155 lines
5.0 KiB
C++
155 lines
5.0 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) 2009-2010, NVIDIA Corporation, 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 _ncv_alg_hpp_
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#define _ncv_alg_hpp_
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#include "NCV.hpp"
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template <class T>
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static void swap(T &p1, T &p2)
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{
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T tmp = p1;
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p1 = p2;
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p2 = tmp;
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}
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template<typename T>
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static T divUp(T a, T b)
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{
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return (a + b - 1) / b;
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}
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template<typename T>
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struct functorAddValues
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{
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static __device__ __inline__ void assign(volatile T *dst, volatile T *src)
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{
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//Works only for integral types. If you see compiler error here, then you have to specify how to copy your object as a set of integral fields.
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*dst = *src;
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}
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static __device__ __inline__ void reduce(volatile T &in1out, const volatile T &in2)
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{
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in1out += in2;
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}
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};
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template<typename T>
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struct functorMinValues
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{
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static __device__ __inline__ void assign(volatile T *dst, volatile T *src)
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{
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//Works only for integral types. If you see compiler error here, then you have to specify how to copy your object as a set of integral fields.
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*dst = *src;
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}
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static __device__ __inline__ void reduce(volatile T &in1out, const volatile T &in2)
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{
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in1out = in1out > in2 ? in2 : in1out;
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}
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};
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template<typename T>
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struct functorMaxValues
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{
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static __device__ __inline__ void assign(volatile T *dst, volatile T *src)
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{
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//Works only for integral types. If you see compiler error here, then you have to specify how to copy your object as a set of integral fields.
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*dst = *src;
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}
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static __device__ __inline__ void reduce(volatile T &in1out, const volatile T &in2)
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{
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in1out = in1out > in2 ? in1out : in2;
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}
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};
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template<typename Tdata, class Tfunc, Ncv32u nThreads>
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static __device__ Tdata subReduce(Tdata threadElem)
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{
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Tfunc functor;
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__shared__ Tdata _reduceArr[nThreads];
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volatile Tdata *reduceArr = _reduceArr;
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functor.assign(reduceArr + threadIdx.x, &threadElem);
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__syncthreads();
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if (nThreads >= 256 && threadIdx.x < 128)
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{
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 128]);
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}
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__syncthreads();
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if (nThreads >= 128 && threadIdx.x < 64)
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{
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 64]);
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}
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__syncthreads();
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if (threadIdx.x < 32)
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{
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if (nThreads >= 64)
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{
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 32]);
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}
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if (nThreads >= 32 && threadIdx.x < 16)
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{
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 16]);
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 8]);
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 4]);
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 2]);
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functor.reduce(reduceArr[threadIdx.x], reduceArr[threadIdx.x + 1]);
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}
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}
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__syncthreads();
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Tdata reduceRes;
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functor.assign(&reduceRes, reduceArr);
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return reduceRes;
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}
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#endif //_ncv_alg_hpp_
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