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|---|---|---|---|
| 1 | #include "sosnina_a_radix_simple_merge/all/include/ops_all.hpp" | ||
| 2 | |||
| 3 | #include <mpi.h> | ||
| 4 | |||
| 5 | #include <algorithm> | ||
| 6 | #include <array> | ||
| 7 | #include <cstddef> | ||
| 8 | #include <cstdint> | ||
| 9 | #include <thread> | ||
| 10 | #include <type_traits> | ||
| 11 | #include <utility> | ||
| 12 | #include <vector> | ||
| 13 | |||
| 14 | #include "sosnina_a_radix_simple_merge/common/include/common.hpp" | ||
| 15 | #include "util/include/util.hpp" | ||
| 16 | |||
| 17 | namespace sosnina_a_radix_simple_merge { | ||
| 18 | |||
| 19 | namespace { | ||
| 20 | |||
| 21 | constexpr int kRadixBits = 8; | ||
| 22 | constexpr int kRadixSize = 1 << kRadixBits; | ||
| 23 | constexpr int kNumPasses = sizeof(int) / sizeof(uint8_t); | ||
| 24 | constexpr uint32_t kSignFlip = 0x80000000U; | ||
| 25 | constexpr size_t kMinElementsPerPart = 4096; | ||
| 26 | constexpr size_t kMinElementsPerPartSmall = 32768; | ||
| 27 | constexpr size_t kSmallArrayThreshold = 1'000'000; | ||
| 28 | constexpr size_t kLargeArrayThreshold = 20'000'000; | ||
| 29 | |||
| 30 | 160 | void RadixSortLSD(std::vector<int> &data, std::vector<int> &buffer) { | |
| 31 | size_t idx = 0; | ||
| 32 |
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474 | for (int elem : data) { |
| 33 | 314 | buffer[idx++] = static_cast<int>(static_cast<uint32_t>(elem) ^ kSignFlip); | |
| 34 | } | ||
| 35 | std::swap(data, buffer); | ||
| 36 | |||
| 37 |
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800 | for (int pass = 0; pass < kNumPasses; ++pass) { |
| 38 | 640 | std::array<int, kRadixSize + 1> count{}; | |
| 39 | |||
| 40 |
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1896 | for (auto elem : data) { |
| 41 | 1256 | auto digit = static_cast<uint8_t>((static_cast<uint32_t>(elem) >> (pass * kRadixBits)) & 0xFF); | |
| 42 | 1256 | ++count.at(static_cast<size_t>(digit) + 1U); | |
| 43 | } | ||
| 44 | |||
| 45 |
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164480 | for (int i = 1; i <= kRadixSize; ++i) { |
| 46 | 163840 | const auto ui = static_cast<size_t>(i); | |
| 47 | 163840 | count.at(ui) += count.at(ui - 1U); | |
| 48 | } | ||
| 49 | |||
| 50 |
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1896 | for (auto elem : data) { |
| 51 | 1256 | auto digit = static_cast<uint8_t>((static_cast<uint32_t>(elem) >> (pass * kRadixBits)) & 0xFF); | |
| 52 | 1256 | const auto di = static_cast<size_t>(digit); | |
| 53 | 1256 | const int write_pos = count.at(di)++; | |
| 54 | 1256 | buffer[static_cast<size_t>(write_pos)] = elem; | |
| 55 | } | ||
| 56 | |||
| 57 | std::swap(data, buffer); | ||
| 58 | } | ||
| 59 | |||
| 60 |
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474 | for (int &elem : data) { |
| 61 | 314 | elem = static_cast<int>(static_cast<uint32_t>(elem) ^ kSignFlip); | |
| 62 | } | ||
| 63 | 160 | } | |
| 64 | |||
| 65 | void SimpleMerge(const std::vector<int> &left, const std::vector<int> &right, std::vector<int> &result) { | ||
| 66 | 80 | std::ranges::merge(left, right, result.begin()); | |
| 67 | } | ||
| 68 | |||
| 69 | template <typename F> | ||
| 70 | 320 | void ParallelForRange(size_t begin, size_t end, int num_threads, F &&fn) { | |
| 71 |
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320 | if (begin >= end) { |
| 72 | ✗ | return; | |
| 73 | } | ||
| 74 | 320 | std::decay_t<F> func{std::forward<F>(fn)}; | |
| 75 |
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480 | num_threads = std::max(1, std::min(num_threads, static_cast<int>(end - begin))); |
| 76 | 320 | const size_t n = end - begin; | |
| 77 | 320 | const size_t chunk = (n + static_cast<size_t>(num_threads) - 1) / static_cast<size_t>(num_threads); | |
| 78 | 320 | std::vector<std::thread> threads; | |
| 79 |
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800 | for (int thread_idx = 0; thread_idx < num_threads; ++thread_idx) { |
| 80 | 480 | const size_t lo = begin + (static_cast<size_t>(thread_idx) * chunk); | |
| 81 |
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480 | if (lo >= end) { |
| 82 | break; | ||
| 83 | } | ||
| 84 |
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480 | const size_t hi = std::min(end, lo + chunk); |
| 85 |
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480 | threads.emplace_back([lo, hi, &func]() { |
| 86 |
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480 | for (size_t i = lo; i < hi; ++i) { |
| 87 | 240 | func(i); | |
| 88 | } | ||
| 89 | }); | ||
| 90 | } | ||
| 91 |
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800 | for (auto &th : threads) { |
| 92 |
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480 | th.join(); |
| 93 | } | ||
| 94 | 320 | } | |
| 95 | |||
| 96 | /// Локальная параллельная radix + merge (как STL): потоки внутри MPI-процесса. | ||
| 97 |
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90 | void SortLocalStlParallel(std::vector<int> &data) { |
| 98 |
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90 | if (data.size() <= 1) { |
| 99 | 10 | return; | |
| 100 | } | ||
| 101 | |||
| 102 |
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80 | const int num_threads = ppc::util::GetNumThreads(); |
| 103 | const size_t per_thread_floor = data.size() >= kLargeArrayThreshold | ||
| 104 |
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80 | ? (data.size() / static_cast<size_t>(std::max(1, num_threads))) |
| 105 | 80 | : (data.size() / static_cast<size_t>(std::max(1, 2 * num_threads))); | |
| 106 | 80 | size_t min_chunk_base = kMinElementsPerPart; | |
| 107 |
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80 | if (data.size() < kMinElementsPerPartSmall) { |
| 108 | 80 | min_chunk_base = std::max(size_t{1}, data.size() / static_cast<size_t>(std::max(1, 2 * num_threads))); | |
| 109 | ✗ | } else if (data.size() < kSmallArrayThreshold) { | |
| 110 | ✗ | min_chunk_base = kMinElementsPerPartSmall; | |
| 111 | } | ||
| 112 | const size_t min_chunk = std::max(min_chunk_base, per_thread_floor); | ||
| 113 | 80 | const int max_parts_by_grain = std::max(1, static_cast<int>(data.size() / min_chunk)); | |
| 114 | 80 | const int num_parts = std::min({num_threads, static_cast<int>(data.size()), max_parts_by_grain}); | |
| 115 | |||
| 116 |
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80 | if (num_parts <= 1) { |
| 117 | ✗ | std::vector<int> buffer(data.size()); | |
| 118 | ✗ | RadixSortLSD(data, buffer); | |
| 119 | return; | ||
| 120 | } | ||
| 121 | |||
| 122 | 80 | std::vector<std::vector<int>> parts(static_cast<size_t>(num_parts)); | |
| 123 | 80 | const size_t base_size = data.size() / static_cast<size_t>(num_parts); | |
| 124 | 80 | const size_t remainder = data.size() % static_cast<size_t>(num_parts); | |
| 125 | size_t pos = 0; | ||
| 126 | |||
| 127 |
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240 | for (int i = 0; i < num_parts; ++i) { |
| 128 | 160 | const size_t part_size = base_size + (std::cmp_less(i, remainder) ? 1U : 0U); | |
| 129 |
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160 | parts[static_cast<size_t>(i)].assign(data.begin() + static_cast<std::ptrdiff_t>(pos), |
| 130 |
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160 | data.begin() + static_cast<std::ptrdiff_t>(pos + part_size)); |
| 131 | pos += part_size; | ||
| 132 | } | ||
| 133 | |||
| 134 |
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80 | ParallelForRange(0, static_cast<size_t>(num_parts), num_threads, [&](size_t i) { |
| 135 | 160 | auto &part = parts[i]; | |
| 136 | 160 | std::vector<int> buffer(part.size()); | |
| 137 | 160 | RadixSortLSD(part, buffer); | |
| 138 | 160 | }); | |
| 139 | |||
| 140 | std::vector<std::vector<int>> current = std::move(parts); | ||
| 141 |
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160 | while (current.size() > 1) { |
| 142 | 80 | const size_t half = (current.size() + 1) / 2; | |
| 143 |
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80 | std::vector<std::vector<int>> next(half); |
| 144 | |||
| 145 | 80 | const size_t pair_count = current.size() / 2; | |
| 146 |
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80 | ParallelForRange(0, pair_count, num_threads, [&](size_t idx) { |
| 147 | 80 | auto &left = current[2 * idx]; | |
| 148 | auto &right = current[(2 * idx) + 1]; | ||
| 149 | 80 | next[idx].resize(left.size() + right.size()); | |
| 150 | 80 | SimpleMerge(left, right, next[idx]); | |
| 151 | std::vector<int>().swap(left); | ||
| 152 | std::vector<int>().swap(right); | ||
| 153 | 80 | }); | |
| 154 |
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80 | if (current.size() % 2 == 1) { |
| 155 | ✗ | next[half - 1] = std::move(current.back()); | |
| 156 | } | ||
| 157 | 80 | current = std::move(next); | |
| 158 | 80 | } | |
| 159 | |||
| 160 | data = std::move(current[0]); | ||
| 161 | 80 | } | |
| 162 | |||
| 163 | 90 | void ComputeChunkParams(size_t total_size, int mpi_size, std::vector<size_t> &chunk_sizes, | |
| 164 | std::vector<size_t> &offsets) { | ||
| 165 | 90 | const size_t base_chunk = total_size / static_cast<size_t>(mpi_size); | |
| 166 | 90 | const size_t remainder = total_size % static_cast<size_t>(mpi_size); | |
| 167 | |||
| 168 |
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270 | for (int i = 0; i < mpi_size; ++i) { |
| 169 |
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180 | chunk_sizes[static_cast<size_t>(i)] = base_chunk + (std::cmp_less(i, remainder) ? 1U : 0U); |
| 170 | 180 | offsets[static_cast<size_t>(i)] = | |
| 171 |
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180 | (i == 0) ? 0 : offsets[static_cast<size_t>(i - 1)] + chunk_sizes[static_cast<size_t>(i - 1)]; |
| 172 | } | ||
| 173 | 90 | } | |
| 174 | |||
| 175 | 90 | void ScatterData(const std::vector<int> &array, std::vector<int> &local_data, const std::vector<size_t> &chunk_sizes, | |
| 176 | const std::vector<size_t> &offsets) { | ||
| 177 | 90 | const int mpi_size = static_cast<int>(chunk_sizes.size()); | |
| 178 | 90 | std::vector<int> send_counts(mpi_size); | |
| 179 |
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90 | std::vector<int> send_displs(mpi_size); |
| 180 | |||
| 181 |
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270 | for (int i = 0; i < mpi_size; ++i) { |
| 182 | 180 | send_counts[i] = static_cast<int>(chunk_sizes[static_cast<size_t>(i)]); | |
| 183 | 180 | send_displs[i] = static_cast<int>(offsets[static_cast<size_t>(i)]); | |
| 184 | } | ||
| 185 | |||
| 186 |
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90 | MPI_Scatterv(array.data(), send_counts.data(), send_displs.data(), MPI_INT, local_data.data(), |
| 187 | static_cast<int>(local_data.size()), MPI_INT, 0, MPI_COMM_WORLD); | ||
| 188 | 90 | } | |
| 189 | |||
| 190 | 90 | std::vector<int> MergeTwoSorted(const std::vector<int> &left, const std::vector<int> &right) { | |
| 191 | 90 | std::vector<int> result(left.size() + right.size()); | |
| 192 | size_t i = 0; | ||
| 193 | size_t j = 0; | ||
| 194 | size_t k = 0; | ||
| 195 | |||
| 196 |
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488 | while (i < left.size() && j < right.size()) { |
| 197 |
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398 | if (left[i] <= right[j]) { |
| 198 | 216 | result[k++] = left[i++]; | |
| 199 | } else { | ||
| 200 | 182 | result[k++] = right[j++]; | |
| 201 | } | ||
| 202 | } | ||
| 203 | |||
| 204 |
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198 | while (i < left.size()) { |
| 205 | 108 | result[k++] = left[i++]; | |
| 206 | } | ||
| 207 | |||
| 208 |
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232 | while (j < right.size()) { |
| 209 | 142 | result[k++] = right[j++]; | |
| 210 | } | ||
| 211 | |||
| 212 | 90 | return result; | |
| 213 | } | ||
| 214 | |||
| 215 | 90 | void ExchangeAndMerge(int partner, std::vector<int> &merged_data) { | |
| 216 | 90 | const size_t my_size = merged_data.size(); | |
| 217 | 90 | size_t partner_size = 0; | |
| 218 | |||
| 219 | 90 | MPI_Sendrecv(&my_size, 1, MPI_UNSIGNED_LONG, partner, 0, &partner_size, 1, MPI_UNSIGNED_LONG, partner, 0, | |
| 220 | MPI_COMM_WORLD, MPI_STATUS_IGNORE); | ||
| 221 | |||
| 222 | 90 | std::vector<int> partner_data(partner_size); | |
| 223 |
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90 | MPI_Sendrecv(merged_data.data(), static_cast<int>(my_size), MPI_INT, partner, 1, partner_data.data(), |
| 224 | static_cast<int>(partner_size), MPI_INT, partner, 1, MPI_COMM_WORLD, MPI_STATUS_IGNORE); | ||
| 225 | |||
| 226 |
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180 | merged_data = MergeTwoSorted(merged_data, partner_data); |
| 227 | 90 | } | |
| 228 | |||
| 229 | void ParallelHypercubeMerge(std::vector<int> &merged_data, int mpi_rank, int mpi_size) { | ||
| 230 | int step = 1; | ||
| 231 | |||
| 232 |
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180 | while (step < mpi_size) { |
| 233 | 90 | const int partner = mpi_rank ^ step; | |
| 234 | |||
| 235 |
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90 | if (partner < mpi_size) { |
| 236 |
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90 | ExchangeAndMerge(partner, merged_data); |
| 237 | } | ||
| 238 | |||
| 239 | 90 | step <<= 1; | |
| 240 | } | ||
| 241 | } | ||
| 242 | |||
| 243 | 90 | void BcastSortedVector(std::vector<int> &data, int mpi_rank) { | |
| 244 | 90 | size_t n = data.size(); | |
| 245 | 90 | MPI_Bcast(&n, 1, MPI_UNSIGNED_LONG, 0, MPI_COMM_WORLD); | |
| 246 |
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90 | if (mpi_rank != 0) { |
| 247 | 45 | data.resize(n); | |
| 248 | } | ||
| 249 |
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90 | if (n > 0U) { |
| 250 | 90 | MPI_Bcast(data.data(), static_cast<int>(n), MPI_INT, 0, MPI_COMM_WORLD); | |
| 251 | } | ||
| 252 | 90 | } | |
| 253 | |||
| 254 | } // namespace | ||
| 255 | |||
| 256 |
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96 | SosninaATestTaskALL::SosninaATestTaskALL(const InType &in) { |
| 257 | SetTypeOfTask(GetStaticTypeOfTask()); | ||
| 258 |
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96 | GetInput() = in; |
| 259 |
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96 | GetOutput() = in; |
| 260 | 96 | } | |
| 261 | |||
| 262 | 96 | bool SosninaATestTaskALL::ValidationImpl() { | |
| 263 | 96 | return !GetInput().empty(); | |
| 264 | } | ||
| 265 | |||
| 266 | 96 | bool SosninaATestTaskALL::PreProcessingImpl() { | |
| 267 | 96 | GetOutput() = GetInput(); | |
| 268 | 96 | return true; | |
| 269 | } | ||
| 270 | |||
| 271 | 96 | bool SosninaATestTaskALL::RunImpl() { | |
| 272 | 96 | int mpi_rank = 0; | |
| 273 | 96 | int mpi_size = 1; | |
| 274 | |||
| 275 | 96 | MPI_Comm_rank(MPI_COMM_WORLD, &mpi_rank); | |
| 276 | 96 | MPI_Comm_size(MPI_COMM_WORLD, &mpi_size); | |
| 277 | |||
| 278 | std::vector<int> &data = GetOutput(); | ||
| 279 | const size_t total_size = data.size(); | ||
| 280 | |||
| 281 |
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96 | if (total_size <= 1) { |
| 282 | return true; | ||
| 283 | } | ||
| 284 | |||
| 285 | 90 | std::vector<size_t> chunk_sizes(static_cast<size_t>(mpi_size)); | |
| 286 |
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90 | std::vector<size_t> offsets(static_cast<size_t>(mpi_size)); |
| 287 | 90 | ComputeChunkParams(total_size, mpi_size, chunk_sizes, offsets); | |
| 288 | |||
| 289 |
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90 | std::vector<int> local_data(chunk_sizes[static_cast<size_t>(mpi_rank)]); |
| 290 |
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90 | ScatterData(data, local_data, chunk_sizes, offsets); |
| 291 | |||
| 292 |
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90 | SortLocalStlParallel(local_data); |
| 293 | |||
| 294 |
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90 | if (mpi_size == 1) { |
| 295 | data = std::move(local_data); | ||
| 296 | return std::ranges::is_sorted(data); | ||
| 297 | } | ||
| 298 | |||
| 299 | std::vector<int> merged_data = std::move(local_data); | ||
| 300 | 90 | ParallelHypercubeMerge(merged_data, mpi_rank, mpi_size); | |
| 301 | |||
| 302 |
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90 | if (mpi_rank == 0) { |
| 303 | data = std::move(merged_data); | ||
| 304 | } else { | ||
| 305 | data.clear(); | ||
| 306 | } | ||
| 307 | |||
| 308 |
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90 | BcastSortedVector(data, mpi_rank); |
| 309 | |||
| 310 |
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90 | MPI_Barrier(MPI_COMM_WORLD); |
| 311 | return std::ranges::is_sorted(data); | ||
| 312 | } | ||
| 313 | |||
| 314 | 96 | bool SosninaATestTaskALL::PostProcessingImpl() { | |
| 315 | 96 | return !GetOutput().empty(); | |
| 316 | } | ||
| 317 | |||
| 318 | } // namespace sosnina_a_radix_simple_merge | ||
| 319 |