259 lines
8.8 KiB
C++
259 lines
8.8 KiB
C++
/*M///////////////////////////////////////////////////////////////////////////////////////
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//
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// IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING.
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//
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// By downloading, copying, installing or using the software you agree to this license.
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// If you do not agree to this license, do not download, install,
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// copy or use the software.
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//
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//
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// License Agreement
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// For Open Source Computer Vision Library
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//
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// Copyright (C) 2000-2008, Intel Corporation, all rights reserved.
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// Copyright (C) 2009, Willow Garage Inc., all rights reserved.
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// Third party copyrights are property of their respective owners.
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//
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// Redistribution and use in source and binary forms, with or without modification,
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// are permitted provided that the following conditions are met:
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//
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// * Redistribution's of source code must retain the above copyright notice,
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// this list of conditions and the following disclaimer.
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//
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// * Redistribution's in binary form must reproduce the above copyright notice,
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// this list of conditions and the following disclaimer in the documentation
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// and/or other materials provided with the distribution.
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//
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// * The name of the copyright holders may not be used to endorse or promote products
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// derived from this software without specific prior written permission.
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//
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// This software is provided by the copyright holders and contributors "as is" and
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// any express or implied warranties, including, but not limited to, the implied
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// warranties of merchantability and fitness for a particular purpose are disclaimed.
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// In no event shall the Intel Corporation or contributors be liable for any direct,
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// indirect, incidental, special, exemplary, or consequential damages
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// (including, but not limited to, procurement of substitute goods or services;
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// loss of use, data, or profits; or business interruption) however caused
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// and on any theory of liability, whether in contract, strict liability,
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// or tort (including negligence or otherwise) arising in any way out of
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// the use of this software, even if advised of the possibility of such damage.
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//
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//M*/
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#ifndef OPENCV_CUDA_SCAN_HPP
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#define OPENCV_CUDA_SCAN_HPP
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#include "opencv2/core/cuda/common.hpp"
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#include "opencv2/core/cuda/utility.hpp"
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#include "opencv2/core/cuda/warp.hpp"
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#include "opencv2/core/cuda/warp_shuffle.hpp"
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/** @file
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* @deprecated Use @ref cudev instead.
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*/
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//! @cond IGNORED
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namespace cv { namespace cuda { namespace device
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{
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enum ScanKind { EXCLUSIVE = 0, INCLUSIVE = 1 };
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template <ScanKind Kind, typename T, typename F> struct WarpScan
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{
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__device__ __forceinline__ WarpScan() {}
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__device__ __forceinline__ WarpScan(const WarpScan& other) { CV_UNUSED(other); }
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__device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
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{
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const unsigned int lane = idx & 31;
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F op;
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if ( lane >= 1) ptr [idx ] = op(ptr [idx - 1], ptr [idx]);
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if ( lane >= 2) ptr [idx ] = op(ptr [idx - 2], ptr [idx]);
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if ( lane >= 4) ptr [idx ] = op(ptr [idx - 4], ptr [idx]);
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if ( lane >= 8) ptr [idx ] = op(ptr [idx - 8], ptr [idx]);
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if ( lane >= 16) ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
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if( Kind == INCLUSIVE )
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return ptr [idx];
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else
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return (lane > 0) ? ptr [idx - 1] : 0;
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}
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__device__ __forceinline__ unsigned int index(const unsigned int tid)
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{
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return tid;
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}
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__device__ __forceinline__ void init(volatile T *ptr){}
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static const int warp_offset = 0;
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typedef WarpScan<INCLUSIVE, T, F> merge;
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};
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template <ScanKind Kind , typename T, typename F> struct WarpScanNoComp
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{
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__device__ __forceinline__ WarpScanNoComp() {}
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__device__ __forceinline__ WarpScanNoComp(const WarpScanNoComp& other) { CV_UNUSED(other); }
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__device__ __forceinline__ T operator()( volatile T *ptr , const unsigned int idx)
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{
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const unsigned int lane = threadIdx.x & 31;
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F op;
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ptr [idx ] = op(ptr [idx - 1], ptr [idx]);
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ptr [idx ] = op(ptr [idx - 2], ptr [idx]);
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ptr [idx ] = op(ptr [idx - 4], ptr [idx]);
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ptr [idx ] = op(ptr [idx - 8], ptr [idx]);
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ptr [idx ] = op(ptr [idx - 16], ptr [idx]);
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if( Kind == INCLUSIVE )
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return ptr [idx];
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else
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return (lane > 0) ? ptr [idx - 1] : 0;
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}
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__device__ __forceinline__ unsigned int index(const unsigned int tid)
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{
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return (tid >> warp_log) * warp_smem_stride + 16 + (tid & warp_mask);
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}
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__device__ __forceinline__ void init(volatile T *ptr)
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{
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ptr[threadIdx.x] = 0;
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}
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static const int warp_smem_stride = 32 + 16 + 1;
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static const int warp_offset = 16;
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static const int warp_log = 5;
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static const int warp_mask = 31;
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typedef WarpScanNoComp<INCLUSIVE, T, F> merge;
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};
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template <ScanKind Kind , typename T, typename Sc, typename F> struct BlockScan
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{
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__device__ __forceinline__ BlockScan() {}
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__device__ __forceinline__ BlockScan(const BlockScan& other) { CV_UNUSED(other); }
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__device__ __forceinline__ T operator()(volatile T *ptr)
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{
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const unsigned int tid = threadIdx.x;
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const unsigned int lane = tid & warp_mask;
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const unsigned int warp = tid >> warp_log;
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Sc scan;
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typename Sc::merge merge_scan;
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const unsigned int idx = scan.index(tid);
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T val = scan(ptr, idx);
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__syncthreads ();
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if( warp == 0)
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scan.init(ptr);
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__syncthreads ();
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if( lane == 31 )
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ptr [scan.warp_offset + warp ] = (Kind == INCLUSIVE) ? val : ptr [idx];
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__syncthreads ();
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if( warp == 0 )
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merge_scan(ptr, idx);
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__syncthreads();
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if ( warp > 0)
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val = ptr [scan.warp_offset + warp - 1] + val;
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__syncthreads ();
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ptr[idx] = val;
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__syncthreads ();
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return val ;
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}
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static const int warp_log = 5;
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static const int warp_mask = 31;
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};
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template <typename T>
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__device__ T warpScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
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{
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#if __CUDA_ARCH__ >= 300
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const unsigned int laneId = cv::cuda::device::Warp::laneId();
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// scan on shuffl functions
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#pragma unroll
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for (int i = 1; i <= (OPENCV_CUDA_WARP_SIZE / 2); i *= 2)
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{
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const T n = cv::cuda::device::shfl_up(idata, i);
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if (laneId >= i)
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idata += n;
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}
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return idata;
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#else
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unsigned int pos = 2 * tid - (tid & (OPENCV_CUDA_WARP_SIZE - 1));
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s_Data[pos] = 0;
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pos += OPENCV_CUDA_WARP_SIZE;
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s_Data[pos] = idata;
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s_Data[pos] += s_Data[pos - 1];
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s_Data[pos] += s_Data[pos - 2];
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s_Data[pos] += s_Data[pos - 4];
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s_Data[pos] += s_Data[pos - 8];
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s_Data[pos] += s_Data[pos - 16];
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return s_Data[pos];
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#endif
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}
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template <typename T>
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__device__ __forceinline__ T warpScanExclusive(T idata, volatile T* s_Data, unsigned int tid)
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{
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return warpScanInclusive(idata, s_Data, tid) - idata;
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}
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template <int tiNumScanThreads, typename T>
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__device__ T blockScanInclusive(T idata, volatile T* s_Data, unsigned int tid)
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{
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if (tiNumScanThreads > OPENCV_CUDA_WARP_SIZE)
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{
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//Bottom-level inclusive warp scan
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T warpResult = warpScanInclusive(idata, s_Data, tid);
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//Save top elements of each warp for exclusive warp scan
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//sync to wait for warp scans to complete (because s_Data is being overwritten)
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__syncthreads();
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if ((tid & (OPENCV_CUDA_WARP_SIZE - 1)) == (OPENCV_CUDA_WARP_SIZE - 1))
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{
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s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE] = warpResult;
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}
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//wait for warp scans to complete
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__syncthreads();
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if (tid < (tiNumScanThreads / OPENCV_CUDA_WARP_SIZE) )
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{
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//grab top warp elements
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T val = s_Data[tid];
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//calculate exclusive scan and write back to shared memory
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s_Data[tid] = warpScanExclusive(val, s_Data, tid);
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}
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//return updated warp scans with exclusive scan results
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__syncthreads();
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return warpResult + s_Data[tid >> OPENCV_CUDA_LOG_WARP_SIZE];
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}
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else
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{
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return warpScanInclusive(idata, s_Data, tid);
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}
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}
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}}}
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//! @endcond
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#endif // OPENCV_CUDA_SCAN_HPP
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