/****************************************************************************** * Copyright (c) 2011, Duane Merrill. All rights reserved. * Copyright (c) 2011-2016, 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 #include #include #include #include #include #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 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 begin, // Begin offsets into a and b IndexPair end, // End offsets into a and b IndexPair &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::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)\n", 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 begin, // Begin offsets into a and b IndexPair end, // End offsets into a and b IndexPair &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 T operator()(const T &a, const T &b) 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::value_type SegmentOffset; // Value type typedef typename std::iterator_traits::value_type Value; // Counting iterator type typedef CountingInputIterator CountingIterator; // Segment offsets iterator wrapper type typedef typename If<(IsPointer::VALUE), CacheModifiedInputIterator, // 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::VALUE), CacheModifiedInputIterator, // 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 KeyValuePair; // Index pair data type typedef IndexPair IndexPair; // BlockScan scan operator for reduction-by-segment typedef ReduceByKeyOp 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 threadblock 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 threadblock'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(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(threadIdx.x, d_output + block_idx.a_idx, segment_reductions, tile_segments); } /** * Multi-segment tile reduction. */ template __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(threadIdx.x, d_values + block_idx.b_idx, values, tile_values, identity); // Store to shared StoreDirectStriped(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\n", 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(threadIdx.x, d_segment_end_offsets + block_idx.a_idx, segment_offsets, num_segments, num_values); // Store to shared StoreDirectStriped(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 thread₀ next_tile_idx); // [out] Suffix item shifted out by the thread_{BLOCK_THREADS-1} 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(block_idx, thread_idx, next_thread_idx, next_tile_idx); } else { // Merge the tile's segment and value indices (partially full tile) MultiSegmentTile(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 T operator()(const T &a, const T &b) 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::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 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 threadblock 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 __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(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(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 *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 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::value_type SegmentOffset; // Counting iterator type typedef CountingInputIterator CountingIterator; // Cache-modified iterator for segment end-offsets CacheModifiedInputIterator 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 start_idx = {0, 0}; IndexPair end_idx = {num_segments, num_values}; IndexPair 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 T operator()(const T &a, const T &b) 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 *d_tuple_partials, ///< [out] A sequence of (gridDim.x * 2) partial reduction tuples IndexPair *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 even_share) ///< [in] Even-share descriptor for mapping an equal number of tiles onto each thread block { typedef KeyValuePair KeyValuePair; // Specialize threadblock 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 threadblock 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 T operator()(const T &a, const T &b) 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 threadblock 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 T operator()(const T &a, const T &b) typename OffsetT> ///< Signed integer type for global offsets struct DeviceSegReduceDispatch { // Value type typedef typename std::iterator_traits::value_type Value; // Reduce-by-key data type tuple (segment-ID, value) typedef KeyValuePair KeyValuePair; // Index pair data type typedef IndexPairIndexPair; /****************************************************************************** * 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(); seg_reduce_region_by_key_config.Init(); #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(); seg_reduce_region_by_key_config.template Init(); } /* else if (ptx_version >= 300) { seg_reduce_region_config.template Init(); seg_reduce_region_by_key_config.template Init(); } else if (ptx_version >= 200) { seg_reduce_region_config.template Init(); seg_reduce_region_by_key_config.template Init(); } else if (ptx_version >= 130) { seg_reduce_region_config.template Init(); seg_reduce_region_by_key_config.template Init(); } */ else { seg_reduce_region_config.template Init(); seg_reduce_region_by_key_config.template Init(); } #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 __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 __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] [optional] CUDA stream to launch kernels within. Default is stream₀. bool debug_synchronous, ///< [in] [optional] 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; GridEvenShareeven_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>>>()\n", partition_grid_size, partition_block_size, (long long) stream); // Invoke seg_reduce_partition_kernel seg_reduce_partition_kernel<<>>( 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\n", 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<<>>( 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\n", 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] [optional] CUDA stream to launch kernels within. Default is stream₀. bool debug_synchronous) ///< [in] [optional] 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, SegReduceRegionKernel, SegReduceRegionByKeyKernel, 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 reduction (or fold) * 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 [inferred] Random-access input iterator type for reading values * \tparam SegmentOffsetIterator [inferred] Random-access input iterator type for reading segment end-offsets * \tparam OutputIteratorT [inferred] Random-access output iterator type for writing segment reductions * \tparam Value [inferred] Value type * \tparam ReductionOp [inferred] Binary reduction operator type having member T operator()(const T &a, const T &b) */ 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] [optional] CUDA stream to launch kernels within. Default is stream₀. bool debug_synchronous = false) ///< [in] [optional] 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 [inferred] Random-access input iterator type for reading values * \tparam SegmentOffsetIterator [inferred] Random-access input iterator type for reading segment end-offsets * \tparam OutputIteratorT [inferred] 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] [optional] CUDA stream to launch kernels within. Default is stream₀. bool debug_synchronous = false) ///< [in] [optional] 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::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 void Initialize( GenMode gen_mode, Value *h_values, vector &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("\n\n"); // 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 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("\n\n"); } /** * 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\n", num_values); // Initialize problem on host h_values = new Value[num_values]; vector 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("\n\n%s cub::DeviceSegReduce::%s %d items (%d-byte %s), %d segments (%d-byte offset indices)\n", (CDP) ? "CDP device invoked" : "Host-invoked", (Equals::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=] " "[--v] " "[--i=] " "[--n=]\n" "[--ss=]\n" "\n", argv[0]); exit(0); } // Initialize device CubDebugExit(args.DeviceInit()); Test((int) num_values, avg_segment_size, Sum(), (long long) 0, CUB_TYPE_STRING(long long)); return 0; }