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added performance tests for gpu::reduce

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This commit is contained in:
Vladislav Vinogradov
2011-09-26 08:14:31 +00:00
parent 66b41b67f9
commit 67a9b79433
3 changed files with 111 additions and 21 deletions
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65
modules/gpu/src/cuda/matrix_reductions.cu
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@@ -1894,27 +1894,29 @@ namespace cv { namespace gpu { namespace mathfunc
const int x = blockIdx.x * 16 + threadIdx.x;
S myVal = op.startValue();
if (x < src.cols)
{
S myVal = op.startValue();
for (int y = threadIdx.y; y < src.rows; y += 16)
myVal = op(myVal, src.ptr(y)[x]);
}
smem[threadIdx.y * 16 + threadIdx.x] = myVal;
__syncthreads();
smem[threadIdx.x * 16 + threadIdx.y] = myVal;
__syncthreads();
if (threadIdx.y == 0)
{
myVal = smem[threadIdx.x];
#pragma unroll
for (int i = 1; i < 16; ++i)
myVal = op(myVal, smem[i * 16 + threadIdx.x]);
dst[x] = saturate_cast<D>(op.result(myVal, src.rows));
}
if (threadIdx.x < 8)
{
volatile S* srow = smem + threadIdx.y * 16;
srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 8]);
srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 4]);
srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 2]);
srow[threadIdx.x] = op(srow[threadIdx.x], srow[threadIdx.x + 1]);
}
__syncthreads();
if (threadIdx.y == 0 && x < src.cols)
dst[x] = saturate_cast<D>(op.result(smem[threadIdx.x * 16], src.rows));
}
template <template <typename> class Op, typename T, typename S, typename D> void reduceRows_caller(const DevMem2D_<T>& src, DevMem2D_<D> dst, cudaStream_t stream)
@@ -1965,7 +1967,6 @@ namespace cv { namespace gpu { namespace mathfunc
template <int cn, class Op, typename T, typename S, typename D> __global__ void reduceCols(const DevMem2D_<T> src, D* dst, const Op op)
{
__shared__ S smem[256 * cn];
@@ -1980,6 +1981,9 @@ namespace cv { namespace gpu { namespace mathfunc
for (int c = 0; c < cn; ++c)
myVal[c] = op.startValue();
#if __CUDA_ARCH__ >= 200
// For cc >= 2.0 prefer L1 cache
for (int x = threadIdx.x; x < src.cols; x += 256)
{
#pragma unroll
@@ -1987,6 +1991,29 @@ namespace cv { namespace gpu { namespace mathfunc
myVal[c] = op(myVal[c], src_row[x * cn + c]);
}
#else // __CUDA_ARCH__ >= 200
// For older arch use shared memory for cache
for (int x = 0; x < src.cols; x += 256)
{
#pragma unroll
for (int c = 0; c < cn; ++c)
{
smem[c * 256 + threadIdx.x] = op.startValue();
const int load_x = x * cn + c * 256 + threadIdx.x;
if (load_x < src.cols * cn)
smem[c * 256 + threadIdx.x] = src_row[load_x];
}
__syncthreads();
#pragma unroll
for (int c = 0; c < cn; ++c)
myVal[c] = op(myVal[c], smem[threadIdx.x * cn + c]);
__syncthreads();
}
#endif // __CUDA_ARCH__ >= 200
#pragma unroll
for (int c = 0; c < cn; ++c)
smem[c * 256 + threadIdx.x] = myVal[c];
@@ -2025,12 +2052,8 @@ namespace cv { namespace gpu { namespace mathfunc
}
__syncthreads();
if (threadIdx.x == 0)
{
#pragma unroll
for (int c = 0; c < cn; ++c)
dst[y * cn + c] = saturate_cast<D>(op.result(smem[c * 256], src.cols));
}
if (threadIdx.x < cn)
dst[y * cn + threadIdx.x] = saturate_cast<D>(op.result(smem[threadIdx.x * 256], src.cols));
}
template <int cn, template <typename> class Op, typename T, typename S, typename D> void reduceCols_caller(const DevMem2D_<T>& src, DevMem2D_<D> dst, cudaStream_t stream)