block.hpp 8.05 KB
Newer Older
wester committed
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42
/*M///////////////////////////////////////////////////////////////////////////////////////
//
//  IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
//
//  By downloading, copying, installing or using the software you agree to this license.
//  If you do not agree to this license, do not download, install,
//  copy or use the software.
//
//
//                           License Agreement
//                For Open Source Computer Vision Library
//
// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
// Third party copyrights are property of their respective owners.
//
// Redistribution and use in source and binary forms, with or without modification,
// are permitted provided that the following conditions are met:
//
//   * Redistribution's of source code must retain the above copyright notice,
//     this list of conditions and the following disclaimer.
//
//   * Redistribution's 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.
//
//   * The name of the copyright holders may not 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 the Intel Corporation or contributors 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.
//
//M*/

wester committed
43 44
#ifndef __OPENCV_GPU_DEVICE_BLOCK_HPP__
#define __OPENCV_GPU_DEVICE_BLOCK_HPP__
wester committed
45

wester committed
46
namespace cv { namespace gpu { namespace device
wester committed
47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202
{
    struct Block
    {
        static __device__ __forceinline__ unsigned int id()
        {
            return blockIdx.x;
        }

        static __device__ __forceinline__ unsigned int stride()
        {
            return blockDim.x * blockDim.y * blockDim.z;
        }

        static __device__ __forceinline__ void sync()
        {
            __syncthreads();
        }

        static __device__ __forceinline__ int flattenedThreadId()
        {
            return threadIdx.z * blockDim.x * blockDim.y + threadIdx.y * blockDim.x + threadIdx.x;
        }

        template<typename It, typename T>
        static __device__ __forceinline__ void fill(It beg, It end, const T& value)
        {
            int STRIDE = stride();
            It t = beg + flattenedThreadId();

            for(; t < end; t += STRIDE)
                *t = value;
        }

        template<typename OutIt, typename T>
        static __device__ __forceinline__ void yota(OutIt beg, OutIt end, T value)
        {
            int STRIDE = stride();
            int tid = flattenedThreadId();
            value += tid;

            for(OutIt t = beg + tid; t < end; t += STRIDE, value += STRIDE)
                *t = value;
        }

        template<typename InIt, typename OutIt>
        static __device__ __forceinline__ void copy(InIt beg, InIt end, OutIt out)
        {
            int STRIDE = stride();
            InIt  t = beg + flattenedThreadId();
            OutIt o = out + (t - beg);

            for(; t < end; t += STRIDE, o += STRIDE)
                *o = *t;
        }

        template<typename InIt, typename OutIt, class UnOp>
        static __device__ __forceinline__ void transfrom(InIt beg, InIt end, OutIt out, UnOp op)
        {
            int STRIDE = stride();
            InIt  t = beg + flattenedThreadId();
            OutIt o = out + (t - beg);

            for(; t < end; t += STRIDE, o += STRIDE)
                *o = op(*t);
        }

        template<typename InIt1, typename InIt2, typename OutIt, class BinOp>
        static __device__ __forceinline__ void transfrom(InIt1 beg1, InIt1 end1, InIt2 beg2, OutIt out, BinOp op)
        {
            int STRIDE = stride();
            InIt1 t1 = beg1 + flattenedThreadId();
            InIt2 t2 = beg2 + flattenedThreadId();
            OutIt o  = out + (t1 - beg1);

            for(; t1 < end1; t1 += STRIDE, t2 += STRIDE, o += STRIDE)
                *o = op(*t1, *t2);
        }

        template<int CTA_SIZE, typename T, class BinOp>
        static __device__ __forceinline__ void reduce(volatile T* buffer, BinOp op)
        {
            int tid = flattenedThreadId();
            T val =  buffer[tid];

            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }

            if (tid < 32)
            {
                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
            }
        }

        template<int CTA_SIZE, typename T, class BinOp>
        static __device__ __forceinline__ T reduce(volatile T* buffer, T init, BinOp op)
        {
            int tid = flattenedThreadId();
            T val =  buffer[tid] = init;
            __syncthreads();

            if (CTA_SIZE >= 1024) { if (tid < 512) buffer[tid] = val = op(val, buffer[tid + 512]); __syncthreads(); }
            if (CTA_SIZE >=  512) { if (tid < 256) buffer[tid] = val = op(val, buffer[tid + 256]); __syncthreads(); }
            if (CTA_SIZE >=  256) { if (tid < 128) buffer[tid] = val = op(val, buffer[tid + 128]); __syncthreads(); }
            if (CTA_SIZE >=  128) { if (tid <  64) buffer[tid] = val = op(val, buffer[tid +  64]); __syncthreads(); }

            if (tid < 32)
            {
                if (CTA_SIZE >=   64) { buffer[tid] = val = op(val, buffer[tid +  32]); }
                if (CTA_SIZE >=   32) { buffer[tid] = val = op(val, buffer[tid +  16]); }
                if (CTA_SIZE >=   16) { buffer[tid] = val = op(val, buffer[tid +   8]); }
                if (CTA_SIZE >=    8) { buffer[tid] = val = op(val, buffer[tid +   4]); }
                if (CTA_SIZE >=    4) { buffer[tid] = val = op(val, buffer[tid +   2]); }
                if (CTA_SIZE >=    2) { buffer[tid] = val = op(val, buffer[tid +   1]); }
            }
            __syncthreads();
            return buffer[0];
        }

        template <typename T, class BinOp>
        static __device__ __forceinline__ void reduce_n(T* data, unsigned int n, BinOp op)
        {
            int ftid = flattenedThreadId();
            int sft = stride();

            if (sft < n)
            {
                for (unsigned int i = sft + ftid; i < n; i += sft)
                    data[ftid] = op(data[ftid], data[i]);

                __syncthreads();

                n = sft;
            }

            while (n > 1)
            {
                unsigned int half = n/2;

                if (ftid < half)
                    data[ftid] = op(data[ftid], data[n - ftid - 1]);

                __syncthreads();

                n = n - half;
            }
        }
    };
}}}

wester committed
203
#endif /* __OPENCV_GPU_DEVICE_BLOCK_HPP__ */