
bool valid_segment = (thread_idx.a_idx < next_thread_idx.a_idx);
// Check if first value offset is in range
bool valid_value = (thread_idx.b_idx < next_thread_idx.b_idx);
// Load first segment end-offset
OffsetT segment_end_offset = (valid_segment) ?
d_segment_end_offsets[thread_idx.a_idx] :
num_values; // Out of range (the last segment end-offset is one-past the last value offset)
// Load first value offset
OffsetT value_offset = (valid_value) ?
d_value_offsets[thread_idx.b_idx] :
num_values; // Out of range (one-past the last value offset)
// Assemble segment-demarcating tail flags and partial reduction tuples
TailFlag tail_flags[ITEMS_PER_THREAD];
KeyValuePair partial_reductions[ITEMS_PER_THREAD];
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ++ITEM)
{
// Default tuple and flag values
partial_reductions[ITEM].key = thread_idx.a_idx;
partial_reductions[ITEM].value = identity;
tail_flags[ITEM] = 0;
// Whether or not we slide (a) right along the segment path or (b) down the value path
if (valid_segment && (!valid_value || (segment_end_offset <= value_offset)))
{
// Consume this segment index
// Set tail flag noting the end of the segment
tail_flags[ITEM] = 1;
// Increment segment index
thread_idx.a_idx++;
// Read next segment end-offset (if valid)
if ((valid_segment = (thread_idx.a_idx < next_thread_idx.a_idx)))
segment_end_offset = d_segment_end_offsets[thread_idx.a_idx];
}
else if (valid_value)
{
// Consume this value index
// Update the tuple's value with the value at this index.
partial_reductions[ITEM].value = d_values[value_offset];
// Increment value index
thread_idx.b_idx++;
// Read next value offset (if valid)
if ((valid_value = (thread_idx.b_idx < next_thread_idx.b_idx)))
value_offset = d_value_offsets[thread_idx.b_idx];
}
}
// Use prefix scan to reduce values by segment-id. The segment-reductions end up in items flagged as segment-tails.
KeyValuePair block_aggregate;
BlockScan(temp_storage.scan).InclusiveScan(
partial_reductions, // Scan input
partial_reductions, // Scan output
scan_op, // Scan operator
block_aggregate, // Block-wide total (unused)
prefix_op); // Prefix operator for seeding the block-wide scan with the running total
// The first segment index for this region (hoist?)
OffsetT first_segment_idx = temp_storage.block_idx.a_idx[0];
// Scatter an accumulated reduction if it is the head of a valid segment
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
if (tail_flags[ITEM])
{
OffsetT segment_idx = partial_reductions[ITEM].key;
Value value = partial_reductions[ITEM].value;
// Write value reduction to corresponding segment id
d_output[segment_idx] = value;
// Save off the first value product that this thread block will scatter
if (segment_idx == first_segment_idx)
{
temp_storage.first_tuple.value = value;
}
}
}
*/
}
/**
* Have the thread block process the specified region of the MergePath decision path
*/
__device__ __forceinline__ void ProcessRegion(
OffsetT block_diagonal,
OffsetT next_block_diagonal,
KeyValuePair &first_tuple, // [Out] Valid in thread-0
KeyValuePair &last_tuple) // [Out] Valid in thread-0
{
// Thread block initialization
if (threadIdx.x < 2)
{
// Retrieve block starting and ending indices
IndexPair block_idx = {0, 0};
if (gridDim.x > 1)
{
block_idx = d_block_idx[blockIdx.x + threadIdx.x];
}
else if (threadIdx.x > 0)
{
block_idx.a_idx = num_segments;
block_idx.b_idx = num_values;
}
// Share block starting and ending indices
temp_storage.block_region_idx[threadIdx.x] = block_idx;
// Initialize the block's running prefix
if (threadIdx.x == 0)
{
prefix_op.running_total.key = block_idx.a_idx;
prefix_op.running_total.value = identity;
// Initialize the "first scattered partial reduction tuple" to the prefix tuple (in case we don't actually scatter one)
temp_storage.first_tuple = prefix_op.running_total;
}
}
// Ensure coherence of region indices
__syncthreads();
// Read block's starting indices
IndexPair block_idx = temp_storage.block_region_idx[0];
// Have the thread block iterate over the region
#pragma unroll 1
while (block_diagonal < next_block_diagonal)
{
// Read block's ending indices (hoist?)
IndexPair next_block_idx = temp_storage.block_region_idx[1];
// Clamp the per-thread search range to within one work-tile of block's current indices
IndexPair next_tile_idx;
next_tile_idx.a_idx = CUB_MIN(next_block_idx.a_idx, block_idx.a_idx + TILE_ITEMS);
next_tile_idx.b_idx = CUB_MIN(next_block_idx.b_idx, block_idx.b_idx + TILE_ITEMS);
// Have each thread search for the end-indices of its subranges within the segment and value inputs
IndexPair next_thread_idx;
if (USE_SMEM_SEGMENT_CACHE)
{
// Search in smem cache
OffsetT num_segments = next_tile_idx.a_idx - block_idx.a_idx;
// Load global
SegmentOffset segment_offsets[ITEMS_PER_THREAD];
LoadDirectStriped<BLOCK_THREADS>(threadIdx.x, d_segment_end_offsets + block_idx.a_idx, segment_offsets, num_segments, num_values);
// Store to shared
StoreDirectStriped<BLOCK_THREADS>(threadIdx.x, temp_storage.cached_segment_end_offsets, segment_offsets);
__syncthreads();
OffsetT next_thread_diagonal = block_diagonal + ((threadIdx.x + 1) * ITEMS_PER_THREAD);
MergePathSearch(
next_thread_diagonal, // Next thread diagonal
temp_storage.cached_segment_end_offsets - block_idx.a_idx, // A (segment end-offsets)
d_value_offsets, // B (value offsets)
block_idx, // Start indices into A and B
next_tile_idx, // End indices into A and B
next_thread_idx); // [out] diagonal intersection indices into A and B
}
else
{
// Search in global
OffsetT next_thread_diagonal = block_diagonal + ((threadIdx.x + 1) * ITEMS_PER_THREAD);
MergePathSearch(
next_thread_diagonal, // Next thread diagonal
d_segment_end_offsets, // A (segment end-offsets)
d_value_offsets, // B (value offsets)
block_idx, // Start indices into A and B
next_tile_idx, // End indices into A and B
next_thread_idx); // [out] diagonal intersection indices into A and B
}
// Share thread end-indices to get thread begin-indices and tile end-indices
IndexPair thread_idx;
BlockShift(temp_storage.shift).Up(
next_thread_idx, // Input item
thread_idx, // [out] Output item
block_idx, // Prefix item to be provided to <em>thread</em><sub>0</sub>
next_tile_idx); // [out] Suffix item shifted out by the <em>thread</em><sub><tt>BLOCK_THREADS-1</tt></sub> to be provided to all threads
// if (block_idx.a_idx == next_tile_idx.a_idx)
// {
// // There are no segment end-offsets in this tile. Perform a
// // simple block-wide reduction and accumulate the result into
// // the running total.
// SingleSegmentTile(next_tile_idx, block_idx);
// }
// else if (block_idx.b_idx == next_tile_idx.b_idx)
// {
// // There are no values in this tile (only empty segments).
// EmptySegmentsTile(next_tile_idx.a_idx, block_idx.a_idx);
// }
// else
if ((next_tile_idx.a_idx < num_segments) && (next_tile_idx.b_idx < num_values))
{
// Merge the tile's segment and value indices (full tile)
MultiSegmentTile<true>(block_idx, thread_idx, next_thread_idx, next_tile_idx);
}
else
{
// Merge the tile's segment and value indices (partially full tile)
MultiSegmentTile<false>(block_idx, thread_idx, next_thread_idx, next_tile_idx);
}
// Advance the block's indices in preparation for the next tile
block_idx = next_tile_idx;
// Advance to the next region in the decision path
block_diagonal += TILE_ITEMS;
// Barrier for smem reuse
__syncthreads();
}
// Get first and last tuples for the region
if (threadIdx.x == 0)
{
first_tuple = temp_storage.first_tuple;
last_tuple = prefix_op.running_total;
}
}
};
/******************************************************************************
* Tuning policy types
******************************************************************************/
/**
* Parameterizable tuning policy type for BlockSegReduceRegionByKey
*/
template <
int _BLOCK_THREADS, ///< Threads per thread block
int _ITEMS_PER_THREAD, ///< Items per thread (per tile of input)
BlockLoadAlgorithm _LOAD_ALGORITHM, ///< The BlockLoad algorithm to use
bool _LOAD_WARP_TIME_SLICING, ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)
CacheLoadModifier _LOAD_MODIFIER, ///< Cache load modifier for reading input elements
BlockScanAlgorithm _SCAN_ALGORITHM> ///< The BlockScan algorithm to use
struct BlockSegReduceRegionByKeyPolicy
{
enum
{
BLOCK_THREADS = _BLOCK_THREADS, ///< Threads per thread block
ITEMS_PER_THREAD = _ITEMS_PER_THREAD, ///< Items per thread (per tile of input)
LOAD_WARP_TIME_SLICING = _LOAD_WARP_TIME_SLICING, ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage) };
};
static const BlockLoadAlgorithm LOAD_ALGORITHM = _LOAD_ALGORITHM; ///< The BlockLoad algorithm to use
static const CacheLoadModifier LOAD_MODIFIER = _LOAD_MODIFIER; ///< Cache load modifier for reading input elements
static const BlockScanAlgorithm SCAN_ALGORITHM = _SCAN_ALGORITHM; ///< The BlockScan algorithm to use
};
/******************************************************************************
* Persistent thread block types
******************************************************************************/
/**
* \brief BlockSegReduceRegionByKey implements a stateful abstraction of CUDA thread blocks for participating in device-wide reduce-value-by-key.
*/
template <
typename BlockSegReduceRegionByKeyPolicy, ///< Parameterized BlockSegReduceRegionByKeyPolicy tuning policy
typename InputIteratorT, ///< Random-access iterator referencing key-value input tuples
typename OutputIteratorT, ///< Random-access iterator referencing segment output totals
typename ReductionOp> ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
struct BlockSegReduceRegionByKey
{
//---------------------------------------------------------------------
// Types and constants
//---------------------------------------------------------------------
// Constants
enum
{
BLOCK_THREADS = BlockSegReduceRegionByKeyPolicy::BLOCK_THREADS,
ITEMS_PER_THREAD = BlockSegReduceRegionByKeyPolicy::ITEMS_PER_THREAD,
TILE_ITEMS = BLOCK_THREADS * ITEMS_PER_THREAD,
};
// KeyValuePair input type
typedef typename std::iterator_traits<InputIteratorT>::value_type KeyValuePair;
// Signed integer type for global offsets
typedef typename KeyValuePair::Key OffsetT;
// Value type
typedef typename KeyValuePair::Value Value;
// Head flag type
typedef int HeadFlag;
// Input iterator wrapper type for loading KeyValuePair elements through cache
typedef CacheModifiedInputIterator<
BlockSegReduceRegionByKeyPolicy::LOAD_MODIFIER,
KeyValuePair,
OffsetT>
WrappedInputIteratorT;
// Parameterized BlockLoad type
typedef BlockLoad<
WrappedInputIteratorT,
BLOCK_THREADS,
ITEMS_PER_THREAD,
BlockSegReduceRegionByKeyPolicy::LOAD_ALGORITHM,
BlockSegReduceRegionByKeyPolicy::LOAD_WARP_TIME_SLICING>
BlockLoad;
// BlockScan scan operator for reduction-by-segment
typedef ReduceByKeyOp<ReductionOp> ReduceByKeyOp;
// Stateful BlockScan prefix callback type for managing a running total while scanning consecutive tiles
typedef RunningBlockPrefixCallbackOp<
KeyValuePair,
ReduceByKeyOp>
RunningPrefixCallbackOp;
// Parameterized BlockScan type for block-wide reduce-value-by-key
typedef BlockScan<
KeyValuePair,
BLOCK_THREADS,
BlockSegReduceRegionByKeyPolicy::SCAN_ALGORITHM>
BlockScan;
// Parameterized BlockDiscontinuity type for identifying key discontinuities
typedef BlockDiscontinuity<
OffsetT,
BLOCK_THREADS>
BlockDiscontinuity;
// Operator for detecting discontinuities in a list of segment identifiers.
struct NewSegmentOp
{
/// Returns true if row_b is the start of a new row
__device__ __forceinline__ bool operator()(const OffsetT& b, const OffsetT& a)
{
return (a != b);
}
};
// Shared memory type for this thread block
struct _TempStorage
{
union
{
typename BlockLoad::TempStorage load; // Smem needed for tile loading
struct {
typename BlockScan::TempStorage scan; // Smem needed for reduce-value-by-segment scan
typename BlockDiscontinuity::TempStorage discontinuity; // Smem needed for head-flagging
};
};
};
// Alias wrapper allowing storage to be unioned
struct TempStorage : Uninitialized<_TempStorage> {};
//---------------------------------------------------------------------
// Thread fields
//---------------------------------------------------------------------
_TempStorage &temp_storage; ///< Reference to shared storage
WrappedInputIteratorT d_tuple_partials; ///< A sequence of partial reduction tuples to scan
OutputIteratorT d_output; ///< A sequence of segment totals
Value identity; ///< Identity value (for zero-length segments)
ReduceByKeyOp scan_op; ///< Reduce-by-key scan operator
RunningPrefixCallbackOp prefix_op; ///< Stateful running total for block-wide prefix scan of partial reduction tuples
//---------------------------------------------------------------------
// Operations
//---------------------------------------------------------------------
/**
* Constructor
*/
__device__ __forceinline__
BlockSegReduceRegionByKey(
TempStorage &temp_storage, ///< Reference to shared storage
InputIteratorT d_tuple_partials, ///< A sequence of partial reduction tuples to scan
OutputIteratorT d_output, ///< A sequence of segment totals
Value identity, ///< Identity value (for zero-length segments)
ReductionOp reduction_op) ///< Reduction operator
:
temp_storage(temp_storage.Alias()),
d_tuple_partials(d_tuple_partials),
d_output(d_output),
identity(identity),
scan_op(reduction_op),
prefix_op(scan_op)
{}
/**
* Processes a reduce-value-by-key input tile, outputting reductions for each segment
*/
template <bool FULL_TILE>
__device__ __forceinline__
void ProcessTile(
OffsetT block_offset,
OffsetT first_segment_idx,
OffsetT last_segment_idx,
int guarded_items = TILE_ITEMS)
{
KeyValuePair partial_reductions[ITEMS_PER_THREAD];
OffsetT segment_ids[ITEMS_PER_THREAD];
HeadFlag head_flags[ITEMS_PER_THREAD];
// Load a tile of block partials from previous kernel
if (FULL_TILE)
{
// Full tile
BlockLoad(temp_storage.load).Load(d_tuple_partials + block_offset, partial_reductions);
}
else
{
KeyValuePair oob_default;
oob_default.key = last_segment_idx; // The last segment ID to be reduced
oob_default.value = identity;
// Partially-full tile
BlockLoad(temp_storage.load).Load(d_tuple_partials + block_offset, partial_reductions, guarded_items, oob_default);
}
// Barrier for shared memory reuse
__syncthreads();
// Copy the segment IDs for head-flagging
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
segment_ids[ITEM] = partial_reductions[ITEM].key;
}
// FlagT segment heads by looking for discontinuities
BlockDiscontinuity(temp_storage.discontinuity).FlagHeads(
head_flags, // [out] Head flags
segment_ids, // Segment ids
NewSegmentOp(), // Functor for detecting start of new rows
prefix_op.running_total.key); // Last segment ID from previous tile to compare with first segment ID in this tile
// Reduce-value-by-segment across partial_reductions using exclusive prefix scan
KeyValuePair block_aggregate;
BlockScan(temp_storage.scan).ExclusiveScan(
partial_reductions, // Scan input
partial_reductions, // Scan output
scan_op, // Scan operator
block_aggregate, // Block-wide total (unused)
prefix_op); // Prefix operator for seeding the block-wide scan with the running total
// Scatter an accumulated reduction if it is the head of a valid segment
#pragma unroll
for (int ITEM = 0; ITEM < ITEMS_PER_THREAD; ITEM++)
{
if (head_flags[ITEM])
{
d_output[partial_reductions[ITEM].key] = partial_reductions[ITEM].value;
}
}
}
/**
* Iterate over input tiles belonging to this thread block
*/
__device__ __forceinline__
void ProcessRegion(
OffsetT block_offset,
OffsetT block_end,
OffsetT first_segment_idx,
OffsetT last_segment_idx)
{
if (threadIdx.x == 0)
{
// Initialize running prefix to the first segment index paired with identity
prefix_op.running_total.key = first_segment_idx;
prefix_op.running_total.value = identity;
}
// Process full tiles
while (block_offset + TILE_ITEMS <= block_end)
{
ProcessTile<true>(block_offset, first_segment_idx, last_segment_idx);
__syncthreads();
block_offset += TILE_ITEMS;
}
// Process final value tile (if present)
int guarded_items = block_end - block_offset;
if (guarded_items)
{
ProcessTile<false>(block_offset, first_segment_idx, last_segment_idx, guarded_items);
}
}
};
/******************************************************************************
* Kernel entrypoints
******************************************************************************/
/**
* Segmented reduce region kernel entry point (multi-block).
*/
template <
typename SegmentOffsetIterator, ///< Random-access input iterator type for reading segment end-offsets
typename OffsetT> ///< Signed integer type for global offsets
__global__ void SegReducePartitionKernel(
SegmentOffsetIterator d_segment_end_offsets, ///< [in] A sequence of \p num_segments segment end-offsets
IndexPair<OffsetT> *d_block_idx,
int num_partition_samples,
OffsetT num_values, ///< [in] Number of values to reduce
OffsetT num_segments, ///< [in] Number of segments being reduced
GridEvenShare<OffsetT> even_share) ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
{
// Segment offset type
typedef typename std::iterator_traits<SegmentOffsetIterator>::value_type SegmentOffset;
// Counting iterator type
typedef CountingInputIterator<SegmentOffsetT, OffsetT> CountingIterator;
// Cache-modified iterator for segment end-offsets
CacheModifiedInputIterator<LOAD_LDG, SegmentOffsetT, OffsetT> d_wrapped_segment_end_offsets(d_segment_end_offsets);
// Counting iterator for value offsets
CountingIterator d_value_offsets(0);
// Initialize even-share to tell us where to start and stop our tile-processing
int partition_id = (blockDim.x * blockIdx.x) + threadIdx.x;
even_share.Init(partition_id);
// Search for block starting and ending indices
IndexPair<OffsetT> start_idx = {0, 0};
IndexPair<OffsetT> end_idx = {num_segments, num_values};
IndexPair<OffsetT> block_idx;
MergePathSearch(
even_share.block_offset, // Next thread diagonal
d_wrapped_segment_end_offsets, // A (segment end-offsets)
d_value_offsets, // B (value offsets)
start_idx, // Start indices into A and B
end_idx, // End indices into A and B
block_idx); // [out] diagonal intersection indices into A and B
// Write output
if (partition_id < num_partition_samples)
{
d_block_idx[partition_id] = block_idx;
}
}
/**
* Segmented reduce region kernel entry point (multi-block).
*/
template <
typename BlockSegReduceRegionPolicy, ///< Parameterized BlockSegReduceRegionPolicy tuning policy
typename SegmentOffsetIterator, ///< Random-access input iterator type for reading segment end-offsets
typename ValueIterator, ///< Random-access input iterator type for reading values
typename OutputIteratorT, ///< Random-access output iterator type for writing segment reductions
typename ReductionOp, ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
typename OffsetT, ///< Signed integer type for global offsets
typename Value> ///< Value type
__launch_bounds__ (BlockSegReduceRegionPolicy::BLOCK_THREADS)
__global__ void SegReduceRegionKernel(
SegmentOffsetIterator d_segment_end_offsets, ///< [in] A sequence of \p num_segments segment end-offsets
ValueIterator d_values, ///< [in] A sequence of \p num_values values
OutputIteratorT d_output, ///< [out] A sequence of \p num_segments segment totals
KeyValuePair<OffsetT, Value> *d_tuple_partials, ///< [out] A sequence of (gridDim.x * 2) partial reduction tuples
IndexPair<OffsetT> *d_block_idx,
OffsetT num_values, ///< [in] Number of values to reduce
OffsetT num_segments, ///< [in] Number of segments being reduced
Value identity, ///< [in] Identity value (for zero-length segments)
ReductionOp reduction_op, ///< [in] Reduction operator
GridEvenShare<OffsetT> even_share) ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
{
typedef KeyValuePair<OffsetT, Value> KeyValuePair;
// Specialize thread block abstraction type for reducing a range of segmented values
typedef BlockSegReduceRegion<
BlockSegReduceRegionPolicy,
SegmentOffsetIterator,
ValueIterator,
OutputIteratorT,
ReductionOp,
OffsetT>
BlockSegReduceRegion;
// Shared memory allocation
__shared__ typename BlockSegReduceRegion::TempStorage temp_storage;
// Initialize thread block even-share to tell us where to start and stop our tile-processing
even_share.BlockInit();
// Construct persistent thread block
BlockSegReduceRegion thread_block(
temp_storage,
d_segment_end_offsets,
d_values,
d_output,
d_block_idx,
num_values,
num_segments,
identity,
reduction_op);
// First and last partial reduction tuples within the range (valid in thread-0)
KeyValuePair first_tuple, last_tuple;
// Consume block's region of work
thread_block.ProcessRegion(
even_share.block_offset,
even_share.block_end,
first_tuple,
last_tuple);
if (threadIdx.x == 0)
{
if (gridDim.x > 1)
{
// Special case where the first segment written and the carry-out are for the same segment
if (first_tuple.key == last_tuple.key)
{
first_tuple.value = identity;
}
// Write the first and last partial products from this thread block so
// that they can be subsequently "fixed up" in the next kernel.
d_tuple_partials[blockIdx.x * 2] = first_tuple;
d_tuple_partials[(blockIdx.x * 2) + 1] = last_tuple;
}
}
}
/**
* Segmented reduce region kernel entry point (single-block).
*/
template <
typename BlockSegReduceRegionByKeyPolicy, ///< Parameterized BlockSegReduceRegionByKeyPolicy tuning policy
typename InputIteratorT, ///< Random-access iterator referencing key-value input tuples
typename OutputIteratorT, ///< Random-access iterator referencing segment output totals
typename ReductionOp, ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
typename OffsetT, ///< Signed integer type for global offsets
typename Value> ///< Value type
__launch_bounds__ (BlockSegReduceRegionByKeyPolicy::BLOCK_THREADS, 1)
__global__ void SegReduceRegionByKeyKernel(
InputIteratorT d_tuple_partials, ///< [in] A sequence of partial reduction tuples
OutputIteratorT d_output, ///< [out] A sequence of \p num_segments segment totals
OffsetT num_segments, ///< [in] Number of segments in the \p d_output sequence
int num_tuple_partials, ///< [in] Number of partial reduction tuples being reduced
Value identity, ///< [in] Identity value (for zero-length segments)
ReductionOp reduction_op) ///< [in] Reduction operator
{
// Specialize thread block abstraction type for reducing a range of values by key
typedef BlockSegReduceRegionByKey<
BlockSegReduceRegionByKeyPolicy,
InputIteratorT,
OutputIteratorT,
ReductionOp>
BlockSegReduceRegionByKey;
// Shared memory allocation
__shared__ typename BlockSegReduceRegionByKey::TempStorage temp_storage;
// Construct persistent thread block
BlockSegReduceRegionByKey thread_block(
temp_storage,
d_tuple_partials,
d_output,
identity,
reduction_op);
// Process input tiles
thread_block.ProcessRegion(
0, // Region start
num_tuple_partials, // Region end
0, // First segment ID
num_segments); // Last segment ID (one-past)
}
/******************************************************************************
* Dispatch
******************************************************************************/
/**
* Utility class for dispatching the appropriately-tuned kernels for DeviceReduce
*/
template <
typename ValueIterator, ///< Random-access input iterator type for reading values
typename SegmentOffsetIterator, ///< Random-access input iterator type for reading segment end-offsets
typename OutputIteratorT, ///< Random-access output iterator type for writing segment reductions
typename ReductionOp, ///< Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
typename OffsetT> ///< Signed integer type for global offsets
struct DeviceSegReduceDispatch
{
// Value type
typedef typename std::iterator_traits<ValueIterator>::value_type Value;
// Reduce-by-key data type tuple (segment-ID, value)
typedef KeyValuePair<OffsetT, Value> KeyValuePair;
// Index pair data type
typedef IndexPair<OffsetT>IndexPair;
/******************************************************************************
* Tuning policies
******************************************************************************/
/// SM35
struct Policy350
{
// ReduceRegionPolicy
typedef BlockSegReduceRegionPolicy<
128, ///< Threads per thread block
6, ///< Items per thread (per tile of input)
true, ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
false, ///< Whether or not to cache incoming values in shared memory before reducing each tile
LOAD_DEFAULT, ///< Cache load modifier for reading segment offsets
LOAD_LDG, ///< Cache load modifier for reading values
BLOCK_REDUCE_RAKING, ///< The BlockReduce algorithm to use
BLOCK_SCAN_WARP_SCANS> ///< The BlockScan algorithm to use
SegReduceRegionPolicy;
// ReduceRegionByKeyPolicy
typedef BlockSegReduceRegionByKeyPolicy<
256, ///< Threads per thread block
9, ///< Items per thread (per tile of input)
BLOCK_LOAD_DIRECT, ///< The BlockLoad algorithm to use
false, ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)
LOAD_LDG, ///< Cache load modifier for reading input elements
BLOCK_SCAN_WARP_SCANS> ///< The BlockScan algorithm to use
SegReduceRegionByKeyPolicy;
};
/// SM10
struct Policy100
{
// ReduceRegionPolicy
typedef BlockSegReduceRegionPolicy<
128, ///< Threads per thread block
3, ///< Items per thread (per tile of input)
false, ///< Whether or not to cache incoming segment offsets in shared memory before reducing each tile
false, ///< Whether or not to cache incoming values in shared memory before reducing each tile
LOAD_DEFAULT, ///< Cache load modifier for reading segment offsets
LOAD_DEFAULT, ///< Cache load modifier for reading values
BLOCK_REDUCE_RAKING, ///< The BlockReduce algorithm to use
BLOCK_SCAN_RAKING> ///< The BlockScan algorithm to use
SegReduceRegionPolicy;
// ReduceRegionByKeyPolicy
typedef BlockSegReduceRegionByKeyPolicy<
128, ///< Threads per thread block
3, ///< Items per thread (per tile of input)
BLOCK_LOAD_WARP_TRANSPOSE, ///< The BlockLoad algorithm to use
false, ///< Whether or not only one warp's worth of shared memory should be allocated and time-sliced among block-warps during any load-related data transpositions (versus each warp having its own storage)
LOAD_DEFAULT, ///< Cache load modifier for reading input elements
BLOCK_SCAN_WARP_SCANS> ///< The BlockScan algorithm to use
SegReduceRegionByKeyPolicy;
};
/******************************************************************************
* Tuning policies of current PTX compiler pass
******************************************************************************/
#if (CUB_PTX_ARCH >= 350)
typedef Policy350 PtxPolicy;
/*
#elif (CUB_PTX_ARCH >= 300)
typedef Policy300 PtxPolicy;
#elif (CUB_PTX_ARCH >= 200)
typedef Policy200 PtxPolicy;
#elif (CUB_PTX_ARCH >= 130)
typedef Policy130 PtxPolicy;
*/
#else
typedef Policy100 PtxPolicy;
#endif
// "Opaque" policies (whose parameterizations aren't reflected in the type signature)
struct PtxSegReduceRegionPolicy : PtxPolicy::SegReduceRegionPolicy {};
struct PtxSegReduceRegionByKeyPolicy : PtxPolicy::SegReduceRegionByKeyPolicy {};
/******************************************************************************
* Utilities
******************************************************************************/
/**
* Initialize kernel dispatch configurations with the policies corresponding to the PTX assembly we will use
*/
template <
typename SegReduceKernelConfig,
typename SegReduceByKeyKernelConfig>
__host__ __device__ __forceinline__
static void InitConfigs(
int ptx_version,
SegReduceKernelConfig &seg_reduce_region_config,
SegReduceByKeyKernelConfig &seg_reduce_region_by_key_config)
{
#if (CUB_PTX_ARCH > 0)
// We're on the device, so initialize the kernel dispatch configurations with the current PTX policy
seg_reduce_region_config.Init<PtxSegReduceRegionPolicy>();
seg_reduce_region_by_key_config.Init<PtxSegReduceRegionByKeyPolicy>();
#else
// We're on the host, so lookup and initialize the kernel dispatch configurations with the policies that match the device's PTX version
if (ptx_version >= 350)
{
seg_reduce_region_config.template Init<typename Policy350::SegReduceRegionPolicy>();
seg_reduce_region_by_key_config.template Init<typename Policy350::SegReduceRegionByKeyPolicy>();
}
/*
else if (ptx_version >= 300)
{
seg_reduce_region_config.template Init<typename Policy300::SegReduceRegionPolicy>();
seg_reduce_region_by_key_config.template Init<typename Policy300::SegReduceRegionByKeyPolicy>();
}
else if (ptx_version >= 200)
{
seg_reduce_region_config.template Init<typename Policy200::SegReduceRegionPolicy>();
seg_reduce_region_by_key_config.template Init<typename Policy200::SegReduceRegionByKeyPolicy>();
}
else if (ptx_version >= 130)
{
seg_reduce_region_config.template Init<typename Policy130::SegReduceRegionPolicy>();
seg_reduce_region_by_key_config.template Init<typename Policy130::SegReduceRegionByKeyPolicy>();
}
*/
else
{
seg_reduce_region_config.template Init<typename Policy100::SegReduceRegionPolicy>();
seg_reduce_region_by_key_config.template Init<typename Policy100::SegReduceRegionByKeyPolicy>();
}
#endif
}
/**
* SegReduceRegionKernel kernel dispatch configuration
*/
struct SegReduceKernelConfig
{
int block_threads;
int items_per_thread;
bool use_smem_segment_cache;
bool use_smem_value_cache;
CacheLoadModifier load_modifier_segments;
CacheLoadModifier load_modifier_values;
BlockReduceAlgorithm reduce_algorithm;
BlockScanAlgorithm scan_algorithm;
template <typename SegReduceRegionPolicy>
__host__ __device__ __forceinline__
void Init()
{
block_threads = SegReduceRegionPolicy::BLOCK_THREADS;
items_per_thread = SegReduceRegionPolicy::ITEMS_PER_THREAD;
use_smem_segment_cache = SegReduceRegionPolicy::USE_SMEM_SEGMENT_CACHE;
use_smem_value_cache = SegReduceRegionPolicy::USE_SMEM_VALUE_CACHE;
load_modifier_segments = SegReduceRegionPolicy::LOAD_MODIFIER_SEGMENTS;
load_modifier_values = SegReduceRegionPolicy::LOAD_MODIFIER_VALUES;
reduce_algorithm = SegReduceRegionPolicy::REDUCE_ALGORITHM;
scan_algorithm = SegReduceRegionPolicy::SCAN_ALGORITHM;
}
};
/**
* SegReduceRegionByKeyKernel kernel dispatch configuration
*/
struct SegReduceByKeyKernelConfig
{
int block_threads;
int items_per_thread;
BlockLoadAlgorithm load_algorithm;
bool load_warp_time_slicing;
CacheLoadModifier load_modifier;
BlockScanAlgorithm scan_algorithm;
template <typename SegReduceRegionByKeyPolicy>
__host__ __device__ __forceinline__
void Init()
{
block_threads = SegReduceRegionByKeyPolicy::BLOCK_THREADS;
items_per_thread = SegReduceRegionByKeyPolicy::ITEMS_PER_THREAD;
load_algorithm = SegReduceRegionByKeyPolicy::LOAD_ALGORITHM;
load_warp_time_slicing = SegReduceRegionByKeyPolicy::LOAD_WARP_TIME_SLICING;
load_modifier = SegReduceRegionByKeyPolicy::LOAD_MODIFIER;
scan_algorithm = SegReduceRegionByKeyPolicy::SCAN_ALGORITHM;
}
};
/******************************************************************************
* Dispatch entrypoints
******************************************************************************/
/**
* Internal dispatch routine for computing a device-wide segmented reduction.
*/
template <
typename SegReducePartitionKernelPtr,
typename SegReduceRegionKernelPtr, ///< Function type of cub::SegReduceRegionKernel
typename SegReduceRegionByKeyKernelPtr> ///< Function type of cub::SegReduceRegionByKeyKernel
__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] Reference to size in bytes of \p d_temp_storage allocation.
ValueIterator d_values, ///< [in] A sequence of \p num_values data to reduce
SegmentOffsetIterator d_segment_offsets, ///< [in] A sequence of (\p num_segments + 1) segment offsets
OutputIteratorT d_output, ///< [out] A sequence of \p num_segments segment totals
OffsetT num_values, ///< [in] Total number of values to reduce
OffsetT num_segments, ///< [in] Number of segments being reduced
Value identity, ///< [in] Identity value (for zero-length segments)
ReductionOp reduction_op, ///< [in] Reduction operator
cudaStream_t stream, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous, ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. Also causes launch configurations to be printed to the console. Default is \p false.
int sm_version, ///< [in] SM version of target device to use when computing SM occupancy
SegReducePartitionKernelPtr seg_reduce_partition_kernel, ///< [in] Kernel function pointer to parameterization of cub::SegReduceRegionKernel
SegReduceRegionKernelPtr seg_reduce_region_kernel, ///< [in] Kernel function pointer to parameterization of cub::SegReduceRegionKernel
SegReduceRegionByKeyKernelPtr seg_reduce_region_by_key_kernel, ///< [in] Kernel function pointer to parameterization of cub::SegReduceRegionByKeyKernel
SegReduceKernelConfig &seg_reduce_region_config, ///< [in] Dispatch parameters that match the policy that \p seg_reduce_region_kernel was compiled for
SegReduceByKeyKernelConfig &seg_reduce_region_by_key_config) ///< [in] Dispatch parameters that match the policy that \p seg_reduce_region_by_key_kernel was compiled for
{
#ifndef CUB_RUNTIME_ENABLED
// Kernel launch not supported from this device
return CubDebug(cudaErrorNotSupported );
#else
cudaError error = cudaSuccess;
do
{
// Dispatch two kernels: (1) a multi-block segmented reduction
// to reduce regions by block, and (2) a single-block reduce-by-key kernel
// to "fix up" segments spanning more than one region.
// Tile size of seg_reduce_region_kernel
int tile_size = seg_reduce_region_config.block_threads * seg_reduce_region_config.items_per_thread;
// Get device ordinal
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 SM occupancy for histogram_region_kernel
int seg_reduce_region_sm_occupancy;
if (CubDebug(error = MaxSmOccupancy(
seg_reduce_region_sm_occupancy,
sm_version,
seg_reduce_region_kernel,
seg_reduce_region_config.block_threads))) break;
// Get device occupancy for histogram_region_kernel
int seg_reduce_region_occupancy = seg_reduce_region_sm_occupancy * sm_count;
// Even-share work distribution
int num_diagonals = num_values + num_segments; // Total number of work items
int subscription_factor = seg_reduce_region_sm_occupancy; // Amount of CTAs to oversubscribe the device beyond actively-resident (heuristic)
int max_grid_size = seg_reduce_region_occupancy * subscription_factor;
GridEvenShare<OffsetT>even_share(
num_diagonals,
max_grid_size,
tile_size);
// Get grid size for seg_reduce_region_kernel
int seg_reduce_region_grid_size = even_share.grid_size;
// Number of "fix-up" reduce-by-key tuples (2 per thread block)
int num_tuple_partials = seg_reduce_region_grid_size * 2;
int num_partition_samples = seg_reduce_region_grid_size + 1;
// Temporary storage allocation requirements
void* allocations[2];
size_t allocation_sizes[2] =
{
num_tuple_partials * sizeof(KeyValuePair), // bytes needed for "fix-up" reduce-by-key tuples
num_partition_samples * sizeof(IndexPair), // bytes needed block indices
};
// Alias the temporary allocations from the single storage blob (or set the necessary size of the blob)
if (CubDebug(error = AliasTemporaries(d_temp_storage, temp_storage_bytes, allocations, allocation_sizes))) break;
if (d_temp_storage == NULL)
{
// Return if the caller is simply requesting the size of the storage allocation
return cudaSuccess;
}
// Alias the allocations
KeyValuePair *d_tuple_partials = (KeyValuePair*) allocations[0]; // "fix-up" tuples
IndexPair *d_block_idx = (IndexPair *) allocations[1]; // block starting/ending indices
// Array of segment end-offsets
SegmentOffsetIterator d_segment_end_offsets = d_segment_offsets + 1;
// Grid launch params for seg_reduce_partition_kernel
int partition_block_size = 32;
int partition_grid_size = (num_partition_samples + partition_block_size - 1) / partition_block_size;
// Partition work among multiple thread blocks if necessary
if (seg_reduce_region_grid_size > 1)
{
// Log seg_reduce_partition_kernel configuration
if (debug_synchronous) _CubLog("Invoking seg_reduce_partition_kernel<<<%d, %d, 0, %lld>>>()
",
partition_grid_size, partition_block_size, (long long) stream);
// Invoke seg_reduce_partition_kernel
seg_reduce_partition_kernel<<<partition_grid_size, partition_block_size, 0, stream>>>(
d_segment_end_offsets, ///< [in] A sequence of \p num_segments segment end-offsets
d_block_idx,
num_partition_samples,
num_values, ///< [in] Number of values to reduce
num_segments, ///< [in] Number of segments being reduced
even_share); ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block
// Sync the stream if specified
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
}
// Log seg_reduce_region_kernel configuration
if (debug_synchronous) _CubLog("Invoking seg_reduce_region_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread, %d SM occupancy
",
seg_reduce_region_grid_size, seg_reduce_region_config.block_threads, (long long) stream, seg_reduce_region_config.items_per_thread, seg_reduce_region_sm_occupancy);
// Mooch
if (CubDebug(error = cudaDeviceSetSharedMemConfig(cudaSharedMemBankSizeEightByte))) break;
// Invoke seg_reduce_region_kernel
seg_reduce_region_kernel<<<seg_reduce_region_grid_size, seg_reduce_region_config.block_threads, 0, stream>>>(
d_segment_end_offsets,
d_values,
d_output,
d_tuple_partials,
d_block_idx,
num_values,
num_segments,
identity,
reduction_op,
even_share);
// Sync the stream if specified
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
/*
// Perform "fix-up" of region partial reductions if grid size is greater than one thread block
if (seg_reduce_region_grid_size > 1)
{
// Log seg_reduce_region_by_key_kernel configuration
if (debug_synchronous) _CubLog("Invoking seg_reduce_region_by_key_kernel<<<%d, %d, 0, %lld>>>(), %d items per thread
",
1, seg_reduce_region_by_key_config.block_threads, (long long) stream, seg_reduce_region_by_key_config.items_per_thread);
// Invoke seg_reduce_region_by_key_kernel
seg_reduce_region_by_key_kernel<<<1, seg_reduce_region_by_key_config.block_threads, 0, stream>>>(
d_tuple_partials,
d_output,
num_segments,
num_tuple_partials,
identity,
reduction_op);
// Sync the stream if specified
if (debug_synchronous && (CubDebug(error = SyncStream(stream)))) break;
}
*/
}
while (0);
return error;
#endif // CUB_RUNTIME_ENABLED
}
/**
* Internal dispatch routine for computing a device-wide segmented reduction.
*/
__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] Reference to size in bytes of \p d_temp_storage allocation.
ValueIterator d_values, ///< [in] A sequence of \p num_values data to reduce
SegmentOffsetIterator d_segment_offsets, ///< [in] A sequence of (\p num_segments + 1) segment offsets
OutputIteratorT d_output, ///< [out] A sequence of \p num_segments segment totals
OffsetT num_values, ///< [in] Total number of values to reduce
OffsetT num_segments, ///< [in] Number of segments being reduced
Value identity, ///< [in] Identity value (for zero-length segments)
ReductionOp reduction_op, ///< [in] Reduction operator
cudaStream_t stream, ///< [in] <b>[optional]</b> CUDA stream to launch kernels within. Default is stream<sub>0</sub>.
bool debug_synchronous) ///< [in] <b>[optional]</b> Whether or not to synchronize the stream after every kernel launch to check for errors. Also causes launch configurations to be printed to the console. Default is \p false.
{
cudaError error = cudaSuccess;
do
{
// Get PTX version
int ptx_version;
#if (CUB_PTX_ARCH == 0)
if (CubDebug(error = PtxVersion(ptx_version))) break;
#else
ptx_version = CUB_PTX_ARCH;
#endif
// Get kernel kernel dispatch configurations
SegReduceKernelConfig seg_reduce_region_config;
SegReduceByKeyKernelConfig seg_reduce_region_by_key_config;
InitConfigs(ptx_version, seg_reduce_region_config, seg_reduce_region_by_key_config);
// Dispatch
if (CubDebug(error = Dispatch(
d_temp_storage,
temp_storage_bytes,
d_values,
d_segment_offsets,
d_output,
num_values,
num_segments,
identity,
reduction_op,
stream,
debug_synchronous,
ptx_version, // Use PTX version instead of SM version because, as a statically known quantity, this improves device-side launch dramatically but at the risk of imprecise occupancy calculation for mismatches
SegReducePartitionKernel<SegmentOffsetIterator, OffsetT>,
SegReduceRegionKernel<PtxSegReduceRegionPolicy, SegmentOffsetIterator, ValueIterator, OutputIteratorT, ReductionOp, OffsetT, Value>,
SegReduceRegionByKeyKernel<PtxSegReduceRegionByKeyPolicy, KeyValuePair*, OutputIteratorT, ReductionOp, OffsetT, Value>,
seg_reduce_region_config,
seg_reduce_region_by_key_config))) break;
}
while (0);
return error;
}
};
/******************************************************************************
* DeviceSegReduce
*****************************************************************************/
/**
* \brief DeviceSegReduce provides operations for computing a device-wide, parallel segmented reduction across a sequence of data items residing within global memory.
* \ingroup DeviceModule
*
* \par Overview
* A <a href="http://en.wikipedia.org/wiki/Reduce_(higher-order_function)"><em>reduction</em></a> (or <em>fold</em>)
* uses a binary combining operator to compute a single aggregate from a list of input elements.
*
* \par Usage Considerations
* \cdp_class{DeviceReduce}
*
*/
struct DeviceSegReduce
{
/**
* \brief Computes a device-wide segmented reduction using the specified binary \p reduction_op functor.
*
* \par
* Does not support non-commutative reduction operators.
*
* \devicestorage
*
* \cdp
*
* \iterator
*
* \tparam ValueIterator <b>[inferred]</b> Random-access input iterator type for reading values
* \tparam SegmentOffsetIterator <b>[inferred]</b> Random-access input iterator type for reading segment end-offsets
* \tparam OutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing segment reductions
* \tparam Value <b>[inferred]</b> Value type
* \tparam ReductionOp <b>[inferred]</b> Binary reduction operator type having member <tt>T operator()(const T &a, const T &b)</tt>
*/
template <
typename ValueIterator,
typename SegmentOffsetIterator,
typename OutputIteratorT,
typename Value,
typename ReductionOp>
__host__ __device__ __forceinline__
static cudaError_t Reduce(
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] Reference to size in bytes of \p d_temp_storage allocation.
ValueIterator d_values, ///< [in] A sequence of \p num_values data to reduce
SegmentOffsetIterator d_segment_offsets, ///< [in] A sequence of (\p num_segments + 1) segment offsets
OutputIteratorT d_output, ///< [out] A sequence of \p num_segments segment totals
int num_values, ///< [in] Total number of values to reduce
int num_segments, ///< [in] Number of segments being reduced
Value identity, ///< [in] Identity value (for zero-length segments)
ReductionOp reduction_op, ///< [in] Reduction operator
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. Also causes launch configurations to be printed to the console. Default is \p false.
{
// Signed integer type for global offsets
typedef int OffsetT;
typedef DeviceSegReduceDispatch<
ValueIterator,
SegmentOffsetIterator,
OutputIteratorT,
ReductionOp,
OffsetT>
DeviceSegReduceDispatch;
return DeviceSegReduceDispatch::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_values,
d_segment_offsets,
d_output,
num_values,
num_segments,
identity,
reduction_op,
stream,
debug_synchronous);
}
/**
* \brief Computes a device-wide segmented sum using the addition ('+') operator.
*
* \par
* Does not support non-commutative summation.
*
* \devicestorage
*
* \cdp
*
* \iterator
*
* \tparam ValueIterator <b>[inferred]</b> Random-access input iterator type for reading values
* \tparam SegmentOffsetIterator <b>[inferred]</b> Random-access input iterator type for reading segment end-offsets
* \tparam OutputIteratorT <b>[inferred]</b> Random-access output iterator type for writing segment reductions
*/
template <
typename ValueIterator,
typename SegmentOffsetIterator,
typename OutputIteratorT>
__host__ __device__ __forceinline__
static cudaError_t Sum(
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] Reference to size in bytes of \p d_temp_storage allocation.
ValueIterator d_values, ///< [in] A sequence of \p num_values data to reduce
SegmentOffsetIterator d_segment_offsets, ///< [in] A sequence of (\p num_segments + 1) segment offsets
OutputIteratorT d_output, ///< [out] A sequence of \p num_segments segment totals
int num_values, ///< [in] Total number of values to reduce
int num_segments, ///< [in] Number of segments being reduced
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. Also causes launch configurations to be printed to the console. Default is \p false.
{
// Signed integer type for global offsets
typedef int OffsetT;
// Value type
typedef typename std::iterator_traits<ValueIterator>::value_type Value;
Value identity = Value();
cub::Sum reduction_op;
typedef DeviceSegReduceDispatch<
ValueIterator,
SegmentOffsetIterator,
OutputIteratorT,
cub::Sum,
OffsetT>
DeviceSegReduceDispatch;
return DeviceSegReduceDispatch::Dispatch(
d_temp_storage,
temp_storage_bytes,
d_values,
d_segment_offsets,
d_output,
num_values,
num_segments,
identity,
reduction_op,
stream,
debug_synchronous);
}
};
//---------------------------------------------------------------------
// Test generation
//---------------------------------------------------------------------
/**
* Initialize problem
*/
template <typename OffsetT, typename Value>
void Initialize(
GenMode gen_mode,
Value *h_values,
vector<OffsetT> &segment_offsets,
int num_values,
int avg_segment_size)
{
// Initialize values
// if (g_verbose) printf("Values: ");
for (int i = 0; i < num_values; ++i)
{
InitValue(gen_mode, h_values[i], i);
// if (g_verbose) std::cout << h_values[i] << ", ";
}
// if (g_verbose) printf("
");
// Initialize segment lengths
const unsigned int MAX_INTEGER = -1u;
const unsigned int MAX_SEGMENT_LENGTH = avg_segment_size * 2;
const double SCALE_FACTOR = double(MAX_SEGMENT_LENGTH) / double(MAX_INTEGER);
segment_offsets.push_back(0);
OffsetT consumed = 0;
OffsetT remaining = num_values;
while (remaining > 0)
{
// Randomly sample a 32-bit unsigned int
unsigned int segment_length;
RandomBits(segment_length);
// Scale to maximum segment length
segment_length = (unsigned int) (double(segment_length) * SCALE_FACTOR);
segment_length = CUB_MIN(segment_length, remaining);
consumed += segment_length;
remaining -= segment_length;
segment_offsets.push_back(consumed);
}
}
/**
* Compute reference answer
*/
template <typename OffsetT, typename Value>
void ComputeReference(
Value *h_values,
OffsetT *h_segment_offsets,
Value *h_reference,
int num_segments,
Value identity)
{
if (g_verbose) printf("%d segment reductions: ", num_segments);
for (int segment = 0; segment < num_segments; ++segment)
{
h_reference[segment] = identity;
for (int i = h_segment_offsets[segment]; i < h_segment_offsets[segment + 1]; ++i)
{
h_reference[segment] += h_values[i];
}
if (g_verbose) std::cout << h_reference[segment] << ", ";
}
if (g_verbose) printf("
");
}
/**
* Simple test of device
*/
template <
bool CDP,
typename OffsetT,
typename Value,
typename ReductionOp>
void Test(
OffsetT num_values,
int avg_segment_size,
ReductionOp reduction_op,
Value identity,
char* type_string)
{
Value *h_values = NULL;
Value *h_reference = NULL;
OffsetT *h_segment_offsets = NULL;
printf("%d
", num_values);
// Initialize problem on host
h_values = new Value[num_values];
vector<OffsetT> segment_offsets;
Initialize(UNIFORM, h_values, segment_offsets, num_values, avg_segment_size);
// Allocate simple offsets array and copy STL vector into it
h_segment_offsets = new OffsetT[segment_offsets.size()];
for (int i = 0; i < segment_offsets.size(); ++i)
h_segment_offsets[i] = segment_offsets[i];
OffsetT num_segments = segment_offsets.size() - 1;
if (g_verbose)
{
printf("%d segment offsets: ", num_segments);
for (int i = 0; i < num_segments; ++i)
std::cout << h_segment_offsets[i] << "(" << h_segment_offsets[i + 1] - h_segment_offsets[i] << "), ";
if (g_verbose) std::cout << std::endl << std::endl;
}
// Solve problem on host
h_reference = new Value[num_segments];
ComputeReference(h_values, h_segment_offsets, h_reference, num_segments, identity);
printf("
%s cub::DeviceSegReduce::%s %d items (%d-byte %s), %d segments (%d-byte offset indices)
",
(CDP) ? "CDP device invoked" : "Host-invoked",
(Equals<ReductionOp, Sum>::VALUE) ? "Sum" : "Reduce",
num_values, (int) sizeof(Value), type_string,
num_segments, (int) sizeof(OffsetT));
fflush(stdout);
// Allocate and initialize problem on device
Value *d_values = NULL;
OffsetT *d_segment_offsets = NULL;
Value *d_output = NULL;
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_values, sizeof(Value) * num_values));
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_segment_offsets, sizeof(OffsetT) * (num_segments + 1)));
CubDebugExit(g_allocator.DeviceAllocate((void**)&d_output, sizeof(Value) * num_segments));
CubDebugExit(cudaMemcpy(d_values, h_values, sizeof(Value) * num_values, cudaMemcpyHostToDevice));
CubDebugExit(cudaMemcpy(d_segment_offsets, h_segment_offsets, sizeof(OffsetT) * (num_segments + 1), cudaMemcpyHostToDevice));
// Request and allocate temporary storage
void *d_temp_storage = NULL;
size_t temp_storage_bytes = 0;
CubDebugExit(DeviceSegReduce::Sum(d_temp_storage, temp_storage_bytes, d_values, d_segment_offsets, d_output, num_values, num_segments, 0, false));
CubDebugExit(g_allocator.DeviceAllocate(&d_temp_storage, temp_storage_bytes));
// Clear device output
CubDebugExit(cudaMemset(d_output, 0, sizeof(Value) * num_segments));
// Run warmup/correctness iteration
CubDebugExit(DeviceSegReduce::Sum(d_temp_storage, temp_storage_bytes, d_values, d_segment_offsets, d_output, num_values, num_segments, 0, true));
// Check for correctness (and display results, if specified)
int compare = CompareDeviceResults(h_reference, d_output, num_segments, true, g_verbose);
printf("\t%s", compare ? "FAIL" : "PASS");
// Flush any stdout/stderr
fflush(stdout);
fflush(stderr);
// Performance
GpuTimer gpu_timer;
gpu_timer.Start();
for (int i = 0; i < g_timing_iterations; ++i)
{
CubDebugExit(DeviceSegReduce::Sum(d_temp_storage, temp_storage_bytes, d_values, d_segment_offsets, d_output, num_values, num_segments, 0, false));
}
gpu_timer.Stop();
float elapsed_millis = gpu_timer.ElapsedMillis();
// Display performance
if (g_timing_iterations > 0)
{
float avg_millis = elapsed_millis / g_timing_iterations;
float giga_rate = float(num_values) / avg_millis / 1000.0 / 1000.0;
float giga_bandwidth = giga_rate *
printf(", %.3f avg ms, %.3f billion items/s, %.3f logical GB/s", avg_millis, giga_rate, giga_bandwidth);
}
// Device cleanup
if (d_values) CubDebugExit(g_allocator.DeviceFree(d_values));
if (d_segment_offsets) CubDebugExit(g_allocator.DeviceFree(d_segment_offsets));
if (d_output) CubDebugExit(g_allocator.DeviceFree(d_output));
if (d_temp_storage) CubDebugExit(g_allocator.DeviceFree(d_temp_storage));
// Host cleanup
if (h_values) delete[] h_values;
if (h_segment_offsets) delete[] h_segment_offsets;
if (h_reference) delete[] h_reference;
}
/**
* Main
*/
int main(int argc, char** argv)
{
int num_values = 32 * 1024 * 1024;
int avg_segment_size = 500;
// Initialize command line
CommandLineArgs args(argc, argv);
g_verbose = args.CheckCmdLineFlag("v");
args.GetCmdLineArgument("n", num_values);
args.GetCmdLineArgument("ss", avg_segment_size);
args.GetCmdLineArgument("i", g_timing_iterations);
// Print usage
if (args.CheckCmdLineFlag("help"))
{
printf("%s "
"[--device=<device-id>] "
"[--v] "
"[--i=<timing iterations>] "
"[--n=<input samples>]
"
"[--ss=<average segment size>]
"
"
", argv[0]);
exit(0);
}
// Initialize device
CubDebugExit(args.DeviceInit());
Test<false>((int) num_values, avg_segment_size, Sum(), (long long) 0, CUB_TYPE_STRING(long long));
return 0;
}
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