770 lines
23 KiB
Common Lisp
770 lines
23 KiB
Common Lisp
/*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) 2010-2012, Multicoreware, Inc., all rights reserved.
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// Copyright (C) 2010-2012, Advanced Micro Devices, 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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// @Authors
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// Nathan, liujun@multicorewareinc.com
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// Peng Xiao, pengxiao@outlook.com
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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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#pragma OPENCL EXTENSION cl_khr_global_int32_base_atomics:enable
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#define MAX_FLOAT 3.40282e+038f
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#ifndef T
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#define T float
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#endif
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#ifndef BLOCK_SIZE
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#define BLOCK_SIZE 16
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#endif
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#ifndef MAX_DESC_LEN
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#define MAX_DESC_LEN 64
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#endif
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#ifndef DIST_TYPE
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#define DIST_TYPE 0
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#endif
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//http://graphics.stanford.edu/~seander/bithacks.html#CountBitsSetParallel
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int bit1Count(int v)
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{
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v = v - ((v >> 1) & 0x55555555); // reuse input as temporary
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v = (v & 0x33333333) + ((v >> 2) & 0x33333333); // temp
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return ((v + (v >> 4) & 0xF0F0F0F) * 0x1010101) >> 24; // count
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}
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// dirty fix for non-template support
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#if (DIST_TYPE == 0) // L1Dist
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# ifdef T_FLOAT
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# define DIST(x, y) fabs((x) - (y))
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typedef float value_type;
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typedef float result_type;
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# else
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# define DIST(x, y) abs((x) - (y))
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typedef int value_type;
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typedef int result_type;
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# endif
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#define DIST_RES(x) (x)
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#elif (DIST_TYPE == 1) // L2Dist
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#define DIST(x, y) (((x) - (y)) * ((x) - (y)))
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typedef float value_type;
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typedef float result_type;
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#define DIST_RES(x) sqrt(x)
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#elif (DIST_TYPE == 2) // Hamming
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#define DIST(x, y) bit1Count( (x) ^ (y) )
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typedef int value_type;
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typedef int result_type;
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#define DIST_RES(x) (x)
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#endif
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result_type reduce_block(
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__local value_type *s_query,
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__local value_type *s_train,
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int lidx,
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int lidy
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)
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{
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result_type result = 0;
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#pragma unroll
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for (int j = 0 ; j < BLOCK_SIZE ; j++)
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{
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result += DIST(
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s_query[lidy * BLOCK_SIZE + j],
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s_train[j * BLOCK_SIZE + lidx]);
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}
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return DIST_RES(result);
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}
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result_type reduce_multi_block(
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__local value_type *s_query,
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__local value_type *s_train,
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int block_index,
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int lidx,
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int lidy
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)
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{
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result_type result = 0;
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#pragma unroll
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for (int j = 0 ; j < BLOCK_SIZE ; j++)
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{
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result += DIST(
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s_query[lidy * MAX_DESC_LEN + block_index * BLOCK_SIZE + j],
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s_train[j * BLOCK_SIZE + lidx]);
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}
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return DIST_RES(result);
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}
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/* 2dim launch, global size: dim0 is (query rows + BLOCK_SIZE - 1) / BLOCK_SIZE * BLOCK_SIZE, dim1 is BLOCK_SIZE
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local size: dim0 is BLOCK_SIZE, dim1 is BLOCK_SIZE.
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*/
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__kernel void BruteForceMatch_UnrollMatch(
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__global T *query,
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__global T *train,
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//__global float *mask,
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__global int *bestTrainIdx,
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__global float *bestDistance,
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__local float *sharebuffer,
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int query_rows,
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int query_cols,
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int train_rows,
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int train_cols,
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int step
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)
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{
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const int lidx = get_local_id(0);
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const int lidy = get_local_id(1);
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const int groupidx = get_group_id(0);
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__local value_type *s_query = (__local value_type *)sharebuffer;
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__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
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int queryIdx = groupidx * BLOCK_SIZE + lidy;
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// load the query into local memory.
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#pragma unroll
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for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++)
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{
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int loadx = lidx + i * BLOCK_SIZE;
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s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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}
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float myBestDistance = MAX_FLOAT;
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int myBestTrainIdx = -1;
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// loopUnrolledCached to find the best trainIdx and best distance.
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volatile int imgIdx = 0;
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for (int t = 0, endt = (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE; t < endt; t++)
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{
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result_type result = 0;
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#pragma unroll
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for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
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{
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//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
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const int loadx = lidx + i * BLOCK_SIZE;
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s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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//synchronize to make sure each elem for reduceIteration in share memory is written already.
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barrier(CLK_LOCAL_MEM_FENCE);
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result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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int trainIdx = t * BLOCK_SIZE + lidx;
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if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance/* && mask(queryIdx, trainIdx)*/)
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{
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//bestImgIdx = imgIdx;
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myBestDistance = result;
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myBestTrainIdx = trainIdx;
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}
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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__local float *s_distance = (__local float*)(sharebuffer);
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__local int* s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
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//find BestMatch
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s_distance += lidy * BLOCK_SIZE;
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s_trainIdx += lidy * BLOCK_SIZE;
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s_distance[lidx] = myBestDistance;
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s_trainIdx[lidx] = myBestTrainIdx;
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barrier(CLK_LOCAL_MEM_FENCE);
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//reduce -- now all reduce implement in each threads.
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#pragma unroll
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for (int k = 0 ; k < BLOCK_SIZE; k++)
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{
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if (myBestDistance > s_distance[k])
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{
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myBestDistance = s_distance[k];
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myBestTrainIdx = s_trainIdx[k];
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}
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}
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if (queryIdx < query_rows && lidx == 0)
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{
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bestTrainIdx[queryIdx] = myBestTrainIdx;
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bestDistance[queryIdx] = myBestDistance;
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}
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}
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__kernel void BruteForceMatch_Match(
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__global T *query,
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__global T *train,
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//__global float *mask,
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__global int *bestTrainIdx,
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__global float *bestDistance,
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__local float *sharebuffer,
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int query_rows,
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int query_cols,
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int train_rows,
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int train_cols,
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int step
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)
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{
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const int lidx = get_local_id(0);
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const int lidy = get_local_id(1);
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const int groupidx = get_group_id(0);
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const int queryIdx = groupidx * BLOCK_SIZE + lidy;
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float myBestDistance = MAX_FLOAT;
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int myBestTrainIdx = -1;
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__local value_type *s_query = (__local value_type *)sharebuffer;
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__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
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// loop
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for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
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{
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result_type result = 0;
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for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; i++)
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{
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const int loadx = lidx + i * BLOCK_SIZE;
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//load query and train into local memory
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s_query[lidy * BLOCK_SIZE + lidx] = 0;
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s_train[lidx * BLOCK_SIZE + lidy] = 0;
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if (loadx < query_cols)
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{
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s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx];
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s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx];
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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result += reduce_block(s_query, s_train, lidx, lidy);
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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const int trainIdx = t * BLOCK_SIZE + lidx;
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if (queryIdx < query_rows && trainIdx < train_rows && result < myBestDistance /*&& mask(queryIdx, trainIdx)*/)
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{
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//myBestImgidx = imgIdx;
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myBestDistance = result;
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myBestTrainIdx = trainIdx;
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}
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}
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barrier(CLK_LOCAL_MEM_FENCE);
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__local float *s_distance = (__local float *)sharebuffer;
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__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
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//findBestMatch
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s_distance += lidy * BLOCK_SIZE;
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s_trainIdx += lidy * BLOCK_SIZE;
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s_distance[lidx] = myBestDistance;
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s_trainIdx[lidx] = myBestTrainIdx;
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barrier(CLK_LOCAL_MEM_FENCE);
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//reduce -- now all reduce implement in each threads.
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for (int k = 0 ; k < BLOCK_SIZE; k++)
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{
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if (myBestDistance > s_distance[k])
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{
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myBestDistance = s_distance[k];
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myBestTrainIdx = s_trainIdx[k];
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}
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}
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if (queryIdx < query_rows && lidx == 0)
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{
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bestTrainIdx[queryIdx] = myBestTrainIdx;
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bestDistance[queryIdx] = myBestDistance;
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}
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}
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//radius_unrollmatch
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__kernel void BruteForceMatch_RadiusUnrollMatch(
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__global T *query,
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__global T *train,
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float maxDistance,
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//__global float *mask,
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__global int *bestTrainIdx,
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__global float *bestDistance,
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__global int *nMatches,
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__local float *sharebuffer,
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int query_rows,
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int query_cols,
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int train_rows,
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int train_cols,
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int bestTrainIdx_cols,
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int step,
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int ostep
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)
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{
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const int lidx = get_local_id(0);
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const int lidy = get_local_id(1);
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const int groupidx = get_group_id(0);
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const int groupidy = get_group_id(1);
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const int queryIdx = groupidy * BLOCK_SIZE + lidy;
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const int trainIdx = groupidx * BLOCK_SIZE + lidx;
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__local value_type *s_query = (__local value_type *)sharebuffer;
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__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
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result_type result = 0;
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for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; ++i)
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{
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//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
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const int loadx = lidx + i * BLOCK_SIZE;
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s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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//synchronize to make sure each elem for reduceIteration in share memory is written already.
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barrier(CLK_LOCAL_MEM_FENCE);
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result += reduce_block(s_query, s_train, lidx, lidy);
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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if (queryIdx < query_rows && trainIdx < train_rows &&
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convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
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{
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unsigned int ind = atom_inc(nMatches + queryIdx/*, (unsigned int) -1*/);
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if(ind < bestTrainIdx_cols)
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{
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//bestImgIdx = imgIdx;
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bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
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bestDistance[queryIdx * (ostep / sizeof(float)) + ind] = result;
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}
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}
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}
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//radius_match
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__kernel void BruteForceMatch_RadiusMatch(
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__global T *query,
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__global T *train,
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float maxDistance,
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//__global float *mask,
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__global int *bestTrainIdx,
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__global float *bestDistance,
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__global int *nMatches,
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__local float *sharebuffer,
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int query_rows,
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int query_cols,
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int train_rows,
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int train_cols,
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int bestTrainIdx_cols,
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int step,
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int ostep
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)
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{
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const int lidx = get_local_id(0);
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const int lidy = get_local_id(1);
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const int groupidx = get_group_id(0);
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const int groupidy = get_group_id(1);
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const int queryIdx = groupidy * BLOCK_SIZE + lidy;
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const int trainIdx = groupidx * BLOCK_SIZE + lidx;
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__local value_type *s_query = (__local value_type *)sharebuffer;
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__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
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result_type result = 0;
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for (int i = 0 ; i < (query_cols + BLOCK_SIZE - 1) / BLOCK_SIZE ; ++i)
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{
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//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
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const int loadx = lidx + i * BLOCK_SIZE;
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s_query[lidy * BLOCK_SIZE + lidx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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s_train[lidx * BLOCK_SIZE + lidy] = loadx < query_cols ? train[min(groupidx * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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//synchronize to make sure each elem for reduceIteration in share memory is written already.
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barrier(CLK_LOCAL_MEM_FENCE);
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result += reduce_block(s_query, s_train, lidx, lidy);
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barrier(CLK_LOCAL_MEM_FENCE);
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}
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if (queryIdx < query_rows && trainIdx < train_rows &&
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convert_float(result) < maxDistance/* && mask(queryIdx, trainIdx)*/)
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{
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unsigned int ind = atom_inc(nMatches + queryIdx);
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if(ind < bestTrainIdx_cols)
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{
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//bestImgIdx = imgIdx;
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bestTrainIdx[queryIdx * (ostep / sizeof(int)) + ind] = trainIdx;
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bestDistance[queryIdx * (ostep / sizeof(float)) + ind] = result;
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}
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}
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}
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__kernel void BruteForceMatch_knnUnrollMatch(
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__global T *query,
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__global T *train,
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//__global float *mask,
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__global int2 *bestTrainIdx,
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__global float2 *bestDistance,
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__local float *sharebuffer,
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int query_rows,
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int query_cols,
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int train_rows,
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int train_cols,
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int step
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)
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{
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const int lidx = get_local_id(0);
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const int lidy = get_local_id(1);
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const int groupidx = get_group_id(0);
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const int queryIdx = groupidx * BLOCK_SIZE + lidy;
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__local value_type *s_query = (__local value_type *)sharebuffer;
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__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * MAX_DESC_LEN;
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// load the query into local memory.
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for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE; i ++)
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{
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int loadx = lidx + i * BLOCK_SIZE;
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s_query[lidy * MAX_DESC_LEN + loadx] = loadx < query_cols ? query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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}
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float myBestDistance1 = MAX_FLOAT;
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float myBestDistance2 = MAX_FLOAT;
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int myBestTrainIdx1 = -1;
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int myBestTrainIdx2 = -1;
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//loopUnrolledCached
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volatile int imgIdx = 0;
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for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
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{
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result_type result = 0;
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for (int i = 0 ; i < MAX_DESC_LEN / BLOCK_SIZE ; i++)
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{
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//load a BLOCK_SIZE * BLOCK_SIZE block into local train.
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const int loadx = lidx + i * BLOCK_SIZE;
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s_train[lidx * BLOCK_SIZE + lidy] = loadx < train_cols ? train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx] : 0;
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//synchronize to make sure each elem for reduceIteration in share memory is written already.
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
result += reduce_multi_block(s_query, s_train, i, lidx, lidy);
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
}
|
|
|
|
const int trainIdx = t * BLOCK_SIZE + lidx;
|
|
|
|
if (queryIdx < query_rows && trainIdx < train_rows)
|
|
{
|
|
if (result < myBestDistance1)
|
|
{
|
|
myBestDistance2 = myBestDistance1;
|
|
myBestTrainIdx2 = myBestTrainIdx1;
|
|
myBestDistance1 = result;
|
|
myBestTrainIdx1 = trainIdx;
|
|
}
|
|
else if (result < myBestDistance2)
|
|
{
|
|
myBestDistance2 = result;
|
|
myBestTrainIdx2 = trainIdx;
|
|
}
|
|
}
|
|
}
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
__local float *s_distance = (local float *)sharebuffer;
|
|
__local int *s_trainIdx = (local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
|
|
|
|
// find BestMatch
|
|
s_distance += lidy * BLOCK_SIZE;
|
|
s_trainIdx += lidy * BLOCK_SIZE;
|
|
|
|
s_distance[lidx] = myBestDistance1;
|
|
s_trainIdx[lidx] = myBestTrainIdx1;
|
|
|
|
float bestDistance1 = MAX_FLOAT;
|
|
float bestDistance2 = MAX_FLOAT;
|
|
int bestTrainIdx1 = -1;
|
|
int bestTrainIdx2 = -1;
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
if (lidx == 0)
|
|
{
|
|
for (int i = 0 ; i < BLOCK_SIZE ; i++)
|
|
{
|
|
float val = s_distance[i];
|
|
if (val < bestDistance1)
|
|
{
|
|
bestDistance2 = bestDistance1;
|
|
bestTrainIdx2 = bestTrainIdx1;
|
|
|
|
bestDistance1 = val;
|
|
bestTrainIdx1 = s_trainIdx[i];
|
|
}
|
|
else if (val < bestDistance2)
|
|
{
|
|
bestDistance2 = val;
|
|
bestTrainIdx2 = s_trainIdx[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
s_distance[lidx] = myBestDistance2;
|
|
s_trainIdx[lidx] = myBestTrainIdx2;
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
if (lidx == 0)
|
|
{
|
|
for (int i = 0 ; i < BLOCK_SIZE ; i++)
|
|
{
|
|
float val = s_distance[i];
|
|
|
|
if (val < bestDistance2)
|
|
{
|
|
bestDistance2 = val;
|
|
bestTrainIdx2 = s_trainIdx[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
myBestDistance1 = bestDistance1;
|
|
myBestDistance2 = bestDistance2;
|
|
|
|
myBestTrainIdx1 = bestTrainIdx1;
|
|
myBestTrainIdx2 = bestTrainIdx2;
|
|
|
|
if (queryIdx < query_rows && lidx == 0)
|
|
{
|
|
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
|
|
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
|
|
}
|
|
}
|
|
|
|
__kernel void BruteForceMatch_knnMatch(
|
|
__global T *query,
|
|
__global T *train,
|
|
//__global float *mask,
|
|
__global int2 *bestTrainIdx,
|
|
__global float2 *bestDistance,
|
|
__local float *sharebuffer,
|
|
int query_rows,
|
|
int query_cols,
|
|
int train_rows,
|
|
int train_cols,
|
|
int step
|
|
)
|
|
{
|
|
const int lidx = get_local_id(0);
|
|
const int lidy = get_local_id(1);
|
|
const int groupidx = get_group_id(0);
|
|
|
|
const int queryIdx = groupidx * BLOCK_SIZE + lidy;
|
|
__local value_type *s_query = (__local value_type *)sharebuffer;
|
|
__local value_type *s_train = (__local value_type *)sharebuffer + BLOCK_SIZE * BLOCK_SIZE;
|
|
|
|
float myBestDistance1 = MAX_FLOAT;
|
|
float myBestDistance2 = MAX_FLOAT;
|
|
int myBestTrainIdx1 = -1;
|
|
int myBestTrainIdx2 = -1;
|
|
|
|
//loop
|
|
for (int t = 0 ; t < (train_rows + BLOCK_SIZE - 1) / BLOCK_SIZE ; t++)
|
|
{
|
|
result_type result = 0.0f;
|
|
for (int i = 0 ; i < (query_cols + BLOCK_SIZE -1) / BLOCK_SIZE ; i++)
|
|
{
|
|
const int loadx = lidx + i * BLOCK_SIZE;
|
|
//load query and train into local memory
|
|
s_query[lidy * BLOCK_SIZE + lidx] = 0;
|
|
s_train[lidx * BLOCK_SIZE + lidy] = 0;
|
|
|
|
if (loadx < query_cols)
|
|
{
|
|
s_query[lidy * BLOCK_SIZE + lidx] = query[min(queryIdx, query_rows - 1) * (step / sizeof(float)) + loadx];
|
|
s_train[lidx * BLOCK_SIZE + lidy] = train[min(t * BLOCK_SIZE + lidy, train_rows - 1) * (step / sizeof(float)) + loadx];
|
|
}
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
result += reduce_block(s_query, s_train, lidx, lidy);
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
}
|
|
|
|
const int trainIdx = t * BLOCK_SIZE + lidx;
|
|
|
|
if (queryIdx < query_rows && trainIdx < train_rows /*&& mask(queryIdx, trainIdx)*/)
|
|
{
|
|
if (result < myBestDistance1)
|
|
{
|
|
myBestDistance2 = myBestDistance1;
|
|
myBestTrainIdx2 = myBestTrainIdx1;
|
|
myBestDistance1 = result;
|
|
myBestTrainIdx1 = trainIdx;
|
|
}
|
|
else if (result < myBestDistance2)
|
|
{
|
|
myBestDistance2 = result;
|
|
myBestTrainIdx2 = trainIdx;
|
|
}
|
|
}
|
|
}
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
__local float *s_distance = (__local float *)sharebuffer;
|
|
__local int *s_trainIdx = (__local int *)(sharebuffer + BLOCK_SIZE * BLOCK_SIZE);
|
|
|
|
//findBestMatch
|
|
s_distance += lidy * BLOCK_SIZE;
|
|
s_trainIdx += lidy * BLOCK_SIZE;
|
|
|
|
s_distance[lidx] = myBestDistance1;
|
|
s_trainIdx[lidx] = myBestTrainIdx1;
|
|
|
|
float bestDistance1 = MAX_FLOAT;
|
|
float bestDistance2 = MAX_FLOAT;
|
|
int bestTrainIdx1 = -1;
|
|
int bestTrainIdx2 = -1;
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
if (lidx == 0)
|
|
{
|
|
for (int i = 0 ; i < BLOCK_SIZE ; i++)
|
|
{
|
|
float val = s_distance[i];
|
|
if (val < bestDistance1)
|
|
{
|
|
bestDistance2 = bestDistance1;
|
|
bestTrainIdx2 = bestTrainIdx1;
|
|
|
|
bestDistance1 = val;
|
|
bestTrainIdx1 = s_trainIdx[i];
|
|
}
|
|
else if (val < bestDistance2)
|
|
{
|
|
bestDistance2 = val;
|
|
bestTrainIdx2 = s_trainIdx[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
s_distance[lidx] = myBestDistance2;
|
|
s_trainIdx[lidx] = myBestTrainIdx2;
|
|
|
|
barrier(CLK_LOCAL_MEM_FENCE);
|
|
|
|
if (lidx == 0)
|
|
{
|
|
for (int i = 0 ; i < BLOCK_SIZE ; i++)
|
|
{
|
|
float val = s_distance[i];
|
|
|
|
if (val < bestDistance2)
|
|
{
|
|
bestDistance2 = val;
|
|
bestTrainIdx2 = s_trainIdx[i];
|
|
}
|
|
}
|
|
}
|
|
|
|
myBestDistance1 = bestDistance1;
|
|
myBestDistance2 = bestDistance2;
|
|
|
|
myBestTrainIdx1 = bestTrainIdx1;
|
|
myBestTrainIdx2 = bestTrainIdx2;
|
|
|
|
if (queryIdx < query_rows && lidx == 0)
|
|
{
|
|
bestTrainIdx[queryIdx] = (int2)(myBestTrainIdx1, myBestTrainIdx2);
|
|
bestDistance[queryIdx] = (float2)(myBestDistance1, myBestDistance2);
|
|
}
|
|
}
|
|
|
|
kernel void BruteForceMatch_calcDistanceUnrolled(
|
|
__global T *query,
|
|
__global T *train,
|
|
//__global float *mask,
|
|
__global float *allDist,
|
|
__local float *sharebuffer,
|
|
int query_rows,
|
|
int query_cols,
|
|
int train_rows,
|
|
int train_cols,
|
|
int step)
|
|
{
|
|
/* Todo */
|
|
}
|
|
|
|
kernel void BruteForceMatch_calcDistance(
|
|
__global T *query,
|
|
__global T *train,
|
|
//__global float *mask,
|
|
__global float *allDist,
|
|
__local float *sharebuffer,
|
|
int query_rows,
|
|
int query_cols,
|
|
int train_rows,
|
|
int train_cols,
|
|
int step)
|
|
{
|
|
/* Todo */
|
|
}
|
|
|
|
kernel void BruteForceMatch_findBestMatch(
|
|
__global float *allDist,
|
|
__global int *bestTrainIdx,
|
|
__global float *bestDistance,
|
|
int k
|
|
)
|
|
{
|
|
/* Todo */
|
|
}
|