diff --git a/modules/gpu/include/opencv2/gpu/gpu.hpp b/modules/gpu/include/opencv2/gpu/gpu.hpp
index 7377f52578..1b09f4c777 100644
--- a/modules/gpu/include/opencv2/gpu/gpu.hpp
+++ b/modules/gpu/include/opencv2/gpu/gpu.hpp
@@ -860,6 +860,9 @@ namespace cv
         //! counts non-zero array elements
         CV_EXPORTS int countNonZero(const GpuMat& src, GpuMat& buf);
 
+        //! reduces a matrix to a vector
+        CV_EXPORTS void reduce(const GpuMat& mtx, GpuMat& vec, int dim, int reduceOp, int dtype = -1, Stream& stream = Stream::Null());
+
 
         ///////////////////////////// Calibration 3D //////////////////////////////////
 
diff --git a/modules/gpu/src/cuda/matrix_reductions.cu b/modules/gpu/src/cuda/matrix_reductions.cu
index 60e6c886e8..722be26187 100644
--- a/modules/gpu/src/cuda/matrix_reductions.cu
+++ b/modules/gpu/src/cuda/matrix_reductions.cu
@@ -1804,4 +1804,278 @@ namespace cv { namespace gpu { namespace mathfunc
     template void sqrSumCaller<short>(const DevMem2D, PtrStep, double*, int);
     template void sqrSumCaller<int>(const DevMem2D, PtrStep, double*, int);
     template void sqrSumCaller<float>(const DevMem2D, PtrStep, double*, int);
+
+    //////////////////////////////////////////////////////////////////////////////
+    // reduce
+
+    template <typename S> struct SumReductor
+    {
+        __device__ __forceinline__ S startValue() const
+        {
+            return 0;
+        }
+
+        __device__ __forceinline__ S operator ()(volatile S a, volatile S b) const
+        {
+            return a + b;
+        }
+
+        __device__ __forceinline S result(S r, double) const
+        {
+            return r;
+        }
+    };
+
+    template <typename S> struct AvgReductor
+    {
+        __device__ __forceinline__ S startValue() const
+        {
+            return 0;
+        }
+
+        __device__ __forceinline__ S operator ()(volatile S a, volatile S b) const
+        {
+            return a + b;
+        }
+
+        __device__ __forceinline double result(S r, double sz) const
+        {
+            return r / sz;
+        }
+    };
+
+    template <typename S> struct MinReductor
+    {
+        __device__ __forceinline__ S startValue() const
+        {
+            return numeric_limits<S>::max();
+        }
+
+        template <typename T> __device__ __forceinline__ T operator ()(volatile T a, volatile T b) const
+        {
+            return saturate_cast<T>(::min(a, b));
+        }
+        __device__ __forceinline__ float operator ()(volatile float a, volatile float b) const
+        {
+            return ::fmin(a, b);
+        }
+
+        __device__ __forceinline S result(S r, double) const
+        {
+            return r;
+        }
+    };
+
+    template <typename S> struct MaxReductor
+    {
+        __device__ __forceinline__ S startValue() const
+        {
+            return numeric_limits<S>::min();
+        }
+
+        template <typename T> __device__ __forceinline__ int operator ()(volatile T a, volatile T b) const
+        {
+            return ::max(a, b);
+        }
+        __device__ __forceinline__ float operator ()(volatile float a, volatile float b) const
+        {
+            return ::fmax(a, b);
+        }
+
+        __device__ __forceinline S result(S r, double) const
+        {
+            return r;
+        }
+    };
+
+    template <class Op, typename T, typename S, typename D> __global__ void reduceRows(const DevMem2D_<T> src, D* dst, const Op op)
+    {
+        __shared__ S smem[16 * 16];
+
+        const int x = blockIdx.x * 16 + threadIdx.x;
+
+        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();
+
+            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));
+            }
+        }
+    }
+
+    template <template <typename> class Op, typename T, typename S, typename D> void reduceRows_caller(const DevMem2D_<T>& src, DevMem2D_<D> dst, cudaStream_t stream)
+    {
+        const dim3 block(16, 16);
+        const dim3 grid(divUp(src.cols, block.x));
+
+        Op<S> op;
+        reduceRows<Op<S>, T, S, D><<<grid, block, 0, stream>>>(src, dst.data, op);
+        cudaSafeCall( cudaGetLastError() );
+
+        if (stream == 0)
+            cudaSafeCall( cudaDeviceSynchronize() );
+
+    }
+
+    template <typename T, typename S, typename D> void reduceRows_gpu(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream)
+    {
+        typedef void (*caller_t)(const DevMem2D_<T>& src, DevMem2D_<D> dst, cudaStream_t stream);
+
+        static const caller_t callers[] = 
+        {
+            reduceRows_caller<SumReductor, T, S, D>, 
+            reduceRows_caller<AvgReductor, T, S, D>, 
+            reduceRows_caller<MaxReductor, T, S, D>, 
+            reduceRows_caller<MinReductor, T, S, D>
+        };
+
+        callers[reduceOp](static_cast< DevMem2D_<T> >(src), static_cast< DevMem2D_<D> >(dst), stream);
+    }
+
+    template void reduceRows_gpu<uchar, int, uchar>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<uchar, int, int>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<uchar, int, float>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);  
+
+    template void reduceRows_gpu<ushort, int, ushort>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<ushort, int, int>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<ushort, int, float>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream); 
+
+    template void reduceRows_gpu<short, int, short>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<short, int, int>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<short, int, float>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream); 
+
+    template void reduceRows_gpu<int, int, int>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceRows_gpu<int, int, float>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+    template void reduceRows_gpu<float, float, float>(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+
+
+
+    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];
+
+        const int y = blockIdx.x;
+
+        const T* src_row = src.ptr(y);
+
+        S myVal[cn];
+
+        #pragma unroll
+        for (int c = 0; c < cn; ++c)
+            myVal[c] = op.startValue();
+
+        for (int x = threadIdx.x; x < src.cols; x += 256)
+        {
+            #pragma unroll
+            for (int c = 0; c < cn; ++c)
+                myVal[c] = op(myVal[c], src_row[x * cn + c]);
+        }
+
+        #pragma unroll
+        for (int c = 0; c < cn; ++c)
+            smem[c * 256 + threadIdx.x] = myVal[c];
+        __syncthreads();
+
+        if (threadIdx.x < 128)
+        {
+            #pragma unroll
+            for (int c = 0; c < cn; ++c)
+                smem[c * 256 + threadIdx.x] = op(smem[c * 256 + threadIdx.x], smem[c * 256 + threadIdx.x + 128]);
+        }
+        __syncthreads();
+
+        if (threadIdx.x < 64)
+        {
+            #pragma unroll
+            for (int c = 0; c < cn; ++c)
+                smem[c * 256 + threadIdx.x] = op(smem[c * 256 + threadIdx.x], smem[c * 256 + threadIdx.x + 64]);
+        }
+        __syncthreads();
+
+        volatile S* sdata = smem;
+
+        if (threadIdx.x < 32)
+        {
+            #pragma unroll
+            for (int c = 0; c < cn; ++c)
+            {
+                sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 32]);
+                sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 16]);
+                sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 8]);
+                sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 4]);
+                sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 2]);
+                sdata[c * 256 + threadIdx.x] = op(sdata[c * 256 + threadIdx.x], sdata[c * 256 + threadIdx.x + 1]);
+            }
+        }
+        __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));
+        }
+    }
+
+    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)
+    {
+        const dim3 block(256);
+        const dim3 grid(src.rows);
+
+        Op<S> op;
+        reduceCols<cn, Op<S>, T, S, D><<<grid, block, 0, stream>>>(src, dst.data, op);
+        cudaSafeCall( cudaGetLastError() );
+
+        if (stream == 0)
+            cudaSafeCall( cudaDeviceSynchronize() );
+
+    }
+
+    template <typename T, typename S, typename D> void reduceCols_gpu(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream)
+    {
+        typedef void (*caller_t)(const DevMem2D_<T>& src, DevMem2D_<D> dst, cudaStream_t stream);
+
+        static const caller_t callers[4][4] = 
+        {
+            {reduceCols_caller<1, SumReductor, T, S, D>, reduceCols_caller<1, AvgReductor, T, S, D>, reduceCols_caller<1, MaxReductor, T, S, D>, reduceCols_caller<1, MinReductor, T, S, D>},
+            {reduceCols_caller<2, SumReductor, T, S, D>, reduceCols_caller<2, AvgReductor, T, S, D>, reduceCols_caller<2, MaxReductor, T, S, D>, reduceCols_caller<2, MinReductor, T, S, D>},
+            {reduceCols_caller<3, SumReductor, T, S, D>, reduceCols_caller<3, AvgReductor, T, S, D>, reduceCols_caller<3, MaxReductor, T, S, D>, reduceCols_caller<3, MinReductor, T, S, D>},
+            {reduceCols_caller<4, SumReductor, T, S, D>, reduceCols_caller<4, AvgReductor, T, S, D>, reduceCols_caller<4, MaxReductor, T, S, D>, reduceCols_caller<4, MinReductor, T, S, D>},
+        };
+
+        callers[cn - 1][reduceOp](static_cast< DevMem2D_<T> >(src), static_cast< DevMem2D_<D> >(dst), stream);
+    }
+
+    template void reduceCols_gpu<uchar, int, uchar>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceCols_gpu<uchar, int, int>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template void reduceCols_gpu<uchar, int, float>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+    template void reduceCols_gpu<ushort, int, ushort>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream); 
+    template void reduceCols_gpu<ushort, int, int>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);                  
+    template void reduceCols_gpu<ushort, int, float>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+    template void reduceCols_gpu<short, int, short>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);  
+    template void reduceCols_gpu<short, int, int>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);                  
+    template void reduceCols_gpu<short, int, float>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);  
+
+    template void reduceCols_gpu<int, int, int>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);                  
+    template void reduceCols_gpu<int, int, float>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+    template void reduceCols_gpu<float, float, float>(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
  }}}
\ No newline at end of file
diff --git a/modules/gpu/src/matrix_reductions.cpp b/modules/gpu/src/matrix_reductions.cpp
index 04ed2c1d7c..11cf4bcfcf 100644
--- a/modules/gpu/src/matrix_reductions.cpp
+++ b/modules/gpu/src/matrix_reductions.cpp
@@ -63,6 +63,7 @@ void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const G
 void cv::gpu::minMaxLoc(const GpuMat&, double*, double*, Point*, Point*, const GpuMat&, GpuMat&, GpuMat&) { throw_nogpu(); }
 int cv::gpu::countNonZero(const GpuMat&) { throw_nogpu(); return 0; }
 int cv::gpu::countNonZero(const GpuMat&, GpuMat&) { throw_nogpu(); return 0; }
+void cv::gpu::reduce(const GpuMat&, GpuMat&, int, int, int, Stream&) { throw_nogpu(); }
 
 #else
 
@@ -598,4 +599,150 @@ int cv::gpu::countNonZero(const GpuMat& src, GpuMat& buf)
     return caller(src, buf);
 }
 
+//////////////////////////////////////////////////////////////////////////////
+// reduce
+
+namespace cv { namespace gpu { namespace mathfunc {
+    template <typename T, typename S, typename D> void reduceRows_gpu(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+    template <typename T, typename S, typename D> void reduceCols_gpu(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+}}}
+
+void cv::gpu::reduce(const GpuMat& src, GpuMat& dst, int dim, int reduceOp, int dtype, Stream& stream)
+{
+    using namespace cv::gpu::mathfunc;
+    CV_Assert(src.depth() <= CV_32F && src.channels() <= 4 && dtype <= CV_32F);
+    CV_Assert(dim == 0 || dim == 1);
+    CV_Assert(reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG || reduceOp == CV_REDUCE_MAX || reduceOp == CV_REDUCE_MIN);
+
+    if (dtype < 0)
+        dtype = src.depth();
+
+    dst.create(1, dim == 0 ? src.cols : src.rows, CV_MAKETYPE(dtype, src.channels()));
+
+    if (dim == 0)
+    {
+        typedef void (*caller_t)(const DevMem2D& src, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+        static const caller_t callers[6][6] = 
+        {
+            {
+                reduceRows_gpu<unsigned char, int, unsigned char>,
+                0/*reduceRows_gpu<unsigned char, int, signed char>*/,
+                0/*reduceRows_gpu<unsigned char, int, unsigned short>*/,
+                0/*reduceRows_gpu<unsigned char, int, short>*/,
+                reduceRows_gpu<unsigned char, int, int>,
+                reduceRows_gpu<unsigned char, int, float>
+            },
+            {
+                0/*reduceRows_gpu<signed char, int, unsigned char>*/,
+                0/*reduceRows_gpu<signed char, int, signed char>*/,
+                0/*reduceRows_gpu<signed char, int, unsigned short>*/,
+                0/*reduceRows_gpu<signed char, int, short>*/,
+                0/*reduceRows_gpu<signed char, int, int>*/,
+                0/*reduceRows_gpu<signed char, int, float>*/
+            },
+            {
+                0/*reduceRows_gpu<unsigned short, int, unsigned char>*/,
+                0/*reduceRows_gpu<unsigned short, int, signed char>*/,
+                reduceRows_gpu<unsigned short, int, unsigned short>,
+                0/*reduceRows_gpu<unsigned short, int, short>*/,
+                reduceRows_gpu<unsigned short, int, int>,
+                reduceRows_gpu<unsigned short, int, float>
+            },
+            {
+                0/*reduceRows_gpu<short, int, unsigned char>*/,
+                0/*reduceRows_gpu<short, int, signed char>*/,
+                0/*reduceRows_gpu<short, int, unsigned short>*/,
+                reduceRows_gpu<short, int, short>,
+                reduceRows_gpu<short, int, int>,
+                reduceRows_gpu<short, int, float>
+            },
+            {
+                0/*reduceRows_gpu<int, int, unsigned char>*/,
+                0/*reduceRows_gpu<int, int, signed char>*/,
+                0/*reduceRows_gpu<int, int, unsigned short>*/,
+                0/*reduceRows_gpu<int, int, short>*/,
+                reduceRows_gpu<int, int, int>,
+                reduceRows_gpu<int, int, float>
+            },
+            {
+                0/*reduceRows_gpu<float, float, unsigned char>*/,
+                0/*reduceRows_gpu<float, float, signed char>*/,
+                0/*reduceRows_gpu<float, float, unsigned short>*/,
+                0/*reduceRows_gpu<float, float, short>*/,
+                0/*reduceRows_gpu<float, float, int>*/,
+                reduceRows_gpu<float, float, float>
+            }
+        };
+
+        const caller_t func = callers[src.depth()][dst.depth()];
+        if (!func)
+            CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
+
+        func(src.reshape(1), dst.reshape(1), reduceOp, StreamAccessor::getStream(stream));
+    }
+    else
+    {
+        typedef void (*caller_t)(const DevMem2D& src, int cn, const DevMem2D& dst, int reduceOp, cudaStream_t stream);
+
+        static const caller_t callers[6][6] = 
+        {
+            {
+                reduceCols_gpu<unsigned char, int, unsigned char>,
+                0/*reduceCols_gpu<unsigned char, int, signed char>*/,
+                0/*reduceCols_gpu<unsigned char, int, unsigned short>*/,
+                0/*reduceCols_gpu<unsigned char, int, short>*/,
+                reduceCols_gpu<unsigned char, int, int>,
+                reduceCols_gpu<unsigned char, int, float>
+            },
+            {
+                0/*reduceCols_gpu<signed char, int, unsigned char>*/,
+                0/*reduceCols_gpu<signed char, int, signed char>*/,
+                0/*reduceCols_gpu<signed char, int, unsigned short>*/,
+                0/*reduceCols_gpu<signed char, int, short>*/,
+                0/*reduceCols_gpu<signed char, int, int>*/,
+                0/*reduceCols_gpu<signed char, int, float>*/
+            },
+            {
+                0/*reduceCols_gpu<unsigned short, int, unsigned char>*/,
+                0/*reduceCols_gpu<unsigned short, int, signed char>*/,
+                reduceCols_gpu<unsigned short, int, unsigned short>,
+                0/*reduceCols_gpu<unsigned short, int, short>*/,
+                reduceCols_gpu<unsigned short, int, int>,
+                reduceCols_gpu<unsigned short, int, float>
+            },
+            {
+                0/*reduceCols_gpu<short, int, unsigned char>*/,
+                0/*reduceCols_gpu<short, int, signed char>*/,
+                0/*reduceCols_gpu<short, int, unsigned short>*/,
+                reduceCols_gpu<short, int, short>,
+                reduceCols_gpu<short, int, int>,
+                reduceCols_gpu<short, int, float>
+            },
+            {
+                0/*reduceCols_gpu<int, int, unsigned char>*/,
+                0/*reduceCols_gpu<int, int, signed char>*/,
+                0/*reduceCols_gpu<int, int, unsigned short>*/,
+                0/*reduceCols_gpu<int, int, short>*/,
+                reduceCols_gpu<int, int, int>,
+                reduceCols_gpu<int, int, float>
+            },
+            {
+                0/*reduceCols_gpu<float, unsigned char>*/,
+                0/*reduceCols_gpu<float, signed char>*/,
+                0/*reduceCols_gpu<float, unsigned short>*/,
+                0/*reduceCols_gpu<float, short>*/,
+                0/*reduceCols_gpu<float, int>*/,
+                reduceCols_gpu<float, float, float>
+            }
+        };
+
+        const caller_t func = callers[src.depth()][dst.depth()];
+        if (!func)
+            CV_Error(CV_StsUnsupportedFormat, "Unsupported combination of input and output array formats");
+
+        func(src, src.channels(), dst, reduceOp, StreamAccessor::getStream(stream));        
+    }
+}
+
 #endif
diff --git a/modules/gpu/test/test_arithm.cpp b/modules/gpu/test/test_arithm.cpp
index 31365ca1f7..34cadf32ed 100644
--- a/modules/gpu/test/test_arithm.cpp
+++ b/modules/gpu/test/test_arithm.cpp
@@ -1788,4 +1788,76 @@ INSTANTIATE_TEST_CASE_P(Arithm, AddWeighted, testing::Combine(
                         testing::ValuesIn(types(CV_8U, CV_64F, 1, 1)),
                         testing::ValuesIn(types(CV_8U, CV_64F, 1, 1))));
 
+//////////////////////////////////////////////////////////////////////////////
+// reduce
+
+struct Reduce : testing::TestWithParam< std::tr1::tuple<cv::gpu::DeviceInfo, int, int, int> >
+{
+    cv::gpu::DeviceInfo devInfo;
+    int type;
+    int dim;
+    int reduceOp;
+
+    cv::Size size;
+    cv::Mat src;
+
+    cv::Mat dst_gold;
+
+    virtual void SetUp() 
+    {
+        devInfo = std::tr1::get<0>(GetParam());
+        type = std::tr1::get<1>(GetParam());
+        dim = std::tr1::get<2>(GetParam());
+        reduceOp = std::tr1::get<3>(GetParam());
+
+        cv::gpu::setDevice(devInfo.deviceID());
+
+        cv::RNG& rng = cvtest::TS::ptr()->get_rng();
+
+        size = cv::Size(rng.uniform(100, 400), rng.uniform(100, 400));
+
+        src = cvtest::randomMat(rng, size, type, 0.0, 255.0, false);
+
+        cv::reduce(src, dst_gold, dim, reduceOp, reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? CV_32F : CV_MAT_DEPTH(type));
+
+        if (dim == 1)
+        {
+            dst_gold.cols = dst_gold.rows;
+            dst_gold.rows = 1;
+            dst_gold.step = dst_gold.cols * dst_gold.elemSize();
+        }
+    }
+};
+
+TEST_P(Reduce, Accuracy) 
+{
+    static const char* reduceOpStrs[] = {"CV_REDUCE_SUM", "CV_REDUCE_AVG", "CV_REDUCE_MAX", "CV_REDUCE_MIN"};
+    const char* reduceOpStr = reduceOpStrs[reduceOp];
+
+    PRINT_PARAM(devInfo);
+    PRINT_TYPE(type);
+    PRINT_PARAM(dim);
+    PRINT_PARAM(reduceOpStr);
+    PRINT_PARAM(size);
+
+    cv::Mat dst;
+    
+    ASSERT_NO_THROW(
+        cv::gpu::GpuMat dev_dst;
+
+        cv::gpu::reduce(cv::gpu::GpuMat(src), dev_dst, dim, reduceOp, reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? CV_32F : CV_MAT_DEPTH(type));
+
+        dev_dst.download(dst);
+    );
+
+    double norm = reduceOp == CV_REDUCE_SUM || reduceOp == CV_REDUCE_AVG ? 1e-1 : 0.0;
+    EXPECT_MAT_NEAR(dst_gold, dst, norm);
+}
+
+INSTANTIATE_TEST_CASE_P(Arithm, Reduce, testing::Combine(
+                        testing::ValuesIn(devices()),
+                        testing::Values(CV_8UC1, CV_8UC3, CV_8UC4, CV_16UC1, CV_16UC3, CV_16UC4, CV_32FC1, CV_32FC3, CV_32FC4),
+                        testing::Values(0, 1),
+                        testing::Values((int)CV_REDUCE_SUM, (int)CV_REDUCE_AVG, (int)CV_REDUCE_MAX, (int)CV_REDUCE_MIN)));
+
 #endif // HAVE_CUDA