| 1 | /* |
|---|---|
| 2 | * map_vector_sparse.hpp |
| 3 | * |
| 4 | * Created on: Jan 22, 2019 |
| 5 | * Author: i-bird |
| 6 | */ |
| 7 | |
| 8 | #ifndef MAP_VECTOR_SPARSE_HPP_ |
| 9 | #define MAP_VECTOR_SPARSE_HPP_ |
| 10 | |
| 11 | #include "util/cuda_launch.hpp" |
| 12 | #include "Vector/map_vector.hpp" |
| 13 | #include "Vector/cuda/map_vector_sparse_cuda_ker.cuh" |
| 14 | #include "Vector/cuda/map_vector_sparse_cuda_kernels.cuh" |
| 15 | #include "util/cuda/ofp_context.hxx" |
| 16 | #include <iostream> |
| 17 | #include <limits> |
| 18 | |
| 19 | #if defined(__NVCC__) |
| 20 | #if !defined(CUDA_ON_CPU) |
| 21 | #include "util/cuda/moderngpu/kernel_segreduce.hxx" |
| 22 | #include "util/cuda/moderngpu/kernel_merge.hxx" |
| 23 | #endif |
| 24 | #include "util/cuda/kernels.cuh" |
| 25 | #endif |
| 26 | |
| 27 | #include "util/cuda/scan_ofp.cuh" |
| 28 | #include "util/cuda/sort_ofp.cuh" |
| 29 | |
| 30 | enum flush_type |
| 31 | { |
| 32 | FLUSH_ON_HOST = 0, |
| 33 | FLUSH_ON_DEVICE = 1, |
| 34 | FLUSH_NO_DATA = 2 |
| 35 | }; |
| 36 | |
| 37 | template<typename OfpmVectorT> |
| 38 | using ValueTypeOf = typename std::remove_reference<OfpmVectorT>::type::value_type; |
| 39 | |
| 40 | namespace openfpm |
| 41 | { |
| 42 | // All props |
| 43 | template<typename sg_type> |
| 44 | struct htoD |
| 45 | { |
| 46 | //! encapsulated source object |
| 47 | sg_type & sg; |
| 48 | |
| 49 | unsigned int lele; |
| 50 | |
| 51 | htoD(sg_type & sg, unsigned int lele) |
| 52 | :sg(sg),lele(lele) |
| 53 | {}; |
| 54 | |
| 55 | |
| 56 | //! It call the copy function for each property |
| 57 | template<typename T> |
| 58 | __device__ __host__ inline void operator()(T& t) const |
| 59 | { |
| 60 | sg.template hostToDevice<T::value>(lele,lele); |
| 61 | } |
| 62 | }; |
| 63 | |
| 64 | constexpr int VECTOR_SPARSE_STANDARD = 1; |
| 65 | constexpr int VECTOR_SPARSE_BLOCK = 2; |
| 66 | |
| 67 | template<typename reduction_type, unsigned int impl> |
| 68 | struct cpu_block_process |
| 69 | { |
| 70 | template<typename encap_src, typename encap_dst> |
| 71 | static inline void process(encap_src & src, encap_dst & dst) |
| 72 | { |
| 73 | dst = reduction_type::red(dst,src); |
| 74 | } |
| 75 | }; |
| 76 | |
| 77 | template<typename reduction_type> |
| 78 | struct cpu_block_process<reduction_type,VECTOR_SPARSE_BLOCK> |
| 79 | { |
| 80 | template<typename encap_src, typename encap_dst> |
| 81 | static inline void process(encap_src & src, encap_dst & dst) |
| 82 | { |
| 83 | for (size_t i = 0 ; i < encap_src::size ; i++) |
| 84 | { |
| 85 | dst[i] = reduction_type::red(dst[i],src[i]); |
| 86 | } |
| 87 | } |
| 88 | }; |
| 89 | |
| 90 | template<typename reduction_type> |
| 91 | struct cpu_block_process<reduction_type,3> |
| 92 | { |
| 93 | template<typename encap_src, typename encap_dst,unsigned int N1> |
| 94 | static inline void process(encap_src & src, encap_dst (& dst)[N1]) |
| 95 | { |
| 96 | for (unsigned int j = 0 ; j < N1 ; j++) |
| 97 | { |
| 98 | for (size_t i = 0 ; i < encap_dst::size ; i++) |
| 99 | { |
| 100 | dst[i] = reduction_type::red(dst[i][j],src[j][i]); |
| 101 | } |
| 102 | } |
| 103 | } |
| 104 | |
| 105 | template<unsigned int N1, unsigned int blockSize, typename encap_src, typename encap_dst> |
| 106 | static inline void process_e(encap_src & src, encap_dst & dst) |
| 107 | { |
| 108 | for (unsigned int j = 0 ; j < N1 ; j++) |
| 109 | { |
| 110 | for (size_t i = 0 ; i < blockSize ; i++) |
| 111 | { |
| 112 | dst[i] = reduction_type::red(dst[i][j],src[i][j]); |
| 113 | } |
| 114 | } |
| 115 | } |
| 116 | }; |
| 117 | |
| 118 | /*! \brief Functor switch to select the vector sparse for standars scalar and blocked implementation |
| 119 | * |
| 120 | * |
| 121 | */ |
| 122 | template<unsigned int impl, typename block_functor> |
| 123 | struct scalar_block_implementation_switch // Case for scalar implementations |
| 124 | { |
| 125 | template <unsigned int p, typename vector_index_type> |
| 126 | static void extendSegments(vector_index_type & segments, size_t dataSize) |
| 127 | { |
| 128 | #ifdef __NVCC__ |
| 129 | // Pass as there is nothing to append for mgpu |
| 130 | #else // __NVCC__ |
| 131 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file is supposed to be compiled with nvcc"<< std::endl; |
| 132 | #endif // __NVCC__ |
| 133 | } |
| 134 | |
| 135 | template <unsigned int pSegment, typename vector_reduction, typename T, typename vector_data_type, typename vector_index_type , typename vector_index_type2> |
| 136 | static void segreduce(vector_data_type & vector_data, |
| 137 | vector_data_type & vector_data_unsorted, |
| 138 | vector_index_type & vector_data_map, |
| 139 | vector_index_type2 & segment_offset, |
| 140 | vector_data_type & vector_data_red, |
| 141 | block_functor & blf, |
| 142 | mgpu::ofp_context_t & context) |
| 143 | { |
| 144 | #ifdef __NVCC__ |
| 145 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 146 | typedef typename boost::mpl::at<typename vector_data_type::value_type::type,typename reduction_type::prop>::type red_type; |
| 147 | typedef typename reduction_type::template op_red<red_type> red_op; |
| 148 | typedef typename boost::mpl::at<typename vector_index_type::value_type::type,boost::mpl::int_<0>>::type seg_type; |
| 149 | red_type init; |
| 150 | init = 0; |
| 151 | |
| 152 | assert((std::is_same<seg_type,int>::value == true)); |
| 153 | |
| 154 | mgpu::segreduce( |
| 155 | (red_type *)vector_data.template getDeviceBuffer<reduction_type::prop::value>(), vector_data.size(), |
| 156 | (int *)segment_offset.template getDeviceBuffer<1>(), segment_offset.size(), |
| 157 | (red_type *)vector_data_red.template getDeviceBuffer<reduction_type::prop::value>(), |
| 158 | red_op(), init, context); |
| 159 | #else // __NVCC__ |
| 160 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file is supposed to be compiled with nvcc"<< std::endl; |
| 161 | #endif // __NVCC__ |
| 162 | } |
| 163 | |
| 164 | |
| 165 | /*! \briefMerge all datas |
| 166 | * |
| 167 | * \param vct_index_old sorted vector of the old indexes |
| 168 | * \param vct_data_old vector of old data |
| 169 | * \param vct_index_out output indexes merged new and old indexes |
| 170 | * \param vct_index_merge_id indicate from where it come from the merged index (if the number is bigger than vct_index_old.size() |
| 171 | * the merged index come from the new data) |
| 172 | * \param vct_index_merge indexes old and new merged with conflicts |
| 173 | * \param vct_add_data_unique data to add (has been already reduced) |
| 174 | * \param vct_data_old old data |
| 175 | * \param vct_add_data data to add in its original form in the insert buffer |
| 176 | * \param vct_data_out reduced data vector new + old |
| 177 | * \param vct_index_dtmp temporal buffer vector used for intermediate calculation |
| 178 | * |
| 179 | */ |
| 180 | template < |
| 181 | typename vector_data_type, |
| 182 | typename vector_index_type, |
| 183 | typename vector_index_type2, |
| 184 | typename vector_index_dtmp_type, |
| 185 | typename Ti, |
| 186 | typename ... v_reduce> |
| 187 | static void solveConflicts( |
| 188 | vector_index_type & vct_index_old, |
| 189 | vector_index_type & vct_index_merge, |
| 190 | vector_index_type & vct_index_merge_id, |
| 191 | vector_index_type & vct_index_out, |
| 192 | vector_index_dtmp_type & vct_index_dtmp, |
| 193 | vector_index_type & data_map, |
| 194 | vector_index_type2 & segments_new, |
| 195 | vector_data_type & vct_data_old, |
| 196 | vector_data_type & vct_add_data, |
| 197 | vector_data_type & vct_add_data_unique, |
| 198 | vector_data_type & vct_data_out, |
| 199 | ite_gpu<1> & itew, |
| 200 | block_functor & blf, |
| 201 | mgpu::ofp_context_t & context |
| 202 | ) |
| 203 | { |
| 204 | #ifdef __NVCC__ |
| 205 | |
| 206 | CUDA_LAUNCH((solve_conflicts< |
| 207 | decltype(vct_index_merge.toKernel()), |
| 208 | decltype(vct_data_old.toKernel()), |
| 209 | decltype(vct_index_dtmp.toKernel()), |
| 210 | 128, |
| 211 | v_reduce ... |
| 212 | >), |
| 213 | itew, |
| 214 | vct_index_merge.toKernel(),vct_data_old.toKernel(), |
| 215 | vct_index_merge_id.toKernel(),vct_add_data_unique.toKernel(), |
| 216 | vct_index_out.toKernel(),vct_data_out.toKernel(), |
| 217 | vct_index_dtmp.toKernel(), |
| 218 | vct_index_old.size()); |
| 219 | |
| 220 | // we scan tmp3 |
| 221 | openfpm::scan( |
| 222 | (Ti*)vct_index_dtmp.template getDeviceBuffer<0>(), |
| 223 | vct_index_dtmp.size(), |
| 224 | (Ti *)vct_index_dtmp.template getDeviceBuffer<1>(), |
| 225 | context); |
| 226 | |
| 227 | // get the size to resize vct_index and vct_data |
| 228 | vct_index_dtmp.template deviceToHost<0,1>(vct_index_dtmp.size()-1,vct_index_dtmp.size()-1); |
| 229 | int size = vct_index_dtmp.template get<1>(vct_index_dtmp.size()-1) + vct_index_dtmp.template get<0>(vct_index_dtmp.size()-1); |
| 230 | |
| 231 | vct_index_old.resize(size); |
| 232 | vct_data_old.resize(size); |
| 233 | |
| 234 | CUDA_LAUNCH(realign,itew,vct_index_out.toKernel(),vct_data_out.toKernel(), |
| 235 | vct_index_old.toKernel(), vct_data_old.toKernel(), |
| 236 | vct_index_dtmp.toKernel()); |
| 237 | |
| 238 | |
| 239 | #else // __NVCC__ |
| 240 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file is supposed to be compiled with nvcc"<< std::endl; |
| 241 | #endif // __NVCC__ |
| 242 | } |
| 243 | }; |
| 244 | |
| 245 | |
| 246 | template<typename block_functor> |
| 247 | struct scalar_block_implementation_switch<2, block_functor> // Case for blocked implementations |
| 248 | { |
| 249 | template <unsigned int p, typename vector_index_type> |
| 250 | static void extendSegments(vector_index_type & segments, size_t dataSize) |
| 251 | { |
| 252 | #ifdef __NVCC__ |
| 253 | // Append trailing element to segment (marks end of last segment) |
| 254 | segments.resize(segments.size()+1); |
| 255 | segments.template get<p>(segments.size() - 1) = dataSize; |
| 256 | segments.template hostToDevice<p>(segments.size() - 1, segments.size() - 1); |
| 257 | #else // __NVCC__ |
| 258 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file is supposed to be compiled with nvcc"<< std::endl; |
| 259 | #endif // __NVCC__ |
| 260 | } |
| 261 | |
| 262 | template <unsigned int pSegment, typename vector_reduction, typename T, typename vector_data_type, typename vector_index_type ,typename vector_index_type2> |
| 263 | static void segreduce(vector_data_type & vector_data, |
| 264 | vector_data_type & vector_data_unsorted, |
| 265 | vector_index_type & vector_data_map, |
| 266 | vector_index_type2 & segment_offset, |
| 267 | vector_data_type & vector_data_red, |
| 268 | block_functor & blf, |
| 269 | mgpu::ofp_context_t & context) |
| 270 | { |
| 271 | |
| 272 | } |
| 273 | |
| 274 | template < |
| 275 | typename vector_data_type, |
| 276 | typename vector_index_type, |
| 277 | typename vector_index_type2, |
| 278 | typename vector_index_dtmp_type, |
| 279 | typename Ti, |
| 280 | typename ... v_reduce> |
| 281 | static void solveConflicts( |
| 282 | vector_index_type & vct_index_old, |
| 283 | vector_index_type & vct_index_merge, |
| 284 | vector_index_type & vct_index_merge_id, |
| 285 | vector_index_type & vct_index_out, |
| 286 | vector_index_dtmp_type & vct_index_dtmp, |
| 287 | vector_index_type & data_map, |
| 288 | vector_index_type2 & segments_new, |
| 289 | vector_data_type & vct_data, |
| 290 | vector_data_type & vct_add_data, |
| 291 | vector_data_type & vct_add_data_unique, |
| 292 | vector_data_type & vct_data_out, |
| 293 | ite_gpu<1> & itew, |
| 294 | block_functor & blf, |
| 295 | mgpu::ofp_context_t & context |
| 296 | ) |
| 297 | { |
| 298 | #ifdef __NVCC__ |
| 299 | blf.template solve_conflicts<1, |
| 300 | decltype(vct_index_merge), |
| 301 | decltype(segments_new), |
| 302 | decltype(vct_data), |
| 303 | v_reduce ...> |
| 304 | (vct_index_merge, vct_index_merge_id, segments_new, data_map, |
| 305 | vct_data, vct_add_data, |
| 306 | vct_index_old, vct_data_out, |
| 307 | context); |
| 308 | vct_data_out.swap(vct_data); |
| 309 | |
| 310 | #else // __NVCC__ |
| 311 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file is supposed to be compiled with nvcc"<< std::endl; |
| 312 | #endif // __NVCC__ |
| 313 | } |
| 314 | }; |
| 315 | |
| 316 | template<typename Ti> |
| 317 | struct reorder |
| 318 | { |
| 319 | Ti id; |
| 320 | Ti id2; |
| 321 | |
| 322 | bool operator<(const reorder & t) const |
| 323 | { |
| 324 | return id < t.id; |
| 325 | } |
| 326 | }; |
| 327 | |
| 328 | template<typename reduction_type, typename vector_reduction, typename T,unsigned int impl, typename red_type> |
| 329 | struct sparse_vector_reduction_cpu_impl |
| 330 | { |
| 331 | template<typename vector_data_type, typename vector_index_type,typename vector_index_type_reo> |
| 332 | static inline void red(size_t & i, vector_data_type & vector_data_red, |
| 333 | vector_data_type & vector_data, |
| 334 | vector_index_type & vector_index, |
| 335 | vector_index_type_reo & reorder_add_index_cpu) |
| 336 | { |
| 337 | size_t start = reorder_add_index_cpu.get(i).id; |
| 338 | red_type red = vector_data.template get<reduction_type::prop::value>(i); |
| 339 | |
| 340 | size_t j = 1; |
| 341 | for ( ; i+j < reorder_add_index_cpu.size() && reorder_add_index_cpu.get(i+j).id == start ; j++) |
| 342 | { |
| 343 | cpu_block_process<reduction_type,impl>::process(vector_data.template get<reduction_type::prop::value>(i+j),red); |
| 344 | //reduction_type::red(red,vector_data.template get<reduction_type::prop::value>(i+j)); |
| 345 | } |
| 346 | vector_data_red.add(); |
| 347 | vector_data_red.template get<reduction_type::prop::value>(vector_data_red.size()-1) = red; |
| 348 | |
| 349 | if (T::value == 0) |
| 350 | { |
| 351 | vector_index.add(); |
| 352 | vector_index.template get<0>(vector_index.size() - 1) = reorder_add_index_cpu.get(i).id; |
| 353 | } |
| 354 | |
| 355 | i += j; |
| 356 | } |
| 357 | }; |
| 358 | |
| 359 | |
| 360 | template<typename reduction_type, typename vector_reduction, typename T,unsigned int impl, typename red_type, unsigned int N1> |
| 361 | struct sparse_vector_reduction_cpu_impl<reduction_type,vector_reduction,T,impl,red_type[N1]> |
| 362 | { |
| 363 | template<typename vector_data_type, typename vector_index_type,typename vector_index_type_reo> |
| 364 | static inline void red(size_t & i, vector_data_type & vector_data_red, |
| 365 | vector_data_type & vector_data, |
| 366 | vector_index_type & vector_index, |
| 367 | vector_index_type_reo & reorder_add_index_cpu) |
| 368 | { |
| 369 | size_t start = reorder_add_index_cpu.get(i).id; |
| 370 | red_type red[N1]; |
| 371 | |
| 372 | for (size_t k = 0 ; k < N1 ; k++) |
| 373 | { |
| 374 | red[k] = vector_data.template get<reduction_type::prop::value>(i)[k]; |
| 375 | } |
| 376 | |
| 377 | size_t j = 1; |
| 378 | for ( ; i+j < reorder_add_index_cpu.size() && reorder_add_index_cpu.get(i+j).id == start ; j++) |
| 379 | { |
| 380 | auto ev = vector_data.template get<reduction_type::prop::value>(i+j); |
| 381 | cpu_block_process<reduction_type,impl+1>::process(ev,red); |
| 382 | //reduction_type::red(red,vector_data.template get<reduction_type::prop::value>(i+j)); |
| 383 | } |
| 384 | |
| 385 | vector_data_red.add(); |
| 386 | |
| 387 | for (size_t k = 0 ; k < N1 ; k++) |
| 388 | { |
| 389 | vector_data_red.template get<reduction_type::prop::value>(vector_data_red.size()-1)[k] = red[k]; |
| 390 | } |
| 391 | |
| 392 | if (T::value == 0) |
| 393 | { |
| 394 | vector_index.add(); |
| 395 | vector_index.template get<0>(vector_index.size() - 1) = reorder_add_index_cpu.get(i).id; |
| 396 | } |
| 397 | |
| 398 | i += j; |
| 399 | } |
| 400 | }; |
| 401 | |
| 402 | /*! \brief this class is a functor for "for_each" algorithm |
| 403 | * |
| 404 | * This class is a functor for "for_each" algorithm. For each |
| 405 | * element of the boost::vector the operator() is called. |
| 406 | * Is mainly used to copy one encap into another encap object |
| 407 | * |
| 408 | * \tparam encap source |
| 409 | * \tparam encap dst |
| 410 | * |
| 411 | */ |
| 412 | template<typename vector_data_type, |
| 413 | typename vector_index_type, |
| 414 | typename vector_index_type_reo, |
| 415 | typename vector_reduction, |
| 416 | unsigned int impl> |
| 417 | struct sparse_vector_reduction_cpu |
| 418 | { |
| 419 | //! Vector in which to the reduction |
| 420 | vector_data_type & vector_data_red; |
| 421 | |
| 422 | //! Vector in which to the reduction |
| 423 | vector_data_type & vector_data; |
| 424 | |
| 425 | //! reorder vector index |
| 426 | vector_index_type_reo & reorder_add_index_cpu; |
| 427 | |
| 428 | //! Index type vector |
| 429 | vector_index_type & vector_index; |
| 430 | |
| 431 | /*! \brief constructor |
| 432 | * |
| 433 | * \param src source encapsulated object |
| 434 | * \param dst source encapsulated object |
| 435 | * |
| 436 | */ |
| 437 | inline sparse_vector_reduction_cpu(vector_data_type & vector_data_red, |
| 438 | vector_data_type & vector_data, |
| 439 | vector_index_type & vector_index, |
| 440 | vector_index_type_reo & reorder_add_index_cpu) |
| 441 | :vector_data_red(vector_data_red),vector_data(vector_data),vector_index(vector_index),reorder_add_index_cpu(reorder_add_index_cpu) |
| 442 | {}; |
| 443 | |
| 444 | //! It call the copy function for each property |
| 445 | template<typename T> |
| 446 | inline void operator()(T& t) const |
| 447 | { |
| 448 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 449 | typedef typename boost::mpl::at<typename ValueTypeOf<vector_data_type>::type,typename reduction_type::prop>::type red_type; |
| 450 | |
| 451 | if (reduction_type::is_special() == false) |
| 452 | { |
| 453 | for (size_t i = 0 ; i < reorder_add_index_cpu.size() ; ) |
| 454 | { |
| 455 | sparse_vector_reduction_cpu_impl<reduction_type,vector_reduction,T,impl,red_type>::red(i,vector_data_red,vector_data,vector_index,reorder_add_index_cpu); |
| 456 | |
| 457 | /* size_t start = reorder_add_index_cpu.get(i).id; |
| 458 | red_type red = vector_data.template get<reduction_type::prop::value>(i); |
| 459 | |
| 460 | size_t j = 1; |
| 461 | for ( ; i+j < reorder_add_index_cpu.size() && reorder_add_index_cpu.get(i+j).id == start ; j++) |
| 462 | { |
| 463 | cpu_block_process<reduction_type,impl>::process(vector_data.template get<reduction_type::prop::value>(i+j),red); |
| 464 | //reduction_type::red(red,vector_data.template get<reduction_type::prop::value>(i+j)); |
| 465 | } |
| 466 | vector_data_red.add(); |
| 467 | vector_data_red.template get<reduction_type::prop::value>(vector_data_red.size()-1) = red; |
| 468 | |
| 469 | if (T::value == 0) |
| 470 | { |
| 471 | vector_index.add(); |
| 472 | vector_index.template get<0>(vector_index.size() - 1) = reorder_add_index_cpu.get(i).id; |
| 473 | } |
| 474 | |
| 475 | i += j;*/ |
| 476 | } |
| 477 | } |
| 478 | } |
| 479 | }; |
| 480 | |
| 481 | /*! \brief this class is a functor for "for_each" algorithm |
| 482 | * |
| 483 | * This class is a functor for "for_each" algorithm. For each |
| 484 | * element of the boost::vector the operator() is called. |
| 485 | * Is mainly used to copy one encap into another encap object |
| 486 | * |
| 487 | * \tparam encap source |
| 488 | * \tparam encap dst |
| 489 | * |
| 490 | */ |
| 491 | template<typename encap_src, |
| 492 | typename encap_dst, |
| 493 | typename vector_reduction> |
| 494 | struct sparse_vector_reduction_solve_conflict_assign_cpu |
| 495 | { |
| 496 | //! source |
| 497 | encap_src & src; |
| 498 | |
| 499 | //! destination |
| 500 | encap_dst & dst; |
| 501 | |
| 502 | |
| 503 | /*! \brief constructor |
| 504 | * |
| 505 | * \param src source encapsulated object |
| 506 | * \param dst source encapsulated object |
| 507 | * |
| 508 | */ |
| 509 | inline sparse_vector_reduction_solve_conflict_assign_cpu(encap_src & src, encap_dst & dst) |
| 510 | :src(src),dst(dst) |
| 511 | {}; |
| 512 | |
| 513 | //! It call the copy function for each property |
| 514 | template<typename T> |
| 515 | inline void operator()(T& t) const |
| 516 | { |
| 517 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 518 | |
| 519 | dst.template get<reduction_type::prop::value>() = src.template get<reduction_type::prop::value>(); |
| 520 | } |
| 521 | }; |
| 522 | |
| 523 | |
| 524 | template<unsigned int impl,typename vector_reduction, typename T,typename red_type> |
| 525 | struct sparse_vector_reduction_solve_conflict_reduce_cpu_impl |
| 526 | { |
| 527 | template<typename encap_src, typename encap_dst> |
| 528 | static inline void red(encap_src & src, encap_dst & dst) |
| 529 | { |
| 530 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 531 | |
| 532 | cpu_block_process<reduction_type,impl>::process(src.template get<reduction_type::prop::value>(),dst.template get<reduction_type::prop::value>()); |
| 533 | } |
| 534 | }; |
| 535 | |
| 536 | template<unsigned int impl, typename vector_reduction, typename T,typename red_type, unsigned int N1> |
| 537 | struct sparse_vector_reduction_solve_conflict_reduce_cpu_impl<impl,vector_reduction,T,red_type[N1]> |
| 538 | { |
| 539 | template<typename encap_src, typename encap_dst> |
| 540 | static inline void red(encap_src & src, encap_dst & dst) |
| 541 | { |
| 542 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 543 | |
| 544 | auto src_e = src.template get<reduction_type::prop::value>(); |
| 545 | auto dst_e = dst.template get<reduction_type::prop::value>(); |
| 546 | |
| 547 | cpu_block_process<reduction_type,impl+1>::template process_e<N1,red_type::size>(src_e,dst_e); |
| 548 | } |
| 549 | }; |
| 550 | |
| 551 | /*! \brief this class is a functor for "for_each" algorithm |
| 552 | * |
| 553 | * This class is a functor for "for_each" algorithm. For each |
| 554 | * element of the boost::vector the operator() is called. |
| 555 | * Is mainly used to copy one encap into another encap object |
| 556 | * |
| 557 | * \tparam encap source |
| 558 | * \tparam encap dst |
| 559 | * |
| 560 | */ |
| 561 | template<typename encap_src, |
| 562 | typename encap_dst, |
| 563 | typename vector_reduction, |
| 564 | unsigned int impl> |
| 565 | struct sparse_vector_reduction_solve_conflict_reduce_cpu |
| 566 | { |
| 567 | //! source |
| 568 | encap_src & src; |
| 569 | |
| 570 | //! destination |
| 571 | encap_dst & dst; |
| 572 | |
| 573 | |
| 574 | /*! \brief constructor |
| 575 | * |
| 576 | * \param src source encapsulated object |
| 577 | * \param dst source encapsulated object |
| 578 | * |
| 579 | */ |
| 580 | inline sparse_vector_reduction_solve_conflict_reduce_cpu(encap_src & src, encap_dst & dst) |
| 581 | :src(src),dst(dst) |
| 582 | {}; |
| 583 | |
| 584 | //! It call the copy function for each property |
| 585 | template<typename T> |
| 586 | inline void operator()(T& t) const |
| 587 | { |
| 588 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 589 | typedef typename boost::mpl::at<typename encap_src::T_type::type, typename reduction_type::prop>::type red_type; |
| 590 | |
| 591 | sparse_vector_reduction_solve_conflict_reduce_cpu_impl<impl,vector_reduction,T,red_type>::red(src,dst); |
| 592 | |
| 593 | // cpu_block_process<reduction_type,impl>::process(src.template get<reduction_type::prop::value>(),dst.template get<reduction_type::prop::value>()); |
| 594 | // reduction_type::red(dst.template get<reduction_type::prop::value>(),src.template get<reduction_type::prop::value>()); |
| 595 | } |
| 596 | }; |
| 597 | |
| 598 | /*! \brief this class is a functor for "for_each" algorithm |
| 599 | * |
| 600 | * This class is a functor for "for_each" algorithm. For each |
| 601 | * element of the boost::vector the operator() is called. |
| 602 | * Is mainly used to copy one encap into another encap object |
| 603 | * |
| 604 | * \tparam encap source |
| 605 | * \tparam encap dst |
| 606 | * |
| 607 | */ |
| 608 | template<typename vector_data_type, |
| 609 | typename vector_index_type, |
| 610 | typename vector_index_type2, |
| 611 | typename vector_reduction, |
| 612 | typename block_functor, |
| 613 | unsigned int impl2, unsigned int pSegment=1> |
| 614 | struct sparse_vector_reduction |
| 615 | { |
| 616 | //! Vector in which to the reduction |
| 617 | vector_data_type & vector_data_red; |
| 618 | |
| 619 | //! new datas |
| 620 | vector_data_type & vector_data; |
| 621 | |
| 622 | //! new data in an unsorted way |
| 623 | vector_data_type & vector_data_unsorted; |
| 624 | |
| 625 | //! segment of offsets |
| 626 | vector_index_type2 & segment_offset; |
| 627 | |
| 628 | //! map of the data |
| 629 | vector_index_type & vector_data_map; |
| 630 | |
| 631 | //! block functor |
| 632 | block_functor & blf; |
| 633 | |
| 634 | //! gpu context |
| 635 | mgpu::ofp_context_t & context; |
| 636 | |
| 637 | /*! \brief constructor |
| 638 | * |
| 639 | * \param src source encapsulated object |
| 640 | * \param dst source encapsulated object |
| 641 | * |
| 642 | */ |
| 643 | inline sparse_vector_reduction(vector_data_type & vector_data_red, |
| 644 | vector_data_type & vector_data, |
| 645 | vector_data_type & vector_data_unsorted, |
| 646 | vector_index_type & vector_data_map, |
| 647 | vector_index_type2 & segment_offset, |
| 648 | block_functor & blf, |
| 649 | mgpu::ofp_context_t & context) |
| 650 | :vector_data_red(vector_data_red), |
| 651 | vector_data(vector_data), |
| 652 | vector_data_unsorted(vector_data_unsorted), |
| 653 | segment_offset(segment_offset), |
| 654 | vector_data_map(vector_data_map), |
| 655 | blf(blf), |
| 656 | context(context) |
| 657 | {}; |
| 658 | |
| 659 | //! It call the copy function for each property |
| 660 | template<typename T> |
| 661 | inline void operator()(T& t) const |
| 662 | { |
| 663 | #ifdef __NVCC__ |
| 664 | |
| 665 | typedef typename boost::mpl::at<vector_reduction, T>::type reduction_type; |
| 666 | typedef typename boost::mpl::at<typename ValueTypeOf<vector_data_type>::type,typename reduction_type::prop>::type red_type; |
| 667 | if (reduction_type::is_special() == false) |
| 668 | { |
| 669 | scalar_block_implementation_switch<impl2, block_functor>::template segreduce<pSegment, vector_reduction, T>( |
| 670 | vector_data, |
| 671 | vector_data_unsorted, |
| 672 | vector_data_map, |
| 673 | segment_offset, |
| 674 | vector_data_red, |
| 675 | blf, |
| 676 | context); |
| 677 | } |
| 678 | #else |
| 679 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file is supposed to be compiled with nvcc"<< std::endl; |
| 680 | #endif |
| 681 | } |
| 682 | }; |
| 683 | |
| 684 | |
| 685 | struct stub_block_functor |
| 686 | { |
| 687 | template<unsigned int pSegment, typename vector_reduction, typename T, typename vector_index_type, typename vector_data_type> |
| 688 | static bool seg_reduce(vector_index_type & segments, vector_data_type & src, vector_data_type & dst) |
| 689 | { |
| 690 | return true; |
| 691 | } |
| 692 | |
| 693 | template<typename vector_index_type, typename vector_data_type, typename ... v_reduce> |
| 694 | static bool solve_conflicts(vector_index_type &keys, vector_index_type &merge_indices, |
| 695 | vector_data_type &data1, vector_data_type &data2, |
| 696 | vector_index_type &indices_tmp, vector_data_type &data_tmp, |
| 697 | vector_index_type &keysOut, vector_data_type &dataOut, |
| 698 | mgpu::ofp_context_t & context) |
| 699 | { |
| 700 | return true; |
| 701 | } |
| 702 | |
| 703 | openfpm::vector_gpu<aggregate<unsigned int>> outputMap; |
| 704 | |
| 705 | openfpm::vector_gpu<aggregate<unsigned int>> & get_outputMap() |
| 706 | { |
| 707 | return outputMap; |
| 708 | } |
| 709 | |
| 710 | const openfpm::vector_gpu<aggregate<unsigned int>> & get_outputMap() const |
| 711 | { |
| 712 | return outputMap; |
| 713 | } |
| 714 | }; |
| 715 | |
| 716 | /*! \brief this class is a functor for "for_each" algorithm |
| 717 | * |
| 718 | * This class is a functor for "for_each" algorithm. For each |
| 719 | * element of the boost::vector the operator() is called. |
| 720 | * Is mainly used to copy one encap into another encap object |
| 721 | * |
| 722 | * \tparam encap source |
| 723 | * \tparam encap dst |
| 724 | * |
| 725 | */ |
| 726 | template<typename vector_data_type, typename vector_index_type, typename vector_reduction> |
| 727 | struct sparse_vector_special |
| 728 | { |
| 729 | //! Vector in which to the reduction |
| 730 | vector_data_type & vector_data_red; |
| 731 | |
| 732 | //! Vector in which to the reduction |
| 733 | vector_data_type & vector_data; |
| 734 | |
| 735 | //! segment of offsets |
| 736 | vector_index_type & segment_offset; |
| 737 | |
| 738 | //! gpu context |
| 739 | mgpu::ofp_context_t & context; |
| 740 | |
| 741 | /*! \brief constructor |
| 742 | * |
| 743 | * \param src source encapsulated object |
| 744 | * \param dst source encapsulated object |
| 745 | * |
| 746 | */ |
| 747 | inline sparse_vector_special(vector_data_type & vector_data_red, |
| 748 | vector_data_type & vector_data, |
| 749 | vector_index_type & segment_offset, |
| 750 | mgpu::ofp_context_t & context) |
| 751 | :vector_data_red(vector_data_red),vector_data(vector_data),segment_offset(segment_offset),context(context) |
| 752 | {}; |
| 753 | |
| 754 | //! It call the copy function for each property |
| 755 | template<typename T> |
| 756 | inline void operator()(T& t) const |
| 757 | { |
| 758 | #ifdef __NVCC__ |
| 759 | |
| 760 | typedef typename boost::mpl::at<vector_reduction,T>::type reduction_type; |
| 761 | |
| 762 | // reduction type |
| 763 | typedef typename boost::mpl::at<typename vector_data_type::value_type::type,typename reduction_type::prop>::type red_type; |
| 764 | |
| 765 | if (reduction_type::is_special() == true) |
| 766 | { |
| 767 | auto ite = segment_offset.getGPUIterator(); |
| 768 | |
| 769 | CUDA_LAUNCH((reduce_from_offset<decltype(segment_offset.toKernel()),decltype(vector_data_red.toKernel()),reduction_type>), |
| 770 | ite,segment_offset.toKernel(),vector_data_red.toKernel(),vector_data.size()); |
| 771 | } |
| 772 | |
| 773 | #else |
| 774 | std::cout << __FILE__ << ":"<< __LINE__ << " error: this file si supposed to be compiled with nvcc"<< std::endl; |
| 775 | #endif |
| 776 | } |
| 777 | }; |
| 778 | |
| 779 | template<typename T, |
| 780 | typename Ti = long int, |
| 781 | typename Memory=HeapMemory, |
| 782 | typename layout=typename memory_traits_lin<T>::type, |
| 783 | template<typename> class layout_base=memory_traits_lin , |
| 784 | typename grow_p=grow_policy_double, |
| 785 | unsigned int impl=vect_isel<T>::value, |
| 786 | unsigned int impl2 = VECTOR_SPARSE_STANDARD, |
| 787 | typename block_functor = stub_block_functor> |
| 788 | class vector_sparse |
| 789 | { |
| 790 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_index; |
| 791 | vector<T,Memory,layout_base,grow_p,impl> vct_data; |
| 792 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_m_index; |
| 793 | |
| 794 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_add_index; |
| 795 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_rem_index; |
| 796 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_nadd_index; |
| 797 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_nrem_index; |
| 798 | vector<T,Memory,layout_base,grow_p> vct_add_data; |
| 799 | vector<T,Memory,layout_base,grow_p> vct_add_data_reord; |
| 800 | |
| 801 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_add_index_cont_0; |
| 802 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_add_index_cont_1; |
| 803 | vector<T,Memory,layout_base,grow_p> vct_add_data_cont; |
| 804 | vector<aggregate<Ti,Ti>,Memory,layout_base,grow_p> vct_add_index_unique; |
| 805 | vector<aggregate<int,int>,Memory,layout_base,grow_p> segments_int; |
| 806 | |
| 807 | vector<T,Memory,layout_base,grow_p,impl> vct_add_data_unique; |
| 808 | |
| 809 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_index_tmp4; |
| 810 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_index_tmp; |
| 811 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_index_tmp2; |
| 812 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_index_tmp3; |
| 813 | vector<aggregate<Ti,Ti,Ti>,Memory,layout_base,grow_p> vct_index_dtmp; |
| 814 | |
| 815 | // segments map (This is used only in case of Blocked data) |
| 816 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_segment_index_map; |
| 817 | |
| 818 | block_functor blf; |
| 819 | |
| 820 | T bck; |
| 821 | |
| 822 | CudaMemory mem; |
| 823 | |
| 824 | openfpm::vector<reorder<Ti>> reorder_add_index_cpu; |
| 825 | |
| 826 | size_t max_ele; |
| 827 | |
| 828 | int n_gpu_add_block_slot = 0; |
| 829 | int n_gpu_rem_block_slot = 0; |
| 830 | |
| 831 | /*! \brief get the element i |
| 832 | * |
| 833 | * search the element x |
| 834 | * |
| 835 | * \param i element i |
| 836 | */ |
| 837 | template<bool prefetch> |
| 838 | inline Ti _branchfree_search_nobck(Ti x, Ti & id) const |
| 839 | { |
| 840 | if (vct_index.size() == 0) {id = 0; return -1;} |
| 841 | const Ti *base = &vct_index.template get<0>(0); |
| 842 | const Ti *end = (const Ti *)vct_index.template getPointer<0>() + vct_index.size(); |
| 843 | Ti n = vct_data.size()-1; |
| 844 | while (n > 1) |
| 845 | { |
| 846 | Ti half = n / 2; |
| 847 | if (prefetch) |
| 848 | { |
| 849 | __builtin_prefetch(base + half/2, 0, 0); |
| 850 | __builtin_prefetch(base + half + half/2, 0, 0); |
| 851 | } |
| 852 | base = (base[half] < x) ? base+half : base; |
| 853 | n -= half; |
| 854 | } |
| 855 | |
| 856 | int off = (*base < x); |
| 857 | id = base - &vct_index.template get<0>(0) + off; |
| 858 | return (base + off != end)?*(base + off):-1; |
| 859 | } |
| 860 | |
| 861 | /*! \brief get the element i |
| 862 | * |
| 863 | * search the element x |
| 864 | * |
| 865 | * \param i element i |
| 866 | */ |
| 867 | template<bool prefetch> |
| 868 | inline void _branchfree_search(Ti x, Ti & id) const |
| 869 | { |
| 870 | Ti v = _branchfree_search_nobck<prefetch>(x,id); |
| 871 | id = (x == v)?id:vct_data.size()-1; |
| 872 | } |
| 873 | |
| 874 | |
| 875 | /* \brief take the indexes for the insertion pools and create a continuos array |
| 876 | * |
| 877 | * \param vct_nadd_index number of insertions of each pool |
| 878 | * \param vct_add_index pool of inserts |
| 879 | * \param vct_add_cont_index output continuos array of inserted indexes |
| 880 | * \param vct_add_data array of added data |
| 881 | * \param vct_add_data_cont continuos array of inserted data |
| 882 | * \param contect mgpu context |
| 883 | * |
| 884 | */ |
| 885 | size_t make_continuos(vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_nadd_index, |
| 886 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index, |
| 887 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_cont_index, |
| 888 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_cont_index_map, |
| 889 | vector<T,Memory,layout_base,grow_p> & vct_add_data, |
| 890 | vector<T,Memory,layout_base,grow_p> & vct_add_data_cont, |
| 891 | mgpu::ofp_context_t & context) |
| 892 | { |
| 893 | #ifdef __NVCC__ |
| 894 | |
| 895 | // Add 0 to the last element to vct_nadd_index |
| 896 | vct_nadd_index.resize(vct_nadd_index.size()+1); |
| 897 | vct_nadd_index.template get<0>(vct_nadd_index.size()-1) = 0; |
| 898 | vct_nadd_index.template hostToDevice<0>(vct_nadd_index.size()-1,vct_nadd_index.size()-1); |
| 899 | |
| 900 | // Merge the list of inserted points for each block |
| 901 | vct_index_tmp4.resize(vct_nadd_index.size()); |
| 902 | |
| 903 | openfpm::scan((Ti *)vct_nadd_index.template getDeviceBuffer<0>(), |
| 904 | vct_nadd_index.size(), |
| 905 | (Ti *)vct_index_tmp4.template getDeviceBuffer<0>() , |
| 906 | context); |
| 907 | |
| 908 | vct_index_tmp4.template deviceToHost<0>(vct_index_tmp4.size()-1,vct_index_tmp4.size()-1); |
| 909 | size_t n_ele = vct_index_tmp4.template get<0>(vct_index_tmp4.size()-1); |
| 910 | |
| 911 | // we reuse vct_nadd_index |
| 912 | vct_add_cont_index.resize(n_ele); |
| 913 | vct_add_cont_index_map.resize(n_ele); |
| 914 | |
| 915 | if (impl2 == VECTOR_SPARSE_STANDARD) |
| 916 | { |
| 917 | vct_add_data_cont.resize(n_ele); |
| 918 | } |
| 919 | else |
| 920 | { |
| 921 | vct_segment_index_map.resize(n_ele); |
| 922 | } |
| 923 | |
| 924 | if (n_gpu_add_block_slot >= 128) |
| 925 | { |
| 926 | ite_gpu<1> itew; |
| 927 | itew.wthr.x = vct_nadd_index.size()-1; |
| 928 | itew.wthr.y = 1; |
| 929 | itew.wthr.z = 1; |
| 930 | itew.thr.x = 128; |
| 931 | itew.thr.y = 1; |
| 932 | itew.thr.z = 1; |
| 933 | |
| 934 | CUDA_LAUNCH(construct_insert_list_key_only,itew,vct_add_index.toKernel(), |
| 935 | vct_nadd_index.toKernel(), |
| 936 | vct_index_tmp4.toKernel(), |
| 937 | vct_add_cont_index.toKernel(), |
| 938 | vct_add_cont_index_map.toKernel(), |
| 939 | n_gpu_add_block_slot); |
| 940 | } |
| 941 | else |
| 942 | { |
| 943 | auto itew = vct_add_index.getGPUIterator(); |
| 944 | |
| 945 | CUDA_LAUNCH(construct_insert_list_key_only_small_pool,itew,vct_add_index.toKernel(), |
| 946 | vct_nadd_index.toKernel(), |
| 947 | vct_index_tmp4.toKernel(), |
| 948 | vct_add_cont_index.toKernel(), |
| 949 | vct_add_cont_index_map.toKernel(), |
| 950 | n_gpu_add_block_slot); |
| 951 | } |
| 952 | |
| 953 | return n_ele; |
| 954 | #endif |
| 955 | return 0; |
| 956 | } |
| 957 | |
| 958 | /*! \brief sort the continuos array of inserted key |
| 959 | * |
| 960 | * \param context modern gpu context |
| 961 | * \param vct_add_cont_index array of indexes (unsorted), as output will be sorted |
| 962 | * \param vct_add_cont_index_map reference to the original indexes |
| 963 | * \param vct_add_data_reord sorted data output |
| 964 | * \param vct_add_data_cont added data in a continuos unsorted array |
| 965 | * |
| 966 | */ |
| 967 | void reorder_indexes(vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_cont_index, |
| 968 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_cont_index_map, |
| 969 | vector<T,Memory,layout_base,grow_p> & vct_add_data_reord, |
| 970 | vector<T,Memory,layout_base,grow_p> & vct_add_data_cont, |
| 971 | mgpu::ofp_context_t & context) |
| 972 | { |
| 973 | #ifdef __NVCC__ |
| 974 | ite_gpu<1> itew; |
| 975 | itew.wthr.x = vct_nadd_index.size()-1; |
| 976 | itew.wthr.y = 1; |
| 977 | itew.wthr.z = 1; |
| 978 | itew.thr.x = 128; |
| 979 | itew.thr.y = 1; |
| 980 | itew.thr.z = 1; |
| 981 | |
| 982 | size_t n_ele = vct_add_cont_index.size(); |
| 983 | |
| 984 | n_gpu_add_block_slot = 0; |
| 985 | |
| 986 | // now we sort |
| 987 | openfpm::sort( |
| 988 | (Ti *)vct_add_cont_index.template getDeviceBuffer<0>(), |
| 989 | (Ti *)vct_add_cont_index_map.template getDeviceBuffer<0>(), |
| 990 | vct_add_cont_index.size(), |
| 991 | mgpu::template less_t<Ti>(), |
| 992 | context); |
| 993 | |
| 994 | auto ite = vct_add_cont_index.getGPUIterator(); |
| 995 | |
| 996 | // Now we reorder the data vector accordingly to the indexes |
| 997 | |
| 998 | if (impl2 == VECTOR_SPARSE_STANDARD) |
| 999 | { |
| 1000 | vct_add_data_reord.resize(n_ele); |
| 1001 | CUDA_LAUNCH(reorder_vector_data,ite,vct_add_cont_index_map.toKernel(),vct_add_data_cont.toKernel(),vct_add_data_reord.toKernel()); |
| 1002 | } |
| 1003 | |
| 1004 | #endif |
| 1005 | } |
| 1006 | |
| 1007 | /*! \brief Merge indexes |
| 1008 | * |
| 1009 | * \param |
| 1010 | * |
| 1011 | * |
| 1012 | */ |
| 1013 | template<typename ... v_reduce> |
| 1014 | void merge_indexes(vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index_sort, |
| 1015 | vector<aggregate<Ti,Ti>,Memory,layout_base,grow_p> & vct_add_index_unique, |
| 1016 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_merge_index, |
| 1017 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_merge_index_map, |
| 1018 | mgpu::ofp_context_t & context) |
| 1019 | { |
| 1020 | #ifdef __NVCC__ |
| 1021 | |
| 1022 | typedef boost::mpl::vector<v_reduce...> vv_reduce; |
| 1023 | |
| 1024 | auto ite = vct_add_index_sort.getGPUIterator(); |
| 1025 | |
| 1026 | mem.allocate(sizeof(int)); |
| 1027 | mem.fill(0); |
| 1028 | vct_add_index_unique.resize(vct_add_index_sort.size()+1); |
| 1029 | |
| 1030 | ite = vct_add_index_sort.getGPUIterator(); |
| 1031 | |
| 1032 | vct_index_tmp4.resize(vct_add_index_sort.size()+1); |
| 1033 | |
| 1034 | CUDA_LAUNCH( |
| 1035 | ( |
| 1036 | find_buffer_offsets_for_scan |
| 1037 | <0, |
| 1038 | decltype(vct_add_index_sort.toKernel()), |
| 1039 | decltype(vct_index_tmp4.toKernel()) |
| 1040 | > |
| 1041 | ), |
| 1042 | ite, |
| 1043 | vct_add_index_sort.toKernel(), |
| 1044 | vct_index_tmp4.toKernel()); |
| 1045 | |
| 1046 | openfpm::scan((Ti *)vct_index_tmp4.template getDeviceBuffer<0>(),vct_index_tmp4.size(),(Ti *)vct_index_tmp4.template getDeviceBuffer<0>(),context); |
| 1047 | |
| 1048 | vct_index_tmp4.template deviceToHost<0>(vct_index_tmp4.size()-1,vct_index_tmp4.size()-1); |
| 1049 | int n_ele_unique = vct_index_tmp4.template get<0>(vct_index_tmp4.size()-1); |
| 1050 | |
| 1051 | vct_add_index_unique.resize(n_ele_unique); |
| 1052 | |
| 1053 | if (impl2 == VECTOR_SPARSE_STANDARD) |
| 1054 | { |
| 1055 | vct_add_data_unique.resize(n_ele_unique); |
| 1056 | } |
| 1057 | |
| 1058 | CUDA_LAUNCH( |
| 1059 | (construct_index_unique<0>), |
| 1060 | ite, |
| 1061 | vct_add_index_sort.toKernel(), |
| 1062 | vct_index_tmp4.toKernel(), |
| 1063 | vct_add_index_unique.toKernel()); |
| 1064 | |
| 1065 | typedef boost::mpl::vector<v_reduce...> vv_reduce; |
| 1066 | |
| 1067 | // Then we merge the two list vct_index and vct_add_index_unique |
| 1068 | |
| 1069 | // index to get merge index |
| 1070 | vct_m_index.resize(vct_index.size()); |
| 1071 | |
| 1072 | if (vct_m_index.size() != 0) |
| 1073 | { |
| 1074 | ite = vct_m_index.getGPUIterator(); |
| 1075 | CUDA_LAUNCH((set_indexes<0>),ite,vct_m_index.toKernel(),0); |
| 1076 | } |
| 1077 | |
| 1078 | // after merge we solve the last conflicts, running across the vector again and spitting 1 when there is something to merge |
| 1079 | // we reorder the data array also |
| 1080 | |
| 1081 | vct_merge_index.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1082 | vct_merge_index_map.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1083 | vct_index_tmp3.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1084 | |
| 1085 | // Do not delete this reserve |
| 1086 | // Unfortunately all resize with DataBlocks are broken |
| 1087 | if (impl2 == VECTOR_SPARSE_STANDARD) |
| 1088 | { |
| 1089 | vct_add_data_cont.reserve(vct_index.size() + vct_add_index_unique.size()+1); |
| 1090 | vct_add_data_cont.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1091 | } |
| 1092 | |
| 1093 | ite = vct_add_index_unique.getGPUIterator(); |
| 1094 | vct_index_tmp4.resize(vct_add_index_unique.size()); |
| 1095 | CUDA_LAUNCH((set_indexes<0>),ite,vct_index_tmp4.toKernel(),vct_index.size()); |
| 1096 | |
| 1097 | ite_gpu<1> itew; |
| 1098 | |
| 1099 | itew.wthr.x = vct_merge_index.size() / 128 + (vct_merge_index.size() % 128 != 0); |
| 1100 | itew.wthr.y = 1; |
| 1101 | itew.wthr.z = 1; |
| 1102 | itew.thr.x = 128; |
| 1103 | itew.thr.y = 1; |
| 1104 | itew.thr.z = 1; |
| 1105 | |
| 1106 | vct_index_dtmp.resize(itew.wthr.x); |
| 1107 | |
| 1108 | // we merge with vct_index with vct_add_index_unique in vct_merge_index, vct_merge_index contain the merged indexes |
| 1109 | // |
| 1110 | |
| 1111 | mgpu::merge((Ti *)vct_index.template getDeviceBuffer<0>(),(Ti *)vct_m_index.template getDeviceBuffer<0>(),vct_index.size(), |
| 1112 | (Ti *)vct_add_index_unique.template getDeviceBuffer<0>(),(Ti *)vct_index_tmp4.template getDeviceBuffer<0>(),vct_add_index_unique.size(), |
| 1113 | (Ti *)vct_merge_index.template getDeviceBuffer<0>(),(Ti *)vct_merge_index_map.template getDeviceBuffer<0>(),mgpu::less_t<Ti>(),context); |
| 1114 | |
| 1115 | |
| 1116 | #endif |
| 1117 | } |
| 1118 | |
| 1119 | |
| 1120 | |
| 1121 | template<typename ... v_reduce> |
| 1122 | void merge_datas(vector<T,Memory,layout_base,grow_p> & vct_add_data_reord, |
| 1123 | vector<aggregate<Ti,Ti>,Memory,layout_base,grow_p> & segments_new, |
| 1124 | vector<T,Memory,layout_base,grow_p> & vct_add_data, |
| 1125 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_data_reord_map, |
| 1126 | mgpu::ofp_context_t & context) |
| 1127 | { |
| 1128 | #ifdef __NVCC__ |
| 1129 | ite_gpu<1> itew; |
| 1130 | itew.wthr.x = vct_index_tmp.size() / 128 + (vct_index_tmp.size() % 128 != 0); |
| 1131 | itew.wthr.y = 1; |
| 1132 | itew.wthr.z = 1; |
| 1133 | itew.thr.x = 128; |
| 1134 | itew.thr.y = 1; |
| 1135 | itew.thr.z = 1; |
| 1136 | |
| 1137 | typedef boost::mpl::vector<v_reduce...> vv_reduce; |
| 1138 | |
| 1139 | //////////////////////////////////////////////////////////////////////////////////////////////////// |
| 1140 | |
| 1141 | // Now we can do a segmented reduction |
| 1142 | scalar_block_implementation_switch<impl2, block_functor> |
| 1143 | ::template extendSegments<1>(vct_add_index_unique, vct_add_data_reord_map.size()); |
| 1144 | |
| 1145 | if (impl2 == VECTOR_SPARSE_STANDARD) |
| 1146 | { |
| 1147 | sparse_vector_reduction<typename std::remove_reference<decltype(vct_add_data)>::type, |
| 1148 | decltype(vct_add_data_reord_map), |
| 1149 | decltype(vct_add_index_unique),vv_reduce,block_functor,impl2> |
| 1150 | svr( |
| 1151 | vct_add_data_unique, |
| 1152 | vct_add_data_reord, |
| 1153 | vct_add_data, |
| 1154 | vct_add_data_reord_map, |
| 1155 | vct_add_index_unique, |
| 1156 | blf, |
| 1157 | context); |
| 1158 | |
| 1159 | boost::mpl::for_each_ref<boost::mpl::range_c<int,0,sizeof...(v_reduce)>>(svr); |
| 1160 | } |
| 1161 | |
| 1162 | sparse_vector_special<typename std::remove_reference<decltype(vct_add_data)>::type, |
| 1163 | decltype(vct_add_index_unique), |
| 1164 | vv_reduce> svr2(vct_add_data_unique,vct_add_data_reord,vct_add_index_unique,context); |
| 1165 | boost::mpl::for_each_ref<boost::mpl::range_c<int,0,sizeof...(v_reduce)>>(svr2); |
| 1166 | |
| 1167 | ////////////////////////////////////////////////////////////////////////////////////////////////////// |
| 1168 | |
| 1169 | // Now perform the right solve_conflicts according to impl2 |
| 1170 | scalar_block_implementation_switch<impl2, block_functor>::template solveConflicts< |
| 1171 | decltype(vct_data), |
| 1172 | decltype(vct_index), |
| 1173 | decltype(segments_new), |
| 1174 | decltype(vct_index_dtmp), |
| 1175 | Ti, |
| 1176 | v_reduce ... |
| 1177 | > |
| 1178 | ( |
| 1179 | vct_index, |
| 1180 | vct_index_tmp, |
| 1181 | vct_index_tmp2, |
| 1182 | vct_index_tmp3, |
| 1183 | vct_index_dtmp, |
| 1184 | vct_add_data_reord_map, |
| 1185 | segments_new, |
| 1186 | vct_data, |
| 1187 | vct_add_data, |
| 1188 | vct_add_data_unique, |
| 1189 | vct_add_data_cont, |
| 1190 | itew, |
| 1191 | blf, |
| 1192 | context |
| 1193 | ); |
| 1194 | |
| 1195 | |
| 1196 | #else |
| 1197 | std::cout << __FILE__ << ":"<< __LINE__ << " error: you are supposed to compile this file with nvcc, if you want to use it with gpu"<< std::endl; |
| 1198 | #endif |
| 1199 | } |
| 1200 | |
| 1201 | template<typename ... v_reduce> |
| 1202 | void flush_on_gpu_insert(vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index_cont_0, |
| 1203 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index_cont_1, |
| 1204 | vector<T,Memory,layout_base,grow_p> & vct_add_data_reord, |
| 1205 | mgpu::ofp_context_t & context) |
| 1206 | { |
| 1207 | #ifdef __NVCC__ |
| 1208 | |
| 1209 | // To avoid the case where you never called setGPUInsertBuffer |
| 1210 | if (n_gpu_add_block_slot == 0 || vct_add_index.size() == 0) |
| 1211 | { |
| 1212 | return; |
| 1213 | } |
| 1214 | |
| 1215 | size_t n_ele = make_continuos(vct_nadd_index,vct_add_index,vct_add_index_cont_0,vct_add_index_cont_1, |
| 1216 | vct_add_data,vct_add_data_cont,context); |
| 1217 | |
| 1218 | // At this point we can check whether we have not inserted anything actually, |
| 1219 | // in this case, return without further ado... |
| 1220 | if (vct_add_index_cont_0.size() == 0) |
| 1221 | {return;} |
| 1222 | |
| 1223 | reorder_indexes(vct_add_index_cont_0,vct_add_index_cont_1,vct_add_data_reord,vct_add_data,context); |
| 1224 | |
| 1225 | merge_indexes<v_reduce ... >(vct_add_index_cont_0,vct_add_index_unique, |
| 1226 | vct_index_tmp,vct_index_tmp2, |
| 1227 | context); |
| 1228 | |
| 1229 | merge_datas<v_reduce ... >(vct_add_data_reord,vct_add_index_unique,vct_add_data,vct_add_index_cont_1,context); |
| 1230 | |
| 1231 | #else |
| 1232 | std::cout << __FILE__ << ":"<< __LINE__ << " error: you are supposed to compile this file with nvcc, if you want to use it with gpu"<< std::endl; |
| 1233 | #endif |
| 1234 | } |
| 1235 | |
| 1236 | |
| 1237 | void flush_on_gpu_remove( |
| 1238 | mgpu::ofp_context_t & context) |
| 1239 | { |
| 1240 | #ifdef __NVCC__ |
| 1241 | |
| 1242 | // Add 0 to the last element to vct_nadd_index |
| 1243 | vct_nrem_index.resize(vct_nrem_index.size()+1); |
| 1244 | vct_nrem_index.template get<0>(vct_nrem_index.size()-1) = 0; |
| 1245 | vct_nrem_index.template hostToDevice<0>(vct_nrem_index.size()-1,vct_nrem_index.size()-1); |
| 1246 | |
| 1247 | // Merge the list of inserted points for each block |
| 1248 | vct_index_tmp4.resize(vct_nrem_index.size()); |
| 1249 | |
| 1250 | openfpm::scan((Ti *)vct_nrem_index.template getDeviceBuffer<0>(), vct_nrem_index.size(), (Ti *)vct_index_tmp4.template getDeviceBuffer<0>() , context); |
| 1251 | |
| 1252 | vct_index_tmp4.template deviceToHost<0>(vct_index_tmp4.size()-1,vct_index_tmp4.size()-1); |
| 1253 | size_t n_ele = vct_index_tmp4.template get<0>(vct_index_tmp4.size()-1); |
| 1254 | |
| 1255 | // we reuse vct_nadd_index |
| 1256 | vct_add_index_cont_0.resize(n_ele); |
| 1257 | vct_add_index_cont_1.resize(n_ele); |
| 1258 | |
| 1259 | ite_gpu<1> itew; |
| 1260 | itew.wthr.x = vct_nrem_index.size()-1; |
| 1261 | itew.wthr.y = 1; |
| 1262 | itew.wthr.z = 1; |
| 1263 | itew.thr.x = 128; |
| 1264 | itew.thr.y = 1; |
| 1265 | itew.thr.z = 1; |
| 1266 | |
| 1267 | CUDA_LAUNCH(construct_remove_list,itew,vct_rem_index.toKernel(), |
| 1268 | vct_nrem_index.toKernel(), |
| 1269 | vct_index_tmp4.toKernel(), |
| 1270 | vct_add_index_cont_0.toKernel(), |
| 1271 | vct_add_index_cont_1.toKernel(), |
| 1272 | n_gpu_rem_block_slot); |
| 1273 | |
| 1274 | // now we sort |
| 1275 | openfpm::sort((Ti *)vct_add_index_cont_0.template getDeviceBuffer<0>(),(Ti *)vct_add_index_cont_1.template getDeviceBuffer<0>(), |
| 1276 | vct_add_index_cont_0.size(), mgpu::template less_t<Ti>(), context); |
| 1277 | |
| 1278 | auto ite = vct_add_index_cont_0.getGPUIterator(); |
| 1279 | |
| 1280 | mem.allocate(sizeof(int)); |
| 1281 | mem.fill(0); |
| 1282 | vct_add_index_unique.resize(vct_add_index_cont_0.size()+1); |
| 1283 | |
| 1284 | ite = vct_add_index_cont_0.getGPUIterator(); |
| 1285 | |
| 1286 | // produce unique index list |
| 1287 | // Find the buffer bases |
| 1288 | CUDA_LAUNCH((find_buffer_offsets_zero<0,decltype(vct_add_index_cont_0.toKernel()),decltype(vct_add_index_unique.toKernel())>), |
| 1289 | ite, |
| 1290 | vct_add_index_cont_0.toKernel(),(int *)mem.getDevicePointer(),vct_add_index_unique.toKernel()); |
| 1291 | |
| 1292 | mem.deviceToHost(); |
| 1293 | int n_ele_unique = *(int *)mem.getPointer(); |
| 1294 | |
| 1295 | vct_add_index_unique.resize(n_ele_unique); |
| 1296 | |
| 1297 | openfpm::sort((Ti *)vct_add_index_unique.template getDeviceBuffer<1>(),(Ti *)vct_add_index_unique.template getDeviceBuffer<0>(), |
| 1298 | vct_add_index_unique.size(),mgpu::template less_t<Ti>(),context); |
| 1299 | |
| 1300 | // Then we merge the two list vct_index and vct_add_index_unique |
| 1301 | |
| 1302 | // index to get merge index |
| 1303 | vct_m_index.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1304 | |
| 1305 | ite = vct_m_index.getGPUIterator(); |
| 1306 | CUDA_LAUNCH((set_indexes<0>),ite,vct_m_index.toKernel(),0); |
| 1307 | |
| 1308 | ite = vct_add_index_unique.getGPUIterator(); |
| 1309 | CUDA_LAUNCH((set_indexes<1>),ite,vct_add_index_unique.toKernel(),vct_index.size()); |
| 1310 | |
| 1311 | // after merge we solve the last conflicts, running across the vector again and spitting 1 when there is something to merge |
| 1312 | // we reorder the data array also |
| 1313 | |
| 1314 | vct_index_tmp.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1315 | vct_index_tmp2.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1316 | |
| 1317 | itew.wthr.x = vct_index_tmp.size() / 128 + (vct_index_tmp.size() % 128 != 0); |
| 1318 | itew.wthr.y = 1; |
| 1319 | itew.wthr.z = 1; |
| 1320 | itew.thr.x = 128; |
| 1321 | itew.thr.y = 1; |
| 1322 | itew.thr.z = 1; |
| 1323 | |
| 1324 | vct_index_dtmp.resize(itew.wthr.x); |
| 1325 | |
| 1326 | // we merge with vct_index with vct_add_index_unique in vct_index_tmp, vct_intex_tmp2 contain the merging index |
| 1327 | // |
| 1328 | mgpu::merge((Ti *)vct_index.template getDeviceBuffer<0>(),(Ti *)vct_m_index.template getDeviceBuffer<0>(),vct_index.size(), |
| 1329 | (Ti *)vct_add_index_unique.template getDeviceBuffer<0>(),(Ti *)vct_add_index_unique.template getDeviceBuffer<1>(),vct_add_index_unique.size(), |
| 1330 | (Ti *)vct_index_tmp.template getDeviceBuffer<0>(),(Ti *)vct_index_tmp2.template getDeviceBuffer<0>(),mgpu::less_t<Ti>(),context); |
| 1331 | |
| 1332 | vct_index_tmp3.resize(128*itew.wthr.x); |
| 1333 | |
| 1334 | CUDA_LAUNCH((solve_conflicts_remove<decltype(vct_index_tmp.toKernel()),decltype(vct_index_dtmp.toKernel()),128>), |
| 1335 | itew, |
| 1336 | vct_index_tmp.toKernel(), |
| 1337 | vct_index_tmp2.toKernel(), |
| 1338 | vct_index_tmp3.toKernel(), |
| 1339 | vct_m_index.toKernel(), |
| 1340 | vct_index_dtmp.toKernel(), |
| 1341 | vct_index.size()); |
| 1342 | |
| 1343 | // we scan tmp3 |
| 1344 | openfpm::scan((Ti*)vct_index_dtmp.template getDeviceBuffer<0>(),vct_index_dtmp.size(),(Ti *)vct_index_dtmp.template getDeviceBuffer<1>(),context); |
| 1345 | |
| 1346 | // get the size to resize vct_index and vct_data |
| 1347 | vct_index_dtmp.template deviceToHost<0,1>(vct_index_dtmp.size()-1,vct_index_dtmp.size()-1); |
| 1348 | int size = vct_index_dtmp.template get<1>(vct_index_dtmp.size()-1) + vct_index_dtmp.template get<0>(vct_index_dtmp.size()-1); |
| 1349 | |
| 1350 | vct_add_data_cont.resize(size); |
| 1351 | vct_index.resize(size); |
| 1352 | |
| 1353 | CUDA_LAUNCH(realign_remove,itew,vct_index_tmp3.toKernel(),vct_m_index.toKernel(),vct_data.toKernel(), |
| 1354 | vct_index.toKernel(),vct_add_data_cont.toKernel(), |
| 1355 | vct_index_dtmp.toKernel()); |
| 1356 | |
| 1357 | vct_data.swap(vct_add_data_cont); |
| 1358 | |
| 1359 | #else |
| 1360 | std::cout << __FILE__ << ":"<< __LINE__ << " error: you are suppose to compile this file with nvcc, if you want to use it with gpu"<< std::endl; |
| 1361 | #endif |
| 1362 | } |
| 1363 | |
| 1364 | void resetBck() |
| 1365 | { |
| 1366 | // re-add background |
| 1367 | vct_data.resize(vct_data.size()+1); |
| 1368 | vct_data.get(vct_data.size()-1) = bck; |
| 1369 | |
| 1370 | htoD<decltype(vct_data)> trf(vct_data,vct_data.size()-1); |
| 1371 | boost::mpl::for_each_ref< boost::mpl::range_c<int,0,T::max_prop> >(trf); |
| 1372 | } |
| 1373 | |
| 1374 | template<typename ... v_reduce> |
| 1375 | void flush_on_gpu(vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index_cont_0, |
| 1376 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index_cont_1, |
| 1377 | vector<T,Memory,layout_base,grow_p> & vct_add_data_reord, |
| 1378 | mgpu::ofp_context_t & context) |
| 1379 | { |
| 1380 | flush_on_gpu_insert<v_reduce ... >(vct_add_index_cont_0,vct_add_index_cont_1,vct_add_data_reord,context); |
| 1381 | } |
| 1382 | |
| 1383 | template<typename ... v_reduce> |
| 1384 | void flush_on_cpu() |
| 1385 | { |
| 1386 | if (vct_add_index.size() == 0) |
| 1387 | {return;} |
| 1388 | |
| 1389 | // First copy the added index to reorder |
| 1390 | reorder_add_index_cpu.resize(vct_add_index.size()); |
| 1391 | vct_add_data_cont.resize(vct_add_index.size()); |
| 1392 | |
| 1393 | for (size_t i = 0 ; i < reorder_add_index_cpu.size() ; i++) |
| 1394 | { |
| 1395 | reorder_add_index_cpu.get(i).id = vct_add_index.template get<0>(i); |
| 1396 | reorder_add_index_cpu.get(i).id2 = i; |
| 1397 | } |
| 1398 | |
| 1399 | reorder_add_index_cpu.sort(); |
| 1400 | |
| 1401 | // Copy the data |
| 1402 | for (size_t i = 0 ; i < reorder_add_index_cpu.size() ; i++) |
| 1403 | { |
| 1404 | vct_add_data_cont.get(i) = vct_add_data.get(reorder_add_index_cpu.get(i).id2); |
| 1405 | } |
| 1406 | |
| 1407 | typedef boost::mpl::vector<v_reduce...> vv_reduce; |
| 1408 | |
| 1409 | sparse_vector_reduction_cpu<decltype(vct_add_data), |
| 1410 | decltype(vct_add_index_unique), |
| 1411 | decltype(reorder_add_index_cpu), |
| 1412 | vv_reduce, |
| 1413 | impl2> |
| 1414 | svr(vct_add_data_unique, |
| 1415 | vct_add_data_cont, |
| 1416 | vct_add_index_unique, |
| 1417 | reorder_add_index_cpu); |
| 1418 | |
| 1419 | boost::mpl::for_each_ref<boost::mpl::range_c<int,0,sizeof...(v_reduce)>>(svr); |
| 1420 | |
| 1421 | // merge the the data |
| 1422 | |
| 1423 | vector<T,Memory,layout_base,grow_p,impl> vct_data_tmp; |
| 1424 | vector<aggregate<Ti>,Memory,layout_base,grow_p> vct_index_tmp; |
| 1425 | |
| 1426 | vct_data_tmp.resize(vct_data.size() + vct_add_data_unique.size()); |
| 1427 | vct_index_tmp.resize(vct_index.size() + vct_add_index_unique.size()); |
| 1428 | |
| 1429 | Ti di = 0; |
| 1430 | Ti ai = 0; |
| 1431 | size_t i = 0; |
| 1432 | |
| 1433 | for ( ; i < vct_data_tmp.size() ; i++) |
| 1434 | { |
| 1435 | Ti id_a = (ai < vct_add_index_unique.size())?vct_add_index_unique.template get<0>(ai):std::numeric_limits<Ti>::max(); |
| 1436 | Ti id_d = (di < vct_index.size())?vct_index.template get<0>(di):std::numeric_limits<Ti>::max(); |
| 1437 | |
| 1438 | if ( id_a <= id_d ) |
| 1439 | { |
| 1440 | vct_index_tmp.template get<0>(i) = id_a; |
| 1441 | |
| 1442 | if (id_a == id_d) |
| 1443 | { |
| 1444 | auto dst = vct_data_tmp.get(i); |
| 1445 | auto src = vct_add_data_unique.get(ai); |
| 1446 | |
| 1447 | sparse_vector_reduction_solve_conflict_assign_cpu<decltype(vct_data_tmp.get(i)), |
| 1448 | decltype(vct_add_data.get(ai)), |
| 1449 | vv_reduce> |
| 1450 | sva(src,dst); |
| 1451 | |
| 1452 | boost::mpl::for_each_ref<boost::mpl::range_c<int,0,sizeof...(v_reduce)>>(sva); |
| 1453 | ai++; |
| 1454 | |
| 1455 | dst = vct_data_tmp.get(i); |
| 1456 | src = vct_data.get(di); |
| 1457 | |
| 1458 | sparse_vector_reduction_solve_conflict_reduce_cpu<decltype(vct_data_tmp.get(i)), |
| 1459 | decltype(vct_data.get(di)), |
| 1460 | vv_reduce, |
| 1461 | impl2> |
| 1462 | svr(src,dst); |
| 1463 | boost::mpl::for_each_ref<boost::mpl::range_c<int,0,sizeof...(v_reduce)>>(svr); |
| 1464 | |
| 1465 | di++; |
| 1466 | |
| 1467 | vct_data_tmp.resize(vct_data_tmp.size()-1); |
| 1468 | vct_index_tmp.resize(vct_index_tmp.size()-1); |
| 1469 | } |
| 1470 | else |
| 1471 | { |
| 1472 | vct_index_tmp.template get<0>(i) = vct_add_index_unique.template get<0>(ai); |
| 1473 | vct_data_tmp.get(i) = vct_add_data_unique.get(ai); |
| 1474 | ai++; |
| 1475 | } |
| 1476 | } |
| 1477 | else |
| 1478 | { |
| 1479 | vct_index_tmp.template get<0>(i) = vct_index.template get<0>(di); |
| 1480 | vct_data_tmp.get(i) = vct_data.get(di); |
| 1481 | di++; |
| 1482 | } |
| 1483 | } |
| 1484 | |
| 1485 | vct_index.swap(vct_index_tmp); |
| 1486 | vct_data.swap(vct_data_tmp); |
| 1487 | |
| 1488 | vct_add_data.clear(); |
| 1489 | vct_add_index.clear(); |
| 1490 | vct_add_index_unique.clear(); |
| 1491 | vct_add_data_unique.clear(); |
| 1492 | } |
| 1493 | |
| 1494 | public: |
| 1495 | |
| 1496 | vector_sparse() |
| 1497 | :max_ele(0) |
| 1498 | { |
| 1499 | vct_data.resize(1); |
| 1500 | } |
| 1501 | |
| 1502 | /*! \brief Get the indices buffer |
| 1503 | * |
| 1504 | * \return the reference to the indices buffer |
| 1505 | */ |
| 1506 | auto getIndexBuffer() -> decltype(vct_index)& |
| 1507 | { |
| 1508 | return vct_index; |
| 1509 | } |
| 1510 | |
| 1511 | /*! \brief Get the data buffer |
| 1512 | * |
| 1513 | * \return the reference to the data buffer |
| 1514 | */ |
| 1515 | auto getDataBuffer() -> decltype(vct_data)& |
| 1516 | { |
| 1517 | return vct_data; |
| 1518 | } |
| 1519 | |
| 1520 | /*! \brief Get the indices buffer |
| 1521 | * |
| 1522 | * \return the reference to the indices buffer |
| 1523 | */ |
| 1524 | auto getIndexBuffer() const -> const decltype(vct_index)& |
| 1525 | { |
| 1526 | return vct_index; |
| 1527 | } |
| 1528 | |
| 1529 | /*! \brief Get the data buffer |
| 1530 | * |
| 1531 | * \return the reference to the data buffer |
| 1532 | */ |
| 1533 | auto getDataBuffer() const -> const decltype(vct_data)& |
| 1534 | { |
| 1535 | return vct_data; |
| 1536 | } |
| 1537 | |
| 1538 | /*! \brief Get the sparse index |
| 1539 | * |
| 1540 | * Get the sparse index of the element id |
| 1541 | * |
| 1542 | * \note use get_index and get to retrieve the value index associated to the sparse index |
| 1543 | * |
| 1544 | * \param id Element to get |
| 1545 | * |
| 1546 | * \return the element value requested |
| 1547 | * |
| 1548 | */ |
| 1549 | inline openfpm::sparse_index<Ti> get_sparse(Ti id) const |
| 1550 | { |
| 1551 | Ti di; |
| 1552 | this->_branchfree_search<false>(id,di); |
| 1553 | openfpm::sparse_index<Ti> sid; |
| 1554 | sid.id = di; |
| 1555 | |
| 1556 | return sid; |
| 1557 | } |
| 1558 | |
| 1559 | /*! \brief Get an element of the vector |
| 1560 | * |
| 1561 | * Get an element of the vector |
| 1562 | * |
| 1563 | * \tparam p Property to get |
| 1564 | * \param id Element to get |
| 1565 | * |
| 1566 | * \return the element value requested |
| 1567 | * |
| 1568 | */ |
| 1569 | template <unsigned int p> |
| 1570 | inline auto get(Ti id) const -> decltype(vct_data.template get<p>(id)) |
| 1571 | { |
| 1572 | Ti di; |
| 1573 | this->_branchfree_search<false>(id,di); |
| 1574 | return vct_data.template get<p>(di); |
| 1575 | } |
| 1576 | |
| 1577 | /*! \brief Get an element of the vector |
| 1578 | * |
| 1579 | * Get an element of the vector |
| 1580 | * |
| 1581 | * \tparam p Property to get |
| 1582 | * \param id Element to get |
| 1583 | * |
| 1584 | * \return the element value requested |
| 1585 | * |
| 1586 | */ |
| 1587 | inline auto get(Ti id) const -> decltype(vct_data.get(id)) |
| 1588 | { |
| 1589 | Ti di; |
| 1590 | this->_branchfree_search<false>(id,di); |
| 1591 | return vct_data.get(di); |
| 1592 | } |
| 1593 | |
| 1594 | /*! \brief resize to n elements |
| 1595 | * |
| 1596 | * \param n elements |
| 1597 | * |
| 1598 | */ |
| 1599 | void resize(size_t n) |
| 1600 | { |
| 1601 | max_ele = n; |
| 1602 | } |
| 1603 | |
| 1604 | /*! \brief |
| 1605 | * |
| 1606 | * \warning After using this function to move out the vector of the indexes, this object become useless and |
| 1607 | * must be destroyed |
| 1608 | * |
| 1609 | * \param iv |
| 1610 | * |
| 1611 | */ |
| 1612 | void swapIndexVector(vector<aggregate<Ti>,Memory,layout_base,grow_p> & iv) |
| 1613 | { |
| 1614 | vct_index.swap(iv); |
| 1615 | } |
| 1616 | |
| 1617 | /*! \brief Set the background to bck (which value get must return when the value is not find) |
| 1618 | * |
| 1619 | * \param bck |
| 1620 | * |
| 1621 | */ |
| 1622 | template <unsigned int p> |
| 1623 | auto getBackground() const -> decltype(vct_data.template get<p>(vct_data.size()-1)) |
| 1624 | { |
| 1625 | return vct_data.template get<p>(vct_data.size()-1); |
| 1626 | } |
| 1627 | |
| 1628 | /*! \brief Set the background to bck (which value get must return when the value is not find) |
| 1629 | * |
| 1630 | * \param bck |
| 1631 | * |
| 1632 | */ |
| 1633 | auto getBackground() const -> decltype(vct_data.get(vct_data.size()-1)) |
| 1634 | { |
| 1635 | return vct_data.get(vct_data.size()-1); |
| 1636 | } |
| 1637 | |
| 1638 | template<unsigned int p> |
| 1639 | void setBackground(const typename boost::mpl::at<typename T::type, boost::mpl::int_<p>>::type & bck_) |
| 1640 | { |
| 1641 | meta_copy_d<typename boost::mpl::at<typename T::type, boost::mpl::int_<p>>::type, |
| 1642 | typename std::remove_reference<decltype(vct_data.template get<p>(vct_data.size()-1))>::type> |
| 1643 | ::meta_copy_d_(bck_,vct_data.template get<p>(vct_data.size()-1)); |
| 1644 | |
| 1645 | vct_data.template hostToDevice<p>(vct_data.size()-1,vct_data.size()-1); |
| 1646 | |
| 1647 | meta_copy<typename boost::mpl::at<typename T::type, boost::mpl::int_<p>>::type> |
| 1648 | ::meta_copy_(bck_,bck.template get<p>()); |
| 1649 | } |
| 1650 | |
| 1651 | /*! \brief It insert an element in the sparse vector |
| 1652 | * |
| 1653 | * \tparam p property id |
| 1654 | * |
| 1655 | * \param ele element id |
| 1656 | * |
| 1657 | */ |
| 1658 | template <unsigned int p> |
| 1659 | auto insert(Ti ele) -> decltype(vct_data.template get<p>(0)) |
| 1660 | { |
| 1661 | vct_add_index.add(); |
| 1662 | vct_add_index.template get<0>(vct_add_index.size()-1) = ele; |
| 1663 | vct_add_data.add(); |
| 1664 | return vct_add_data.template get<p>(vct_add_data.size()-1); |
| 1665 | } |
| 1666 | |
| 1667 | /*! \brief It insert an element in the sparse vector |
| 1668 | * |
| 1669 | * \tparam p property id |
| 1670 | * |
| 1671 | * \param ele element id |
| 1672 | * |
| 1673 | */ |
| 1674 | template <unsigned int p> |
| 1675 | auto insertFlush(Ti ele) -> decltype(vct_data.template get<p>(0)) |
| 1676 | { |
| 1677 | size_t di; |
| 1678 | |
| 1679 | // first we have to search if the block exist |
| 1680 | Ti v = _branchfree_search_nobck(ele,di); |
| 1681 | |
| 1682 | if (v == ele) |
| 1683 | { |
| 1684 | // block exist |
| 1685 | return vct_data.template get<p>(di); |
| 1686 | } |
| 1687 | |
| 1688 | // It does not exist, we create it di contain the index where we have to create the new block |
| 1689 | vct_index.insert(di); |
| 1690 | vct_data.isert(di); |
| 1691 | |
| 1692 | return vct_data.template get<p>(di); |
| 1693 | } |
| 1694 | |
| 1695 | /*! \brief It insert an element in the sparse vector |
| 1696 | * |
| 1697 | * \param ele element id |
| 1698 | * |
| 1699 | */ |
| 1700 | auto insertFlush(Ti ele) -> decltype(vct_data.get(0)) |
| 1701 | { |
| 1702 | Ti di; |
| 1703 | |
| 1704 | // first we have to search if the block exist |
| 1705 | Ti v = _branchfree_search_nobck<true>(ele,di); |
| 1706 | |
| 1707 | if (v == ele) |
| 1708 | { |
| 1709 | // block exist |
| 1710 | return vct_data.get(di); |
| 1711 | } |
| 1712 | |
| 1713 | // It does not exist, we create it di contain the index where we have to create the new block |
| 1714 | vct_index.insert(di); |
| 1715 | vct_data.insert(di); |
| 1716 | |
| 1717 | vct_index.template get<0>(di) = ele; |
| 1718 | |
| 1719 | return vct_data.get(di); |
| 1720 | } |
| 1721 | |
| 1722 | /*! \brief It insert an element in the sparse vector |
| 1723 | * |
| 1724 | * \param ele element id |
| 1725 | * |
| 1726 | */ |
| 1727 | auto insert(Ti ele) -> decltype(vct_data.get(0)) |
| 1728 | { |
| 1729 | vct_add_index.add(); |
| 1730 | vct_add_index.template get<0>(vct_add_index.size()-1) = ele; |
| 1731 | vct_add_data.add(); |
| 1732 | return vct_add_data.get(vct_add_data.size()-1); |
| 1733 | } |
| 1734 | |
| 1735 | /*! \brief merge the added element to the main data array but save the insert buffer in v |
| 1736 | * |
| 1737 | * \param v insert buffer |
| 1738 | * |
| 1739 | * \param opt options |
| 1740 | * |
| 1741 | */ |
| 1742 | template<typename ... v_reduce> |
| 1743 | void flush_v(vector<aggregate<Ti>,Memory,layout_base,grow_p> & vct_add_index_cont_0, |
| 1744 | mgpu::ofp_context_t & context, |
| 1745 | flush_type opt = FLUSH_ON_HOST, |
| 1746 | int i = 0) |
| 1747 | { |
| 1748 | // Eliminate background |
| 1749 | vct_data.resize(vct_index.size()); |
| 1750 | |
| 1751 | if (opt & flush_type::FLUSH_ON_DEVICE) |
| 1752 | {this->flush_on_gpu<v_reduce ... >(vct_add_index_cont_0,vct_add_index_cont_1,vct_add_data_reord,context,i);} |
| 1753 | else |
| 1754 | {this->flush_on_cpu<v_reduce ... >();} |
| 1755 | |
| 1756 | resetBck(); |
| 1757 | } |
| 1758 | |
| 1759 | /*! \brief merge the added element to the main data array but save the insert buffer in v |
| 1760 | * |
| 1761 | * \param v insert buffer |
| 1762 | * |
| 1763 | * \param opt options |
| 1764 | * |
| 1765 | */ |
| 1766 | template<typename ... v_reduce> |
| 1767 | void flush_vd(vector<T,Memory,layout_base,grow_p> & vct_add_data_reord, |
| 1768 | mgpu::ofp_context_t & context, |
| 1769 | flush_type opt = FLUSH_ON_HOST) |
| 1770 | { |
| 1771 | // Eliminate background |
| 1772 | vct_data.resize(vct_index.size()); |
| 1773 | |
| 1774 | if (opt & flush_type::FLUSH_ON_DEVICE) |
| 1775 | {this->flush_on_gpu<v_reduce ... >(vct_add_index_cont_0,vct_add_index_cont_1,vct_add_data_reord,context);} |
| 1776 | else |
| 1777 | {this->flush_on_cpu<v_reduce ... >();} |
| 1778 | |
| 1779 | resetBck(); |
| 1780 | } |
| 1781 | |
| 1782 | /*! \brief merge the added element to the main data array |
| 1783 | * |
| 1784 | * \param opt options |
| 1785 | * |
| 1786 | */ |
| 1787 | template<typename ... v_reduce> |
| 1788 | void flush(mgpu::ofp_context_t & context, flush_type opt = FLUSH_ON_HOST) |
| 1789 | { |
| 1790 | // Eliminate background |
| 1791 | vct_data.resize(vct_index.size()); |
| 1792 | |
| 1793 | if (opt & flush_type::FLUSH_ON_DEVICE) |
| 1794 | {this->flush_on_gpu<v_reduce ... >(vct_add_index_cont_0,vct_add_index_cont_1,vct_add_data_reord,context);} |
| 1795 | else |
| 1796 | {this->flush_on_cpu<v_reduce ... >();} |
| 1797 | |
| 1798 | resetBck(); |
| 1799 | } |
| 1800 | |
| 1801 | /*! \brief merge the added element to the main data array |
| 1802 | * |
| 1803 | * \param opt options |
| 1804 | * |
| 1805 | */ |
| 1806 | void flush_remove(mgpu::ofp_context_t & context, flush_type opt = FLUSH_ON_HOST) |
| 1807 | { |
| 1808 | vct_data.resize(vct_data.size()-1); |
| 1809 | |
| 1810 | if (opt & flush_type::FLUSH_ON_DEVICE) |
| 1811 | {this->flush_on_gpu_remove(context);} |
| 1812 | else |
| 1813 | { |
| 1814 | std::cerr << __FILE__ << ":"<< __LINE__ << " error, flush_remove on CPU has not implemented yet"; |
| 1815 | } |
| 1816 | |
| 1817 | resetBck(); |
| 1818 | } |
| 1819 | |
| 1820 | /*! \brief Return how many element you have in this map |
| 1821 | * |
| 1822 | * \return the number of elements |
| 1823 | */ |
| 1824 | size_t size() |
| 1825 | { |
| 1826 | return vct_index.size(); |
| 1827 | } |
| 1828 | |
| 1829 | /*! \brief Return the sorted vector of the indexes |
| 1830 | * |
| 1831 | * \return return the sorted vector of the indexes |
| 1832 | */ |
| 1833 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & |
| 1834 | private_get_vct_index() |
| 1835 | { |
| 1836 | return vct_index; |
| 1837 | } |
| 1838 | |
| 1839 | /*! \brief Transfer from device to host |
| 1840 | * |
| 1841 | * \tparam set of parameters to transfer to host |
| 1842 | * |
| 1843 | */ |
| 1844 | template<unsigned int ... prp> |
| 1845 | void deviceToHost() |
| 1846 | { |
| 1847 | vct_index.template deviceToHost<0>(); |
| 1848 | vct_data.template deviceToHost<prp...>(); |
| 1849 | } |
| 1850 | |
| 1851 | /*! \brief Transfer from host to device |
| 1852 | * |
| 1853 | * \tparam set of parameters to transfer to device |
| 1854 | * |
| 1855 | */ |
| 1856 | template<unsigned int ... prp> |
| 1857 | void hostToDevice() |
| 1858 | { |
| 1859 | vct_index.template hostToDevice<0>(); |
| 1860 | vct_data.template hostToDevice<prp...>(); |
| 1861 | } |
| 1862 | |
| 1863 | /*! \brief toKernel function transform this structure into one that can be used on GPU |
| 1864 | * |
| 1865 | * \return structure that can be used on GPU |
| 1866 | * |
| 1867 | */ |
| 1868 | vector_sparse_gpu_ker<T,Ti,layout_base> toKernel() |
| 1869 | { |
| 1870 | vector_sparse_gpu_ker<T,Ti,layout_base> mvsck(vct_index.toKernel(),vct_data.toKernel(), |
| 1871 | vct_add_index.toKernel(), |
| 1872 | vct_rem_index.toKernel(),vct_add_data.toKernel(), |
| 1873 | vct_nadd_index.toKernel(), |
| 1874 | vct_nrem_index.toKernel(), |
| 1875 | n_gpu_add_block_slot, |
| 1876 | n_gpu_rem_block_slot); |
| 1877 | |
| 1878 | return mvsck; |
| 1879 | } |
| 1880 | |
| 1881 | /*! \brief set the gpu insert buffer for every block |
| 1882 | * |
| 1883 | * \param nblock number of blocks |
| 1884 | * \param nslot number of slots free for each block |
| 1885 | * |
| 1886 | */ |
| 1887 | void setGPUInsertBuffer(int nblock, int nslot) |
| 1888 | { |
| 1889 | vct_add_index.resize(nblock*nslot); |
| 1890 | vct_nadd_index.resize(nblock); |
| 1891 | vct_add_data.resize(nblock*nslot); |
| 1892 | n_gpu_add_block_slot = nslot; |
| 1893 | vct_nadd_index.template fill<0>(0); |
| 1894 | } |
| 1895 | |
| 1896 | /*! \brief In case we manually set the added index buffer and the add data buffer we have to call this |
| 1897 | * function before flush |
| 1898 | * |
| 1899 | * |
| 1900 | */ |
| 1901 | void preFlush() |
| 1902 | { |
| 1903 | #ifdef __NVCC__ |
| 1904 | vct_nadd_index.resize(vct_add_index.size()); |
| 1905 | |
| 1906 | if (vct_nadd_index.size() != 0) |
| 1907 | { |
| 1908 | auto ite = vct_nadd_index.getGPUIterator(); |
| 1909 | CUDA_LAUNCH((set_one_insert_buffer),ite,vct_nadd_index.toKernel()); |
| 1910 | } |
| 1911 | n_gpu_add_block_slot = 1; |
| 1912 | #endif |
| 1913 | } |
| 1914 | |
| 1915 | /*! \brief Get the GPU insert buffer |
| 1916 | * |
| 1917 | * \return the reference to the GPU insert buffer |
| 1918 | */ |
| 1919 | auto getGPUInsertBuffer() -> decltype(vct_add_data)& |
| 1920 | { |
| 1921 | return vct_add_data; |
| 1922 | } |
| 1923 | |
| 1924 | /*! \brief set the gpu remove buffer for every block |
| 1925 | * |
| 1926 | * \param nblock number of blocks |
| 1927 | * \param nslot number of slots free for each block |
| 1928 | * |
| 1929 | */ |
| 1930 | void setGPURemoveBuffer(int nblock, int nslot) |
| 1931 | { |
| 1932 | vct_rem_index.resize(nblock*nslot); |
| 1933 | vct_nrem_index.resize(nblock); |
| 1934 | n_gpu_rem_block_slot = nslot; |
| 1935 | vct_nrem_index.template fill<0>(0); |
| 1936 | } |
| 1937 | |
| 1938 | #ifdef CUDA_GPU |
| 1939 | |
| 1940 | /*! \brief Get iterator over the stored elements |
| 1941 | * |
| 1942 | * \return an iterator |
| 1943 | * |
| 1944 | */ |
| 1945 | auto getGPUIterator() -> decltype(vct_index.getGPUIterator()) |
| 1946 | { |
| 1947 | return vct_index.getGPUIterator(); |
| 1948 | } |
| 1949 | |
| 1950 | #endif |
| 1951 | |
| 1952 | /*! \brief Clear all from all the elements |
| 1953 | * |
| 1954 | * |
| 1955 | */ |
| 1956 | void clear() |
| 1957 | { |
| 1958 | vct_data.clear(); |
| 1959 | vct_index.clear(); |
| 1960 | vct_add_index.clear(); |
| 1961 | vct_add_data.clear(); |
| 1962 | |
| 1963 | // re-add background |
| 1964 | vct_data.resize(vct_data.size()+1); |
| 1965 | vct_data.get(vct_data.size()-1) = bck; |
| 1966 | |
| 1967 | htoD<decltype(vct_data)> trf(vct_data,vct_data.size()-1); |
| 1968 | boost::mpl::for_each_ref< boost::mpl::range_c<int,0,T::max_prop> >(trf); |
| 1969 | |
| 1970 | max_ele = 0; |
| 1971 | n_gpu_add_block_slot = 0; |
| 1972 | n_gpu_rem_block_slot = 0; |
| 1973 | } |
| 1974 | |
| 1975 | void swap(vector_sparse<T,Ti,Memory,layout,layout_base,grow_p,impl> & sp) |
| 1976 | { |
| 1977 | vct_data.swap(sp.vct_data); |
| 1978 | vct_index.swap(sp.vct_index); |
| 1979 | vct_add_index.swap(sp.vct_add_index); |
| 1980 | vct_add_data.swap(sp.vct_add_data); |
| 1981 | |
| 1982 | size_t max_ele_ = sp.max_ele; |
| 1983 | sp.max_ele = max_ele; |
| 1984 | this->max_ele = max_ele_; |
| 1985 | } |
| 1986 | |
| 1987 | vector<T,Memory,layout_base,grow_p> & private_get_vct_add_data() |
| 1988 | { |
| 1989 | return vct_add_data; |
| 1990 | } |
| 1991 | |
| 1992 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & private_get_vct_add_index() |
| 1993 | { |
| 1994 | return vct_add_index; |
| 1995 | } |
| 1996 | |
| 1997 | const vector<aggregate<Ti>,Memory,layout_base,grow_p> & private_get_vct_add_index() const |
| 1998 | { |
| 1999 | return vct_add_index; |
| 2000 | } |
| 2001 | |
| 2002 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & private_get_vct_nadd_index() |
| 2003 | { |
| 2004 | return vct_nadd_index; |
| 2005 | } |
| 2006 | |
| 2007 | const vector<aggregate<Ti>,Memory,layout_base,grow_p> & private_get_vct_nadd_index() const |
| 2008 | { |
| 2009 | return vct_nadd_index; |
| 2010 | } |
| 2011 | |
| 2012 | auto getSegmentToOutMap() -> decltype(blf.get_outputMap()) |
| 2013 | { |
| 2014 | return blf.get_outputMap(); |
| 2015 | } |
| 2016 | |
| 2017 | auto getSegmentToOutMap() const -> decltype(blf.get_outputMap()) |
| 2018 | { |
| 2019 | return blf.get_outputMap(); |
| 2020 | } |
| 2021 | |
| 2022 | /*! \brief Eliminate many internal temporary buffer you can use this between flushes if you get some out of memory |
| 2023 | * |
| 2024 | * |
| 2025 | */ |
| 2026 | void removeUnusedBuffers() |
| 2027 | { |
| 2028 | vct_add_data.resize(0); |
| 2029 | vct_add_data.shrink_to_fit(); |
| 2030 | |
| 2031 | vct_add_data.resize(0); |
| 2032 | vct_add_data.shrink_to_fit(); |
| 2033 | |
| 2034 | vct_add_data_reord.resize(0); |
| 2035 | vct_add_data_reord.shrink_to_fit(); |
| 2036 | |
| 2037 | vct_add_data_cont.resize(0); |
| 2038 | vct_add_data_cont.shrink_to_fit(); |
| 2039 | |
| 2040 | vct_add_data_unique.resize(0); |
| 2041 | vct_add_data_unique.shrink_to_fit(); |
| 2042 | } |
| 2043 | |
| 2044 | /* \brief Return the offsets of the segments for the merge indexes |
| 2045 | * |
| 2046 | * |
| 2047 | */ |
| 2048 | vector<aggregate<Ti,Ti>,Memory,layout_base,grow_p> & getSegmentToMergeIndexMap() |
| 2049 | { |
| 2050 | return vct_add_index_unique; |
| 2051 | } |
| 2052 | |
| 2053 | vector<aggregate<Ti,Ti>,Memory,layout_base,grow_p> & getSegmentToMergeIndexMap() const |
| 2054 | { |
| 2055 | return vct_add_index_unique; |
| 2056 | } |
| 2057 | |
| 2058 | /*! \brief Return the mapping vector |
| 2059 | * |
| 2060 | * When we add new elements this vector contain the merged old elements and new elements position |
| 2061 | * |
| 2062 | * For example the old vector contain |
| 2063 | * |
| 2064 | * Old: 5 10 35 50 66 79 (6 elements) |
| 2065 | * New: 7 44 7 9 44 (5 elements) (in order are 7 7 9 44 44) |
| 2066 | * |
| 2067 | * The merged indexes are (when reordered) |
| 2068 | * |
| 2069 | * 5 7 7 9 10 35 44 44 50 66 79 |
| 2070 | * |
| 2071 | * The returned map contain 5 elements indicating the position of the reordered elements: |
| 2072 | * |
| 2073 | * 0 2 3 1 4 |
| 2074 | * (7)(7)(9)(44)(44) |
| 2075 | */ |
| 2076 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & getMappingVector() |
| 2077 | { |
| 2078 | return vct_add_index_cont_1; |
| 2079 | } |
| 2080 | |
| 2081 | /*! \brief Return the merge mapping vector |
| 2082 | * |
| 2083 | * When we add new elements this vector contain the merged old elements and new elements position |
| 2084 | * |
| 2085 | * For example the old vector contain |
| 2086 | * |
| 2087 | * Old: 5 10 35 50 66 79 (6 elements) |
| 2088 | * New: 7 44 7 9 44 (5 elements) (in order are 7 7 9 44 44) |
| 2089 | * |
| 2090 | * The merged indexes are (when reordered) |
| 2091 | * |
| 2092 | * 5 7 7 9 10 35 44 44 50 66 79 |
| 2093 | * |
| 2094 | * The returned map contain 5 elements indicating the position of the reordered elements: |
| 2095 | * |
| 2096 | * 0 6 7 8 1 2 9 10 3 4 5 |
| 2097 | * (5)(7)(7)(9)(10)(35)(44)(44)(50)(66)(79) |
| 2098 | */ |
| 2099 | vector<aggregate<Ti>,Memory,layout_base,grow_p> & getMergeIndexMapVector() |
| 2100 | { |
| 2101 | return vct_index_tmp2; |
| 2102 | } |
| 2103 | }; |
| 2104 | |
| 2105 | |
| 2106 | template<typename T, unsigned int blockSwitch = VECTOR_SPARSE_STANDARD, typename block_functor = stub_block_functor, typename indexT = int> |
| 2107 | using vector_sparse_gpu = openfpm::vector_sparse< |
| 2108 | T, |
| 2109 | indexT, |
| 2110 | CudaMemory, |
| 2111 | typename memory_traits_inte<T>::type, |
| 2112 | memory_traits_inte, |
| 2113 | grow_policy_double, |
| 2114 | vect_isel<T>::value, |
| 2115 | blockSwitch, |
| 2116 | block_functor |
| 2117 | >; |
| 2118 | |
| 2119 | template<typename T, typename block_functor = stub_block_functor, typename indexT = long int> |
| 2120 | using vector_sparse_gpu_block = openfpm::vector_sparse< |
| 2121 | T, |
| 2122 | indexT, |
| 2123 | CudaMemory, |
| 2124 | typename memory_traits_inte<T>::type, |
| 2125 | memory_traits_inte, |
| 2126 | grow_policy_double, |
| 2127 | vect_isel<T>::value, |
| 2128 | VECTOR_SPARSE_BLOCK, |
| 2129 | block_functor |
| 2130 | >; |
| 2131 | } |
| 2132 | |
| 2133 | |
| 2134 | |
| 2135 | #endif /* MAP_VECTOR_SPARSE_HPP_ */ |
| 2136 |
Generated while processing openfpm/openfpm_data/src/NN/Mem_type/Mem_type_unit_tests.cpp
Generated on 2021-Mar-13 from project openfpm
Powered by 
Code Browser 2.1
Generator usage only permitted with license.