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optimized gpu::minMax a little

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This commit is contained in:
Alexey Spizhevoy 2010-11-24 09:19:11 +00:00
parent 13d18d65a8
commit 68a7fc9868
1 changed files with 29 additions and 22 deletions
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51
modules/gpu/src/cuda/mathfunc.cu
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@ -400,6 +400,16 @@ namespace cv { namespace gpu { namespace mathfunc
// Min max // Min max
enum { MIN, MAX }; enum { MIN, MAX };
template <typename T> struct MinMaxTypeTraits {};
template <> struct MinMaxTypeTraits<unsigned char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<signed char> { typedef int best_type; };
template <> struct MinMaxTypeTraits<unsigned short> { typedef int best_type; };
template <> struct MinMaxTypeTraits<signed short> { typedef int best_type; };
template <> struct MinMaxTypeTraits<int> { typedef int best_type; };
template <> struct MinMaxTypeTraits<float> { typedef float best_type; };
template <> struct MinMaxTypeTraits<double> { typedef double best_type; };
template <typename T, int op> struct Cmp {}; template <typename T, int op> struct Cmp {};
template <typename T> template <typename T>
@ -407,9 +417,7 @@ namespace cv { namespace gpu { namespace mathfunc
{ {
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval) static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval)
{ {
T val = optval[tid + offset]; optval[tid] = min(optval[tid], optval[tid + offset]);
if (val < optval[tid]) optval[tid] = val;
//optval[tid] = min(optval[tid], optval[tid + offset]);
} }
}; };
@ -418,17 +426,16 @@ namespace cv { namespace gpu { namespace mathfunc
{ {
static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval) static __device__ void call(unsigned int tid, unsigned int offset, volatile T* optval)
{ {
T val = optval[tid + offset]; optval[tid] = max(optval[tid], optval[tid + offset]);
if (val > optval[tid]) optval[tid] = val;
//optval[tid] = max(optval[tid], optval[tid + offset]);
} }
}; };
template <int nthreads, typename Cmp, typename T> template <int nthreads, int op, typename T>
__global__ void opt_kernel(int cols, int rows, const PtrStep src, PtrStep optval) __global__ void opt_kernel(int cols, int rows, const PtrStep src, PtrStep optval)
{ {
__shared__ T soptval[nthreads]; typedef typename MinMaxTypeTraits<T>::best_type best_type;
__shared__ best_type soptval[nthreads];
unsigned int x0 = blockIdx.x * blockDim.x; unsigned int x0 = blockIdx.x * blockDim.x;
unsigned int y0 = blockIdx.y * blockDim.y; unsigned int y0 = blockIdx.y * blockDim.y;
@ -441,21 +448,21 @@ namespace cv { namespace gpu { namespace mathfunc
__syncthreads(); __syncthreads();
if (nthreads >= 512) if (tid < 256) { Cmp::call(tid, 256, soptval); __syncthreads(); } if (nthreads >= 512) if (tid < 256) { Cmp<best_type, op>::call(tid, 256, soptval); __syncthreads(); }
if (nthreads >= 256) if (tid < 128) { Cmp::call(tid, 128, soptval); __syncthreads(); } if (nthreads >= 256) if (tid < 128) { Cmp<best_type, op>::call(tid, 128, soptval); __syncthreads(); }
if (nthreads >= 128) if (tid < 64) { Cmp::call(tid, 64, soptval); __syncthreads(); } if (nthreads >= 128) if (tid < 64) { Cmp<best_type, op>::call(tid, 64, soptval); __syncthreads(); }
if (tid < 32) if (tid < 32)
{ {
if (nthreads >= 64) Cmp::call(tid, 32, soptval); if (nthreads >= 64) Cmp<best_type, op>::call(tid, 32, soptval);
if (nthreads >= 32) Cmp::call(tid, 16, soptval); if (nthreads >= 32) Cmp<best_type, op>::call(tid, 16, soptval);
if (nthreads >= 16) Cmp::call(tid, 8, soptval); if (nthreads >= 16) Cmp<best_type, op>::call(tid, 8, soptval);
if (nthreads >= 8) Cmp::call(tid, 4, soptval); if (nthreads >= 8) Cmp<best_type, op>::call(tid, 4, soptval);
if (nthreads >= 4) Cmp::call(tid, 2, soptval); if (nthreads >= 4) Cmp<best_type, op>::call(tid, 2, soptval);
if (nthreads >= 2) Cmp::call(tid, 1, soptval); if (nthreads >= 2) Cmp<best_type, op>::call(tid, 1, soptval);
} }
if (tid == 0) ((T*)optval.ptr(blockIdx.y))[blockIdx.x] = soptval[0]; if (tid == 0) ((T*)optval.ptr(blockIdx.y))[blockIdx.x] = (T)soptval[0];
} }
@ -483,16 +490,16 @@ namespace cv { namespace gpu { namespace mathfunc
dim3 cursize(src.cols, src.rows); dim3 cursize(src.cols, src.rows);
dim3 grid(divUp(cursize.x, threads.x), divUp(cursize.y, threads.y)); dim3 grid(divUp(cursize.x, threads.x), divUp(cursize.y, threads.y));
opt_kernel<256, Cmp<T, MIN>, T><<<grid, threads>>>(cursize.x, cursize.y, src, minval_buf[curbuf]); opt_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, src, minval_buf[curbuf]);
opt_kernel<256, Cmp<T, MAX>, T><<<grid, threads>>>(cursize.x, cursize.y, src, maxval_buf[curbuf]); opt_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, src, maxval_buf[curbuf]);
cursize = grid; cursize = grid;
while (cursize.x > 1 || cursize.y > 1) while (cursize.x > 1 || cursize.y > 1)
{ {
grid.x = divUp(cursize.x, threads.x); grid.x = divUp(cursize.x, threads.x);
grid.y = divUp(cursize.y, threads.y); grid.y = divUp(cursize.y, threads.y);
opt_kernel<256, Cmp<T, MIN>, T><<<grid, threads>>>(cursize.x, cursize.y, minval_buf[curbuf], minval_buf[1 - curbuf]); opt_kernel<256, MIN, T><<<grid, threads>>>(cursize.x, cursize.y, minval_buf[curbuf], minval_buf[1 - curbuf]);
opt_kernel<256, Cmp<T, MAX>, T><<<grid, threads>>>(cursize.x, cursize.y, maxval_buf[curbuf], maxval_buf[1 - curbuf]); opt_kernel<256, MAX, T><<<grid, threads>>>(cursize.x, cursize.y, maxval_buf[curbuf], maxval_buf[1 - curbuf]);
curbuf = 1 - curbuf; curbuf = 1 - curbuf;
cursize = grid; cursize = grid;
} }