212 lines
8.1 KiB
C++
212 lines
8.1 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP
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#define OPENCV_CUDA_DEVICE_BLOCK_HPP
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/** @file
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* @deprecated Use @ref cudev instead.
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*/
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//! @cond IGNORED
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namespace cv { namespace cuda { namespace device
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{
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struct Block
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{
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static __device__ __forceinline__ unsigned int id()
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{
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return blockIdx.x;
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}
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static __device__ __forceinline__ unsigned int stride()
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{
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return blockDim.x * blockDim.y * blockDim.z;
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}
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static __device__ __forceinline__ void sync()
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{
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__syncthreads();
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}
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static __device__ __forceinline__ int flattenedThreadId()
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{
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return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
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}
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template<typename It, typename T>
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static __device__ __forceinline__ void fill(It beg, It end, const T& value)
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{
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int STRIDE = stride();
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It t = beg + flattenedThreadId();
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for(; t < end; t += STRIDE)
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*t = value;
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}
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template<typename OutIt, typename T>
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static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
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{
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int STRIDE = stride();
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int tid = flattenedThreadId();
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value += tid;
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for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
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*t = value;
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}
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template<typename InIt, typename OutIt>
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static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
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{
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int STRIDE = stride();
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InIt t = beg + flattenedThreadId();
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OutIt o = out + (t - beg);
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for(; t < end; t += STRIDE, o += STRIDE)
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*o = *t;
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}
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template<typename InIt, typename OutIt, class UnOp>
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static __device__ __forceinline__ void transform(InIt beg, InIt end, OutIt out, UnOp op)
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{
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int STRIDE = stride();
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InIt t = beg + flattenedThreadId();
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OutIt o = out + (t - beg);
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for(; t < end; t += STRIDE, o += STRIDE)
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*o = op(*t);
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}
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template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
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static __device__ __forceinline__ void transform(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
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{
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int STRIDE = stride();
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InIt1 t1 = beg1 + flattenedThreadId();
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InIt2 t2 = beg2 + flattenedThreadId();
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OutIt o = out + (t1 - beg1);
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for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
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*o = op(*t1, *t2);
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}
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template<int CTA_SIZE, typename T, class BinOp>
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static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
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{
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int tid = flattenedThreadId();
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T val = buffer[tid];
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if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
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if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
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if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
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if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
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if (tid < 32)
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{
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if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
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if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
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if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
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if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
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if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
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if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
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}
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}
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template<int CTA_SIZE, typename T, class BinOp>
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static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
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{
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int tid = flattenedThreadId();
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T val = buffer[tid] = init;
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__syncthreads();
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if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
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if (CTA_SIZE >= 512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
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if (CTA_SIZE >= 256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
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if (CTA_SIZE >= 128) { if (tid < 64) buffer[tid] = val = op(val, buffer[tid + 64]); __syncthreads(); }
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if (tid < 32)
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{
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if (CTA_SIZE >= 64) { buffer[tid] = val = op(val, buffer[tid + 32]); }
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if (CTA_SIZE >= 32) { buffer[tid] = val = op(val, buffer[tid + 16]); }
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if (CTA_SIZE >= 16) { buffer[tid] = val = op(val, buffer[tid + 8]); }
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if (CTA_SIZE >= 8) { buffer[tid] = val = op(val, buffer[tid + 4]); }
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if (CTA_SIZE >= 4) { buffer[tid] = val = op(val, buffer[tid + 2]); }
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if (CTA_SIZE >= 2) { buffer[tid] = val = op(val, buffer[tid + 1]); }
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}
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__syncthreads();
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return buffer[0];
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}
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template <typename T, class BinOp>
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static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
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{
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int ftid = flattenedThreadId();
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int sft = stride();
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if (sft < n)
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{
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for (unsigned int i = sft + ftid; i < n; i += sft)
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data[ftid] = op(data[ftid], data[i]);
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__syncthreads();
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n = sft;
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}
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while (n > 1)
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{
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unsigned int half = n/2;
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if (ftid < half)
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data[ftid] = op(data[ftid], data[n - ftid - 1]);
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__syncthreads();
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n = n - half;
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}
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}
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};
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}}}
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//! @endcond
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#endif /* OPENCV_CUDA_DEVICE_BLOCK_HPP */
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