block.hpp

[詳解]

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#ifndef OPENCV_CUDA_DEVICE_BLOCK_HPP




#define OPENCV_CUDA_DEVICE_BLOCK_HPP












namespace

cv
{
namespace
cuda {
namespace
device


{



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
transform(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
transform(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;



}



}



};


}}}











#endif

/* OPENCV_CUDA_DEVICE_BLOCK_HPP */