/****************************************************************************** * Copyright (c) 2011, Duane Merrill. All rights reserved. * Copyright (c) 2011-2018, NVIDIA CORPORATION. All rights reserved. * * Redistribution and use in source and binary forms, with or without * modification, are permitted provided that the following conditions are met: * * Redistributions of source code must retain the above copyright * notice, this list of conditions and the following disclaimer. * * Redistributions in binary form must reproduce the above copyright * notice, this list of conditions and the following disclaimer in the * documentation and/or other materials provided with the distribution. * * Neither the name of the NVIDIA CORPORATION nor the * names of its contributors may be used to endorse or promote products * derived from this software without specific prior written permission. * * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND * ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED * WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE * DISCLAIMED. IN NO EVENT SHALL NVIDIA CORPORATION BE LIABLE FOR ANY * DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES * (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; * LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND * ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS * SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. * ******************************************************************************/ /** * @file * The cub::BlockHistogram class provides [collective](index.html#sec0) methods for * constructing block-wide histograms from data samples partitioned across a CUDA thread block. */ #pragma once #include #if defined(_CCCL_IMPLICIT_SYSTEM_HEADER_GCC) # pragma GCC system_header #elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_CLANG) # pragma clang system_header #elif defined(_CCCL_IMPLICIT_SYSTEM_HEADER_MSVC) # pragma system_header #endif // no system header #include #include #include CUB_NAMESPACE_BEGIN /****************************************************************************** * Algorithmic variants ******************************************************************************/ /** * @brief BlockHistogramAlgorithm enumerates alternative algorithms for the parallel construction of * block-wide histograms. */ enum BlockHistogramAlgorithm { /** * @par Overview * Sorting followed by differentiation. Execution is comprised of two phases: * -# Sort the data using efficient radix sort * -# Look for "runs" of same-valued keys by detecting discontinuities; the run-lengths are histogram bin counts. * * @par Performance Considerations * Delivers consistent throughput regardless of sample bin distribution. */ BLOCK_HISTO_SORT, /** * @par Overview * Use atomic addition to update byte counts directly * * @par Performance Considerations * Performance is strongly tied to the hardware implementation of atomic * addition, and may be significantly degraded for non uniformly-random * input distributions where many concurrent updates are likely to be * made to the same bin counter. */ BLOCK_HISTO_ATOMIC, }; /****************************************************************************** * Block histogram ******************************************************************************/ /** * @brief The BlockHistogram class provides [collective](index.html#sec0) methods for * constructing block-wide histograms from data samples partitioned across a CUDA thread * block. ![](histogram_logo.png) * * @ingroup BlockModule * * @tparam T * The sample type being histogrammed (must be castable to an integer bin identifier) * * @tparam BLOCK_DIM_X * The thread block length in threads along the X dimension * * @tparam ITEMS_PER_THREAD * The number of items per thread * * @tparam BINS * The number bins within the histogram * * @tparam ALGORITHM * [optional] cub::BlockHistogramAlgorithm enumerator specifying the underlying algorithm * to use (default: cub::BLOCK_HISTO_SORT) * * @tparam BLOCK_DIM_Y * [optional] The thread block length in threads along the Y dimension (default: 1) * * @tparam BLOCK_DIM_Z * [optional] The thread block length in threads along the Z dimension (default: 1) * * @tparam LEGACY_PTX_ARCH * [optional] Unused. * * @par Overview * - A histogram * counts the number of observations that fall into each of the disjoint categories (known as * bins). * - The `T` type must be implicitly castable to an integer type. * - BlockHistogram expects each integral `input[i]` value to satisfy * `0 <= input[i] < BINS`. Values outside of this range result in undefined * behavior. * - BlockHistogram can be optionally specialized to use different algorithms: * -# cub::BLOCK_HISTO_SORT. Sorting followed by differentiation. [More...](\ref * cub::BlockHistogramAlgorithm) * -# cub::BLOCK_HISTO_ATOMIC. Use atomic addition to update byte counts directly. * [More...](\ref cub::BlockHistogramAlgorithm) * * @par Performance Considerations * - @granularity * * @par A Simple Example * @blockcollective{BlockHistogram} * @par * The code snippet below illustrates a 256-bin histogram of 512 integer samples that * are partitioned across 128 threads where each thread owns 4 samples. * @par * @code * #include // or equivalently * * __global__ void ExampleKernel(...) * { * // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 character * samples each typedef cub::BlockHistogram BlockHistogram; * * // Allocate shared memory for BlockHistogram * __shared__ typename BlockHistogram::TempStorage temp_storage; * * // Allocate shared memory for block-wide histogram bin counts * __shared__ unsigned int smem_histogram[256]; * * // Obtain input samples per thread * unsigned char data[4]; * ... * * // Compute the block-wide histogram * BlockHistogram(temp_storage).Histogram(data, smem_histogram); * * @endcode * * @par Performance and Usage Considerations * - All input values must fall between [0, BINS), or behavior is undefined. * - The histogram output can be constructed in shared or device-accessible memory * - See cub::BlockHistogramAlgorithm for performance details regarding algorithmic alternatives * * @par Re-using dynamically allocating shared memory * The following example under the examples/block folder illustrates usage of * dynamically shared memory with BlockReduce and how to re-purpose * the same memory region: * example_block_reduce_dyn_smem.cu * * This example can be easily adapted to the storage required by BlockHistogram. */ template < typename T, int BLOCK_DIM_X, int ITEMS_PER_THREAD, int BINS, BlockHistogramAlgorithm ALGORITHM = BLOCK_HISTO_SORT, int BLOCK_DIM_Y = 1, int BLOCK_DIM_Z = 1, int LEGACY_PTX_ARCH = 0> class BlockHistogram { private: /****************************************************************************** * Constants and type definitions ******************************************************************************/ /// Constants enum { /// The thread block size in threads BLOCK_THREADS = BLOCK_DIM_X * BLOCK_DIM_Y * BLOCK_DIM_Z, }; /// Internal specialization. using InternalBlockHistogram = cub::detail::conditional_t, BlockHistogramAtomic>; /// Shared memory storage layout type for BlockHistogram typedef typename InternalBlockHistogram::TempStorage _TempStorage; /****************************************************************************** * Thread fields ******************************************************************************/ /// Shared storage reference _TempStorage &temp_storage; /// Linear thread-id unsigned int linear_tid; /****************************************************************************** * Utility methods ******************************************************************************/ /// Internal storage allocator __device__ __forceinline__ _TempStorage& PrivateStorage() { __shared__ _TempStorage private_storage; return private_storage; } public: /// @smemstorage{BlockHistogram} struct TempStorage : Uninitialized<_TempStorage> {}; /******************************************************************//** * @name Collective constructors *********************************************************************/ //@{ /** * @brief Collective constructor using a private static allocation of shared memory as temporary storage. */ __device__ __forceinline__ BlockHistogram() : temp_storage(PrivateStorage()), linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)) {} /** * @brief Collective constructor using the specified memory allocation as temporary storage. * * @param[in] temp_storage * Reference to memory allocation having layout type TempStorage */ __device__ __forceinline__ BlockHistogram(TempStorage &temp_storage) : temp_storage(temp_storage.Alias()) , linear_tid(RowMajorTid(BLOCK_DIM_X, BLOCK_DIM_Y, BLOCK_DIM_Z)) {} //@} end member group /******************************************************************//** * @name Histogram operations *********************************************************************/ //@{ /** * @brief Initialize the shared histogram counters to zero. * * @par Snippet * The code snippet below illustrates a the initialization and update of a * histogram of 512 integer samples that are partitioned across 128 threads * where each thread owns 4 samples. * @par * @code * #include // or equivalently * * __global__ void ExampleKernel(...) * { * // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 character samples each * typedef cub::BlockHistogram BlockHistogram; * * // Allocate shared memory for BlockHistogram * __shared__ typename BlockHistogram::TempStorage temp_storage; * * // Allocate shared memory for block-wide histogram bin counts * __shared__ unsigned int smem_histogram[256]; * * // Obtain input samples per thread * unsigned char thread_samples[4]; * ... * * // Initialize the block-wide histogram * BlockHistogram(temp_storage).InitHistogram(smem_histogram); * * // Update the block-wide histogram * BlockHistogram(temp_storage).Composite(thread_samples, smem_histogram); * * @endcode * * @tparam CounterT * [inferred] Histogram counter type */ template __device__ __forceinline__ void InitHistogram(CounterT histogram[BINS]) { // Initialize histogram bin counts to zeros int histo_offset = 0; #pragma unroll for(; histo_offset + BLOCK_THREADS <= BINS; histo_offset += BLOCK_THREADS) { histogram[histo_offset + linear_tid] = 0; } // Finish up with guarded initialization if necessary if ((BINS % BLOCK_THREADS != 0) && (histo_offset + linear_tid < BINS)) { histogram[histo_offset + linear_tid] = 0; } } /** * @brief Constructs a block-wide histogram in shared/device-accessible memory. * Each thread contributes an array of input elements. * * @par * - @granularity * - @smemreuse * * @par Snippet * The code snippet below illustrates a 256-bin histogram of 512 integer samples that * are partitioned across 128 threads where each thread owns 4 samples. * @par * @code * #include // or equivalently * * __global__ void ExampleKernel(...) * { * // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 * character samples each typedef cub::BlockHistogram * BlockHistogram; * * // Allocate shared memory for BlockHistogram * __shared__ typename BlockHistogram::TempStorage temp_storage; * * // Allocate shared memory for block-wide histogram bin counts * __shared__ unsigned int smem_histogram[256]; * * // Obtain input samples per thread * unsigned char thread_samples[4]; * ... * * // Compute the block-wide histogram * BlockHistogram(temp_storage).Histogram(thread_samples, smem_histogram); * * @endcode * * @tparam CounterT * [inferred] Histogram counter type * * @param[in] items * Calling thread's input values to histogram * * @param[out] histogram * Reference to shared/device-accessible memory histogram */ template __device__ __forceinline__ void Histogram(T (&items)[ITEMS_PER_THREAD], CounterT histogram[BINS]) { // Initialize histogram bin counts to zeros InitHistogram(histogram); CTA_SYNC(); // Composite the histogram InternalBlockHistogram(temp_storage).Composite(items, histogram); } /** * @brief Updates an existing block-wide histogram in shared/device-accessible memory. * Each thread composites an array of input elements. * * @par * - @granularity * - @smemreuse * * @par Snippet * The code snippet below illustrates a the initialization and update of a * histogram of 512 integer samples that are partitioned across 128 threads * where each thread owns 4 samples. * @par * @code * #include // or equivalently * * __global__ void ExampleKernel(...) * { * // Specialize a 256-bin BlockHistogram type for a 1D block of 128 threads having 4 * character samples each typedef cub::BlockHistogram * BlockHistogram; * * // Allocate shared memory for BlockHistogram * __shared__ typename BlockHistogram::TempStorage temp_storage; * * // Allocate shared memory for block-wide histogram bin counts * __shared__ unsigned int smem_histogram[256]; * * // Obtain input samples per thread * unsigned char thread_samples[4]; * ... * * // Initialize the block-wide histogram * BlockHistogram(temp_storage).InitHistogram(smem_histogram); * * // Update the block-wide histogram * BlockHistogram(temp_storage).Composite(thread_samples, smem_histogram); * * @endcode * * @tparam CounterT * [inferred] Histogram counter type * * @param[in] items * Calling thread's input values to histogram * * @param[out] histogram * Reference to shared/device-accessible memory histogram */ template __device__ __forceinline__ void Composite(T (&items)[ITEMS_PER_THREAD], CounterT histogram[BINS]) { InternalBlockHistogram(temp_storage).Composite(items, histogram); } }; CUB_NAMESPACE_END