1 //===- Schedule.cpp - Calculate an optimized schedule ---------------------===// 2 // 3 // The LLVM Compiler Infrastructure 4 // 5 // This file is distributed under the University of Illinois Open Source 6 // License. See LICENSE.TXT for details. 7 // 8 //===----------------------------------------------------------------------===// 9 // 10 // This pass generates an entirely new schedule tree from the data dependences 11 // and iteration domains. The new schedule tree is computed in two steps: 12 // 13 // 1) The isl scheduling optimizer is run 14 // 15 // The isl scheduling optimizer creates a new schedule tree that maximizes 16 // parallelism and tileability and minimizes data-dependence distances. The 17 // algorithm used is a modified version of the ``Pluto'' algorithm: 18 // 19 // U. Bondhugula, A. Hartono, J. Ramanujam, and P. Sadayappan. 20 // A Practical Automatic Polyhedral Parallelizer and Locality Optimizer. 21 // In Proceedings of the 2008 ACM SIGPLAN Conference On Programming Language 22 // Design and Implementation, PLDI ’08, pages 101–113. ACM, 2008. 23 // 24 // 2) A set of post-scheduling transformations is applied on the schedule tree. 25 // 26 // These optimizations include: 27 // 28 // - Tiling of the innermost tilable bands 29 // - Prevectorization - The coice of a possible outer loop that is strip-mined 30 // to the innermost level to enable inner-loop 31 // vectorization. 32 // - Some optimizations for spatial locality are also planned. 33 // 34 // For a detailed description of the schedule tree itself please see section 6 35 // of: 36 // 37 // Polyhedral AST generation is more than scanning polyhedra 38 // Tobias Grosser, Sven Verdoolaege, Albert Cohen 39 // ACM Transations on Programming Languages and Systems (TOPLAS), 40 // 37(4), July 2015 41 // http://www.grosser.es/#pub-polyhedral-AST-generation 42 // 43 // This publication also contains a detailed discussion of the different options 44 // for polyhedral loop unrolling, full/partial tile separation and other uses 45 // of the schedule tree. 46 // 47 //===----------------------------------------------------------------------===// 48 49 #include "polly/ScheduleOptimizer.h" 50 #include "polly/CodeGen/CodeGeneration.h" 51 #include "polly/DependenceInfo.h" 52 #include "polly/LinkAllPasses.h" 53 #include "polly/Options.h" 54 #include "polly/ScopInfo.h" 55 #include "polly/Support/GICHelper.h" 56 #include "llvm/Analysis/TargetTransformInfo.h" 57 #include "llvm/Support/Debug.h" 58 #include "isl/aff.h" 59 #include "isl/band.h" 60 #include "isl/constraint.h" 61 #include "isl/map.h" 62 #include "isl/options.h" 63 #include "isl/printer.h" 64 #include "isl/schedule.h" 65 #include "isl/schedule_node.h" 66 #include "isl/space.h" 67 #include "isl/union_map.h" 68 #include "isl/union_set.h" 69 70 using namespace llvm; 71 using namespace polly; 72 73 #define DEBUG_TYPE "polly-opt-isl" 74 75 static cl::opt<std::string> 76 OptimizeDeps("polly-opt-optimize-only", 77 cl::desc("Only a certain kind of dependences (all/raw)"), 78 cl::Hidden, cl::init("all"), cl::ZeroOrMore, 79 cl::cat(PollyCategory)); 80 81 static cl::opt<std::string> 82 SimplifyDeps("polly-opt-simplify-deps", 83 cl::desc("Dependences should be simplified (yes/no)"), 84 cl::Hidden, cl::init("yes"), cl::ZeroOrMore, 85 cl::cat(PollyCategory)); 86 87 static cl::opt<int> MaxConstantTerm( 88 "polly-opt-max-constant-term", 89 cl::desc("The maximal constant term allowed (-1 is unlimited)"), cl::Hidden, 90 cl::init(20), cl::ZeroOrMore, cl::cat(PollyCategory)); 91 92 static cl::opt<int> MaxCoefficient( 93 "polly-opt-max-coefficient", 94 cl::desc("The maximal coefficient allowed (-1 is unlimited)"), cl::Hidden, 95 cl::init(20), cl::ZeroOrMore, cl::cat(PollyCategory)); 96 97 static cl::opt<std::string> FusionStrategy( 98 "polly-opt-fusion", cl::desc("The fusion strategy to choose (min/max)"), 99 cl::Hidden, cl::init("min"), cl::ZeroOrMore, cl::cat(PollyCategory)); 100 101 static cl::opt<std::string> 102 MaximizeBandDepth("polly-opt-maximize-bands", 103 cl::desc("Maximize the band depth (yes/no)"), cl::Hidden, 104 cl::init("yes"), cl::ZeroOrMore, cl::cat(PollyCategory)); 105 106 static cl::opt<std::string> OuterCoincidence( 107 "polly-opt-outer-coincidence", 108 cl::desc("Try to construct schedules where the outer member of each band " 109 "satisfies the coincidence constraints (yes/no)"), 110 cl::Hidden, cl::init("no"), cl::ZeroOrMore, cl::cat(PollyCategory)); 111 112 static cl::opt<int> PrevectorWidth( 113 "polly-prevect-width", 114 cl::desc( 115 "The number of loop iterations to strip-mine for pre-vectorization"), 116 cl::Hidden, cl::init(4), cl::ZeroOrMore, cl::cat(PollyCategory)); 117 118 static cl::opt<bool> FirstLevelTiling("polly-tiling", 119 cl::desc("Enable loop tiling"), 120 cl::init(true), cl::ZeroOrMore, 121 cl::cat(PollyCategory)); 122 123 static cl::opt<int> LatencyVectorFma( 124 "polly-target-latency-vector-fma", 125 cl::desc("The minimal number of cycles between issuing two " 126 "dependent consecutive vector fused multiply-add " 127 "instructions."), 128 cl::Hidden, cl::init(8), cl::ZeroOrMore, cl::cat(PollyCategory)); 129 130 static cl::opt<int> ThrougputVectorFma( 131 "polly-target-througput-vector-fma", 132 cl::desc("A throughput of the processor floating-point arithmetic units " 133 "expressed in the number of vector fused multiply-add " 134 "instructions per clock cycle."), 135 cl::Hidden, cl::init(1), cl::ZeroOrMore, cl::cat(PollyCategory)); 136 137 static cl::list<int> 138 CacheLevelAssociativity("polly-target-cache-level-associativity", 139 cl::desc("The associativity of each cache level."), 140 cl::Hidden, cl::ZeroOrMore, cl::CommaSeparated, 141 cl::cat(PollyCategory)); 142 143 static cl::list<int> CacheLevelSizes( 144 "polly-target-cache-level-sizes", 145 cl::desc("The size of each cache level specified in bytes."), cl::Hidden, 146 cl::ZeroOrMore, cl::CommaSeparated, cl::cat(PollyCategory)); 147 148 static cl::opt<int> FirstLevelDefaultTileSize( 149 "polly-default-tile-size", 150 cl::desc("The default tile size (if not enough were provided by" 151 " --polly-tile-sizes)"), 152 cl::Hidden, cl::init(32), cl::ZeroOrMore, cl::cat(PollyCategory)); 153 154 static cl::list<int> FirstLevelTileSizes( 155 "polly-tile-sizes", cl::desc("A tile size for each loop dimension, filled " 156 "with --polly-default-tile-size"), 157 cl::Hidden, cl::ZeroOrMore, cl::CommaSeparated, cl::cat(PollyCategory)); 158 159 static cl::opt<bool> 160 SecondLevelTiling("polly-2nd-level-tiling", 161 cl::desc("Enable a 2nd level loop of loop tiling"), 162 cl::init(false), cl::ZeroOrMore, cl::cat(PollyCategory)); 163 164 static cl::opt<int> SecondLevelDefaultTileSize( 165 "polly-2nd-level-default-tile-size", 166 cl::desc("The default 2nd-level tile size (if not enough were provided by" 167 " --polly-2nd-level-tile-sizes)"), 168 cl::Hidden, cl::init(16), cl::ZeroOrMore, cl::cat(PollyCategory)); 169 170 static cl::list<int> 171 SecondLevelTileSizes("polly-2nd-level-tile-sizes", 172 cl::desc("A tile size for each loop dimension, filled " 173 "with --polly-default-tile-size"), 174 cl::Hidden, cl::ZeroOrMore, cl::CommaSeparated, 175 cl::cat(PollyCategory)); 176 177 static cl::opt<bool> RegisterTiling("polly-register-tiling", 178 cl::desc("Enable register tiling"), 179 cl::init(false), cl::ZeroOrMore, 180 cl::cat(PollyCategory)); 181 182 static cl::opt<int> RegisterDefaultTileSize( 183 "polly-register-tiling-default-tile-size", 184 cl::desc("The default register tile size (if not enough were provided by" 185 " --polly-register-tile-sizes)"), 186 cl::Hidden, cl::init(2), cl::ZeroOrMore, cl::cat(PollyCategory)); 187 188 static cl::opt<int> PollyPatternMatchingNcQuotient( 189 "polly-pattern-matching-nc-quotient", 190 cl::desc("Quotient that is obtained by dividing Nc, the parameter of the" 191 "macro-kernel, by Nr, the parameter of the micro-kernel"), 192 cl::Hidden, cl::init(256), cl::ZeroOrMore, cl::cat(PollyCategory)); 193 194 static cl::list<int> 195 RegisterTileSizes("polly-register-tile-sizes", 196 cl::desc("A tile size for each loop dimension, filled " 197 "with --polly-register-tile-size"), 198 cl::Hidden, cl::ZeroOrMore, cl::CommaSeparated, 199 cl::cat(PollyCategory)); 200 201 static cl::opt<bool> 202 PMBasedOpts("polly-pattern-matching-based-opts", 203 cl::desc("Perform optimizations based on pattern matching"), 204 cl::init(false), cl::ZeroOrMore, cl::cat(PollyCategory)); 205 206 static cl::opt<bool> OptimizedScops( 207 "polly-optimized-scops", 208 cl::desc("Polly - Dump polyhedral description of Scops optimized with " 209 "the isl scheduling optimizer and the set of post-scheduling " 210 "transformations is applied on the schedule tree"), 211 cl::init(false), cl::ZeroOrMore, cl::cat(PollyCategory)); 212 213 /// Create an isl_union_set, which describes the isolate option based on 214 /// IsoalteDomain. 215 /// 216 /// @param IsolateDomain An isl_set whose last dimension is the only one that 217 /// should belong to the current band node. 218 static __isl_give isl_union_set * 219 getIsolateOptions(__isl_take isl_set *IsolateDomain) { 220 auto Dims = isl_set_dim(IsolateDomain, isl_dim_set); 221 auto *IsolateRelation = isl_map_from_domain(IsolateDomain); 222 IsolateRelation = isl_map_move_dims(IsolateRelation, isl_dim_out, 0, 223 isl_dim_in, Dims - 1, 1); 224 auto *IsolateOption = isl_map_wrap(IsolateRelation); 225 auto *Id = isl_id_alloc(isl_set_get_ctx(IsolateOption), "isolate", nullptr); 226 return isl_union_set_from_set(isl_set_set_tuple_id(IsolateOption, Id)); 227 } 228 229 /// Create an isl_union_set, which describes the atomic option for the dimension 230 /// of the current node. 231 /// 232 /// It may help to reduce the size of generated code. 233 /// 234 /// @param Ctx An isl_ctx, which is used to create the isl_union_set. 235 static __isl_give isl_union_set *getAtomicOptions(__isl_take isl_ctx *Ctx) { 236 auto *Space = isl_space_set_alloc(Ctx, 0, 1); 237 auto *AtomicOption = isl_set_universe(Space); 238 auto *Id = isl_id_alloc(Ctx, "atomic", nullptr); 239 return isl_union_set_from_set(isl_set_set_tuple_id(AtomicOption, Id)); 240 } 241 242 /// Make the last dimension of Set to take values from 0 to VectorWidth - 1. 243 /// 244 /// @param Set A set, which should be modified. 245 /// @param VectorWidth A parameter, which determines the constraint. 246 static __isl_give isl_set *addExtentConstraints(__isl_take isl_set *Set, 247 int VectorWidth) { 248 auto Dims = isl_set_dim(Set, isl_dim_set); 249 auto Space = isl_set_get_space(Set); 250 auto *LocalSpace = isl_local_space_from_space(Space); 251 auto *ExtConstr = 252 isl_constraint_alloc_inequality(isl_local_space_copy(LocalSpace)); 253 ExtConstr = isl_constraint_set_constant_si(ExtConstr, 0); 254 ExtConstr = 255 isl_constraint_set_coefficient_si(ExtConstr, isl_dim_set, Dims - 1, 1); 256 Set = isl_set_add_constraint(Set, ExtConstr); 257 ExtConstr = isl_constraint_alloc_inequality(LocalSpace); 258 ExtConstr = isl_constraint_set_constant_si(ExtConstr, VectorWidth - 1); 259 ExtConstr = 260 isl_constraint_set_coefficient_si(ExtConstr, isl_dim_set, Dims - 1, -1); 261 return isl_set_add_constraint(Set, ExtConstr); 262 } 263 264 /// Build the desired set of partial tile prefixes. 265 /// 266 /// We build a set of partial tile prefixes, which are prefixes of the vector 267 /// loop that have exactly VectorWidth iterations. 268 /// 269 /// 1. Get all prefixes of the vector loop. 270 /// 2. Extend it to a set, which has exactly VectorWidth iterations for 271 /// any prefix from the set that was built on the previous step. 272 /// 3. Subtract loop domain from it, project out the vector loop dimension and 273 /// get a set of prefixes, which don't have exactly VectorWidth iterations. 274 /// 4. Subtract it from all prefixes of the vector loop and get the desired 275 /// set. 276 /// 277 /// @param ScheduleRange A range of a map, which describes a prefix schedule 278 /// relation. 279 static __isl_give isl_set * 280 getPartialTilePrefixes(__isl_take isl_set *ScheduleRange, int VectorWidth) { 281 auto Dims = isl_set_dim(ScheduleRange, isl_dim_set); 282 auto *LoopPrefixes = isl_set_project_out(isl_set_copy(ScheduleRange), 283 isl_dim_set, Dims - 1, 1); 284 auto *ExtentPrefixes = 285 isl_set_add_dims(isl_set_copy(LoopPrefixes), isl_dim_set, 1); 286 ExtentPrefixes = addExtentConstraints(ExtentPrefixes, VectorWidth); 287 auto *BadPrefixes = isl_set_subtract(ExtentPrefixes, ScheduleRange); 288 BadPrefixes = isl_set_project_out(BadPrefixes, isl_dim_set, Dims - 1, 1); 289 return isl_set_subtract(LoopPrefixes, BadPrefixes); 290 } 291 292 __isl_give isl_schedule_node *ScheduleTreeOptimizer::isolateFullPartialTiles( 293 __isl_take isl_schedule_node *Node, int VectorWidth) { 294 assert(isl_schedule_node_get_type(Node) == isl_schedule_node_band); 295 Node = isl_schedule_node_child(Node, 0); 296 Node = isl_schedule_node_child(Node, 0); 297 auto *SchedRelUMap = isl_schedule_node_get_prefix_schedule_relation(Node); 298 auto *ScheduleRelation = isl_map_from_union_map(SchedRelUMap); 299 auto *ScheduleRange = isl_map_range(ScheduleRelation); 300 auto *IsolateDomain = getPartialTilePrefixes(ScheduleRange, VectorWidth); 301 auto *AtomicOption = getAtomicOptions(isl_set_get_ctx(IsolateDomain)); 302 auto *IsolateOption = getIsolateOptions(IsolateDomain); 303 Node = isl_schedule_node_parent(Node); 304 Node = isl_schedule_node_parent(Node); 305 auto *Options = isl_union_set_union(IsolateOption, AtomicOption); 306 Node = isl_schedule_node_band_set_ast_build_options(Node, Options); 307 return Node; 308 } 309 310 __isl_give isl_schedule_node * 311 ScheduleTreeOptimizer::prevectSchedBand(__isl_take isl_schedule_node *Node, 312 unsigned DimToVectorize, 313 int VectorWidth) { 314 assert(isl_schedule_node_get_type(Node) == isl_schedule_node_band); 315 316 auto Space = isl_schedule_node_band_get_space(Node); 317 auto ScheduleDimensions = isl_space_dim(Space, isl_dim_set); 318 isl_space_free(Space); 319 assert(DimToVectorize < ScheduleDimensions); 320 321 if (DimToVectorize > 0) { 322 Node = isl_schedule_node_band_split(Node, DimToVectorize); 323 Node = isl_schedule_node_child(Node, 0); 324 } 325 if (DimToVectorize < ScheduleDimensions - 1) 326 Node = isl_schedule_node_band_split(Node, 1); 327 Space = isl_schedule_node_band_get_space(Node); 328 auto Sizes = isl_multi_val_zero(Space); 329 auto Ctx = isl_schedule_node_get_ctx(Node); 330 Sizes = 331 isl_multi_val_set_val(Sizes, 0, isl_val_int_from_si(Ctx, VectorWidth)); 332 Node = isl_schedule_node_band_tile(Node, Sizes); 333 Node = isolateFullPartialTiles(Node, VectorWidth); 334 Node = isl_schedule_node_child(Node, 0); 335 // Make sure the "trivially vectorizable loop" is not unrolled. Otherwise, 336 // we will have troubles to match it in the backend. 337 Node = isl_schedule_node_band_set_ast_build_options( 338 Node, isl_union_set_read_from_str(Ctx, "{ unroll[x]: 1 = 0 }")); 339 Node = isl_schedule_node_band_sink(Node); 340 Node = isl_schedule_node_child(Node, 0); 341 if (isl_schedule_node_get_type(Node) == isl_schedule_node_leaf) 342 Node = isl_schedule_node_parent(Node); 343 isl_id *LoopMarker = isl_id_alloc(Ctx, "SIMD", nullptr); 344 Node = isl_schedule_node_insert_mark(Node, LoopMarker); 345 return Node; 346 } 347 348 __isl_give isl_schedule_node * 349 ScheduleTreeOptimizer::tileNode(__isl_take isl_schedule_node *Node, 350 const char *Identifier, ArrayRef<int> TileSizes, 351 int DefaultTileSize) { 352 auto Ctx = isl_schedule_node_get_ctx(Node); 353 auto Space = isl_schedule_node_band_get_space(Node); 354 auto Dims = isl_space_dim(Space, isl_dim_set); 355 auto Sizes = isl_multi_val_zero(Space); 356 std::string IdentifierString(Identifier); 357 for (unsigned i = 0; i < Dims; i++) { 358 auto tileSize = i < TileSizes.size() ? TileSizes[i] : DefaultTileSize; 359 Sizes = isl_multi_val_set_val(Sizes, i, isl_val_int_from_si(Ctx, tileSize)); 360 } 361 auto TileLoopMarkerStr = IdentifierString + " - Tiles"; 362 isl_id *TileLoopMarker = 363 isl_id_alloc(Ctx, TileLoopMarkerStr.c_str(), nullptr); 364 Node = isl_schedule_node_insert_mark(Node, TileLoopMarker); 365 Node = isl_schedule_node_child(Node, 0); 366 Node = isl_schedule_node_band_tile(Node, Sizes); 367 Node = isl_schedule_node_child(Node, 0); 368 auto PointLoopMarkerStr = IdentifierString + " - Points"; 369 isl_id *PointLoopMarker = 370 isl_id_alloc(Ctx, PointLoopMarkerStr.c_str(), nullptr); 371 Node = isl_schedule_node_insert_mark(Node, PointLoopMarker); 372 Node = isl_schedule_node_child(Node, 0); 373 return Node; 374 } 375 376 __isl_give isl_schedule_node * 377 ScheduleTreeOptimizer::applyRegisterTiling(__isl_take isl_schedule_node *Node, 378 llvm::ArrayRef<int> TileSizes, 379 int DefaultTileSize) { 380 auto *Ctx = isl_schedule_node_get_ctx(Node); 381 Node = tileNode(Node, "Register tiling", TileSizes, DefaultTileSize); 382 Node = isl_schedule_node_band_set_ast_build_options( 383 Node, isl_union_set_read_from_str(Ctx, "{unroll[x]}")); 384 return Node; 385 } 386 387 bool ScheduleTreeOptimizer::isTileableBandNode( 388 __isl_keep isl_schedule_node *Node) { 389 if (isl_schedule_node_get_type(Node) != isl_schedule_node_band) 390 return false; 391 392 if (isl_schedule_node_n_children(Node) != 1) 393 return false; 394 395 if (!isl_schedule_node_band_get_permutable(Node)) 396 return false; 397 398 auto Space = isl_schedule_node_band_get_space(Node); 399 auto Dims = isl_space_dim(Space, isl_dim_set); 400 isl_space_free(Space); 401 402 if (Dims <= 1) 403 return false; 404 405 auto Child = isl_schedule_node_get_child(Node, 0); 406 auto Type = isl_schedule_node_get_type(Child); 407 isl_schedule_node_free(Child); 408 409 if (Type != isl_schedule_node_leaf) 410 return false; 411 412 return true; 413 } 414 415 __isl_give isl_schedule_node * 416 ScheduleTreeOptimizer::standardBandOpts(__isl_take isl_schedule_node *Node, 417 void *User) { 418 if (FirstLevelTiling) 419 Node = tileNode(Node, "1st level tiling", FirstLevelTileSizes, 420 FirstLevelDefaultTileSize); 421 422 if (SecondLevelTiling) 423 Node = tileNode(Node, "2nd level tiling", SecondLevelTileSizes, 424 SecondLevelDefaultTileSize); 425 426 if (RegisterTiling) 427 Node = 428 applyRegisterTiling(Node, RegisterTileSizes, RegisterDefaultTileSize); 429 430 if (PollyVectorizerChoice == VECTORIZER_NONE) 431 return Node; 432 433 auto Space = isl_schedule_node_band_get_space(Node); 434 auto Dims = isl_space_dim(Space, isl_dim_set); 435 isl_space_free(Space); 436 437 for (int i = Dims - 1; i >= 0; i--) 438 if (isl_schedule_node_band_member_get_coincident(Node, i)) { 439 Node = prevectSchedBand(Node, i, PrevectorWidth); 440 break; 441 } 442 443 return Node; 444 } 445 446 /// Check whether output dimensions of the map rely on the specified input 447 /// dimension. 448 /// 449 /// @param IslMap The isl map to be considered. 450 /// @param DimNum The number of an input dimension to be checked. 451 static bool isInputDimUsed(__isl_take isl_map *IslMap, unsigned DimNum) { 452 auto *CheckedAccessRelation = 453 isl_map_project_out(isl_map_copy(IslMap), isl_dim_in, DimNum, 1); 454 CheckedAccessRelation = 455 isl_map_insert_dims(CheckedAccessRelation, isl_dim_in, DimNum, 1); 456 auto *InputDimsId = isl_map_get_tuple_id(IslMap, isl_dim_in); 457 CheckedAccessRelation = 458 isl_map_set_tuple_id(CheckedAccessRelation, isl_dim_in, InputDimsId); 459 InputDimsId = isl_map_get_tuple_id(IslMap, isl_dim_out); 460 CheckedAccessRelation = 461 isl_map_set_tuple_id(CheckedAccessRelation, isl_dim_out, InputDimsId); 462 auto res = !isl_map_is_equal(CheckedAccessRelation, IslMap); 463 isl_map_free(CheckedAccessRelation); 464 isl_map_free(IslMap); 465 return res; 466 } 467 468 /// Check if the SCoP statement could probably be optimized with analytical 469 /// modeling. 470 /// 471 /// containsMatrMult tries to determine whether the following conditions 472 /// are true: 473 /// 1. all memory accesses of the statement will have stride 0 or 1, 474 /// if we interchange loops (switch the variable used in the inner 475 /// loop to the outer loop). 476 /// 2. all memory accesses of the statement except from the last one, are 477 /// read memory access and the last one is write memory access. 478 /// 3. all subscripts of the last memory access of the statement don't contain 479 /// the variable used in the inner loop. 480 /// 481 /// @param PartialSchedule The PartialSchedule that contains a SCoP statement 482 /// to check. 483 static bool containsMatrMult(__isl_keep isl_map *PartialSchedule) { 484 auto InputDimsId = isl_map_get_tuple_id(PartialSchedule, isl_dim_in); 485 auto *ScpStmt = static_cast<ScopStmt *>(isl_id_get_user(InputDimsId)); 486 isl_id_free(InputDimsId); 487 if (ScpStmt->size() <= 1) 488 return false; 489 auto MemA = ScpStmt->begin(); 490 for (unsigned i = 0; i < ScpStmt->size() - 2 && MemA != ScpStmt->end(); 491 i++, MemA++) 492 if (!(*MemA)->isRead() || 493 ((*MemA)->isArrayKind() && 494 !((*MemA)->isStrideOne(isl_map_copy(PartialSchedule)) || 495 (*MemA)->isStrideZero(isl_map_copy(PartialSchedule))))) 496 return false; 497 MemA++; 498 if (!(*MemA)->isWrite() || !(*MemA)->isArrayKind() || 499 !((*MemA)->isStrideOne(isl_map_copy(PartialSchedule)) || 500 (*MemA)->isStrideZero(isl_map_copy(PartialSchedule)))) 501 return false; 502 auto DimNum = isl_map_dim(PartialSchedule, isl_dim_in); 503 return !isInputDimUsed((*MemA)->getAccessRelation(), DimNum - 1); 504 } 505 506 /// Circular shift of output dimensions of the integer map. 507 /// 508 /// @param IslMap The isl map to be modified. 509 static __isl_give isl_map *circularShiftOutputDims(__isl_take isl_map *IslMap) { 510 auto DimNum = isl_map_dim(IslMap, isl_dim_out); 511 if (DimNum == 0) 512 return IslMap; 513 auto InputDimsId = isl_map_get_tuple_id(IslMap, isl_dim_in); 514 IslMap = isl_map_move_dims(IslMap, isl_dim_in, 0, isl_dim_out, DimNum - 1, 1); 515 IslMap = isl_map_move_dims(IslMap, isl_dim_out, 0, isl_dim_in, 0, 1); 516 return isl_map_set_tuple_id(IslMap, isl_dim_in, InputDimsId); 517 } 518 519 /// Permute two dimensions of the band node. 520 /// 521 /// Permute FirstDim and SecondDim dimensions of the Node. 522 /// 523 /// @param Node The band node to be modified. 524 /// @param FirstDim The first dimension to be permuted. 525 /// @param SecondDim The second dimension to be permuted. 526 static __isl_give isl_schedule_node * 527 permuteBandNodeDimensions(__isl_take isl_schedule_node *Node, unsigned FirstDim, 528 unsigned SecondDim) { 529 assert(isl_schedule_node_get_type(Node) == isl_schedule_node_band && 530 isl_schedule_node_band_n_member(Node) > std::max(FirstDim, SecondDim)); 531 auto PartialSchedule = isl_schedule_node_band_get_partial_schedule(Node); 532 auto PartialScheduleFirstDim = 533 isl_multi_union_pw_aff_get_union_pw_aff(PartialSchedule, FirstDim); 534 auto PartialScheduleSecondDim = 535 isl_multi_union_pw_aff_get_union_pw_aff(PartialSchedule, SecondDim); 536 PartialSchedule = isl_multi_union_pw_aff_set_union_pw_aff( 537 PartialSchedule, SecondDim, PartialScheduleFirstDim); 538 PartialSchedule = isl_multi_union_pw_aff_set_union_pw_aff( 539 PartialSchedule, FirstDim, PartialScheduleSecondDim); 540 Node = isl_schedule_node_delete(Node); 541 Node = isl_schedule_node_insert_partial_schedule(Node, PartialSchedule); 542 return Node; 543 } 544 545 __isl_give isl_schedule_node *ScheduleTreeOptimizer::createMicroKernel( 546 __isl_take isl_schedule_node *Node, MicroKernelParamsTy MicroKernelParams) { 547 applyRegisterTiling(Node, {MicroKernelParams.Mr, MicroKernelParams.Nr}, 1); 548 Node = isl_schedule_node_parent(isl_schedule_node_parent(Node)); 549 Node = permuteBandNodeDimensions(Node, 0, 1); 550 return isl_schedule_node_child(isl_schedule_node_child(Node, 0), 0); 551 } 552 553 __isl_give isl_schedule_node *ScheduleTreeOptimizer::createMacroKernel( 554 __isl_take isl_schedule_node *Node, MacroKernelParamsTy MacroKernelParams) { 555 assert(isl_schedule_node_get_type(Node) == isl_schedule_node_band); 556 if (MacroKernelParams.Mc == 1 && MacroKernelParams.Nc == 1 && 557 MacroKernelParams.Kc == 1) 558 return Node; 559 Node = tileNode( 560 Node, "1st level tiling", 561 {MacroKernelParams.Mc, MacroKernelParams.Nc, MacroKernelParams.Kc}, 1); 562 Node = isl_schedule_node_parent(isl_schedule_node_parent(Node)); 563 Node = permuteBandNodeDimensions(Node, 1, 2); 564 Node = permuteBandNodeDimensions(Node, 0, 2); 565 return isl_schedule_node_child(isl_schedule_node_child(Node, 0), 0); 566 } 567 568 /// Get parameters of the BLIS micro kernel. 569 /// 570 /// We choose the Mr and Nr parameters of the micro kernel to be large enough 571 /// such that no stalls caused by the combination of latencies and dependencies 572 /// are introduced during the updates of the resulting matrix of the matrix 573 /// multiplication. However, they should also be as small as possible to 574 /// release more registers for entries of multiplied matrices. 575 /// 576 /// @param TTI Target Transform Info. 577 /// @return The structure of type MicroKernelParamsTy. 578 /// @see MicroKernelParamsTy 579 static struct MicroKernelParamsTy 580 getMicroKernelParams(const llvm::TargetTransformInfo *TTI) { 581 assert(TTI && "The target transform info should be provided."); 582 583 // Nvec - Number of double-precision floating-point numbers that can be hold 584 // by a vector register. Use 2 by default. 585 auto Nvec = TTI->getRegisterBitWidth(true) / 64; 586 if (Nvec == 0) 587 Nvec = 2; 588 int Nr = 589 ceil(sqrt(Nvec * LatencyVectorFma * ThrougputVectorFma) / Nvec) * Nvec; 590 int Mr = ceil(Nvec * LatencyVectorFma * ThrougputVectorFma / Nr); 591 return {Mr, Nr}; 592 } 593 594 /// Get parameters of the BLIS macro kernel. 595 /// 596 /// During the computation of matrix multiplication, blocks of partitioned 597 /// matrices are mapped to different layers of the memory hierarchy. 598 /// To optimize data reuse, blocks should be ideally kept in cache between 599 /// iterations. Since parameters of the macro kernel determine sizes of these 600 /// blocks, there are upper and lower bounds on these parameters. 601 /// 602 /// @param MicroKernelParams Parameters of the micro-kernel 603 /// to be taken into account. 604 /// @return The structure of type MacroKernelParamsTy. 605 /// @see MacroKernelParamsTy 606 /// @see MicroKernelParamsTy 607 static struct MacroKernelParamsTy 608 getMacroKernelParams(const MicroKernelParamsTy &MicroKernelParams) { 609 // According to www.cs.utexas.edu/users/flame/pubs/TOMS-BLIS-Analytical.pdf, 610 // it requires information about the first two levels of a cache to determine 611 // all the parameters of a macro-kernel. It also checks that an associativity 612 // degree of a cache level is greater than two. Otherwise, another algorithm 613 // for determination of the parameters should be used. 614 if (!(MicroKernelParams.Mr > 0 && MicroKernelParams.Nr > 0 && 615 CacheLevelSizes.size() >= 2 && CacheLevelAssociativity.size() >= 2 && 616 CacheLevelSizes[0] > 0 && CacheLevelSizes[1] > 0 && 617 CacheLevelAssociativity[0] > 2 && CacheLevelAssociativity[1] > 2)) 618 return {1, 1, 1}; 619 // The quotient should be greater than zero. 620 if (PollyPatternMatchingNcQuotient <= 0) 621 return {1, 1, 1}; 622 int Car = floor( 623 (CacheLevelAssociativity[0] - 1) / 624 (1 + static_cast<double>(MicroKernelParams.Nr) / MicroKernelParams.Mr)); 625 int Kc = (Car * CacheLevelSizes[0]) / 626 (MicroKernelParams.Mr * CacheLevelAssociativity[0] * 8); 627 double Cac = static_cast<double>(Kc * 8 * CacheLevelAssociativity[1]) / 628 CacheLevelSizes[1]; 629 int Mc = floor((CacheLevelAssociativity[1] - 2) / Cac); 630 int Nc = PollyPatternMatchingNcQuotient * MicroKernelParams.Nr; 631 return {Mc, Nc, Kc}; 632 } 633 634 /// Identify a memory access through the shape of its memory access relation. 635 /// 636 /// Identify the unique memory access in @p Stmt, that has an access relation 637 /// equal to @p ExpectedAccessRelation. 638 /// 639 /// @param Stmt The SCoP statement that contains the memory accesses under 640 /// consideration. 641 /// @param ExpectedAccessRelation The access relation that identifies 642 /// the memory access. 643 /// @return The memory access of @p Stmt whose memory access relation is equal 644 /// to @p ExpectedAccessRelation. nullptr in case there is no or more 645 /// than one such access. 646 MemoryAccess * 647 identifyAccessByAccessRelation(ScopStmt *Stmt, 648 __isl_take isl_map *ExpectedAccessRelation) { 649 if (isl_map_has_tuple_id(ExpectedAccessRelation, isl_dim_out)) 650 ExpectedAccessRelation = 651 isl_map_reset_tuple_id(ExpectedAccessRelation, isl_dim_out); 652 MemoryAccess *IdentifiedAccess = nullptr; 653 for (auto *Access : *Stmt) { 654 auto *AccessRelation = Access->getAccessRelation(); 655 AccessRelation = isl_map_reset_tuple_id(AccessRelation, isl_dim_out); 656 if (isl_map_is_equal(ExpectedAccessRelation, AccessRelation)) { 657 if (IdentifiedAccess) { 658 isl_map_free(AccessRelation); 659 isl_map_free(ExpectedAccessRelation); 660 return nullptr; 661 } 662 IdentifiedAccess = Access; 663 } 664 isl_map_free(AccessRelation); 665 } 666 isl_map_free(ExpectedAccessRelation); 667 return IdentifiedAccess; 668 } 669 670 /// Add constrains to @Dim dimension of @p ExtMap. 671 /// 672 /// If @ExtMap has the following form [O0, O1, O2]->[I1, I2, I3], 673 /// the following constraint will be added 674 /// Bound * OM <= IM <= Bound * (OM + 1) - 1, 675 /// where M is @p Dim and Bound is @p Bound. 676 /// 677 /// @param ExtMap The isl map to be modified. 678 /// @param Dim The output dimension to be modfied. 679 /// @param Bound The value that is used to specify the constraint. 680 /// @return The modified isl map 681 __isl_give isl_map * 682 addExtensionMapMatMulDimConstraint(__isl_take isl_map *ExtMap, unsigned Dim, 683 unsigned Bound) { 684 assert(Bound != 0); 685 auto *ExtMapSpace = isl_map_get_space(ExtMap); 686 auto *ConstrSpace = isl_local_space_from_space(ExtMapSpace); 687 auto *Constr = 688 isl_constraint_alloc_inequality(isl_local_space_copy(ConstrSpace)); 689 Constr = isl_constraint_set_coefficient_si(Constr, isl_dim_out, Dim, 1); 690 Constr = 691 isl_constraint_set_coefficient_si(Constr, isl_dim_in, Dim, Bound * (-1)); 692 ExtMap = isl_map_add_constraint(ExtMap, Constr); 693 Constr = isl_constraint_alloc_inequality(ConstrSpace); 694 Constr = isl_constraint_set_coefficient_si(Constr, isl_dim_out, Dim, -1); 695 Constr = isl_constraint_set_coefficient_si(Constr, isl_dim_in, Dim, Bound); 696 Constr = isl_constraint_set_constant_si(Constr, Bound - 1); 697 return isl_map_add_constraint(ExtMap, Constr); 698 } 699 700 /// Create an access relation that is specific for matrix multiplication 701 /// pattern. 702 /// 703 /// Create an access relation of the following form: 704 /// { [O0, O1, O2]->[I1, I2, I3] : 705 /// FirstOutputDimBound * O0 <= I1 <= FirstOutputDimBound * (O0 + 1) - 1 706 /// and SecondOutputDimBound * O1 <= I2 <= SecondOutputDimBound * (O1 + 1) - 1 707 /// and ThirdOutputDimBound * O2 <= I3 <= ThirdOutputDimBound * (O2 + 1) - 1} 708 /// where FirstOutputDimBound is @p FirstOutputDimBound, 709 /// SecondOutputDimBound is @p SecondOutputDimBound, 710 /// ThirdOutputDimBound is @p ThirdOutputDimBound 711 /// 712 /// @param Ctx The isl context. 713 /// @param FirstOutputDimBound, 714 /// SecondOutputDimBound, 715 /// ThirdOutputDimBound The parameters of the access relation. 716 /// @return The specified access relation. 717 __isl_give isl_map *getMatMulExt(isl_ctx *Ctx, unsigned FirstOutputDimBound, 718 unsigned SecondOutputDimBound, 719 unsigned ThirdOutputDimBound) { 720 auto *NewRelSpace = isl_space_alloc(Ctx, 0, 3, 3); 721 auto *extensionMap = isl_map_universe(NewRelSpace); 722 if (!FirstOutputDimBound) 723 extensionMap = isl_map_fix_si(extensionMap, isl_dim_out, 0, 0); 724 else 725 extensionMap = addExtensionMapMatMulDimConstraint(extensionMap, 0, 726 FirstOutputDimBound); 727 if (!SecondOutputDimBound) 728 extensionMap = isl_map_fix_si(extensionMap, isl_dim_out, 1, 0); 729 else 730 extensionMap = addExtensionMapMatMulDimConstraint(extensionMap, 1, 731 SecondOutputDimBound); 732 if (!ThirdOutputDimBound) 733 extensionMap = isl_map_fix_si(extensionMap, isl_dim_out, 2, 0); 734 else 735 extensionMap = addExtensionMapMatMulDimConstraint(extensionMap, 2, 736 ThirdOutputDimBound); 737 return extensionMap; 738 } 739 740 /// Create an access relation that is specific to the matrix 741 /// multiplication pattern. 742 /// 743 /// Create an access relation of the following form: 744 /// Stmt[O0, O1, O2]->[OI, OJ], 745 /// where I is @p I, J is @J 746 /// 747 /// @param Stmt The SCoP statement for which to generate the access relation. 748 /// @param I The index of the input dimension that is mapped to the first output 749 /// dimension. 750 /// @param J The index of the input dimension that is mapped to the second 751 /// output dimension. 752 /// @return The specified access relation. 753 __isl_give isl_map * 754 getMatMulPatternOriginalAccessRelation(ScopStmt *Stmt, unsigned I, unsigned J) { 755 auto *AccessRelSpace = isl_space_alloc(Stmt->getIslCtx(), 0, 3, 2); 756 auto *AccessRel = isl_map_universe(AccessRelSpace); 757 AccessRel = isl_map_equate(AccessRel, isl_dim_in, I, isl_dim_out, 0); 758 AccessRel = isl_map_equate(AccessRel, isl_dim_in, J, isl_dim_out, 1); 759 AccessRel = isl_map_set_tuple_id(AccessRel, isl_dim_in, Stmt->getDomainId()); 760 return AccessRel; 761 } 762 763 /// Identify the memory access that corresponds to the access to the second 764 /// operand of the matrix multiplication. 765 /// 766 /// Identify the memory access that corresponds to the access 767 /// to the matrix B of the matrix multiplication C = A x B. 768 /// 769 /// @param Stmt The SCoP statement that contains the memory accesses 770 /// under consideration. 771 /// @return The memory access of @p Stmt that corresponds to the access 772 /// to the second operand of the matrix multiplication. 773 MemoryAccess *identifyAccessA(ScopStmt *Stmt) { 774 auto *OriginalRel = getMatMulPatternOriginalAccessRelation(Stmt, 0, 2); 775 return identifyAccessByAccessRelation(Stmt, OriginalRel); 776 } 777 778 /// Identify the memory access that corresponds to the access to the first 779 /// operand of the matrix multiplication. 780 /// 781 /// Identify the memory access that corresponds to the access 782 /// to the matrix A of the matrix multiplication C = A x B. 783 /// 784 /// @param Stmt The SCoP statement that contains the memory accesses 785 /// under consideration. 786 /// @return The memory access of @p Stmt that corresponds to the access 787 /// to the first operand of the matrix multiplication. 788 MemoryAccess *identifyAccessB(ScopStmt *Stmt) { 789 auto *OriginalRel = getMatMulPatternOriginalAccessRelation(Stmt, 2, 1); 790 return identifyAccessByAccessRelation(Stmt, OriginalRel); 791 } 792 793 /// Create an access relation that is specific to 794 /// the matrix multiplication pattern. 795 /// 796 /// Create an access relation of the following form: 797 /// [O0, O1, O2, O3, O4, O5, O6, O7, O8] -> [OI, O5, OJ] 798 /// where I is @p FirstDim, J is @p SecondDim. 799 /// 800 /// It can be used, for example, to create relations that helps to consequently 801 /// access elements of operands of a matrix multiplication after creation of 802 /// the BLIS micro and macro kernels. 803 /// 804 /// @see ScheduleTreeOptimizer::createMicroKernel 805 /// @see ScheduleTreeOptimizer::createMacroKernel 806 /// 807 /// Subsequently, the described access relation is applied to the range of 808 /// @p MapOldIndVar, that is used to map original induction variables to 809 /// the ones, which are produced by schedule transformations. It helps to 810 /// define relations using a new space and, at the same time, keep them 811 /// in the original one. 812 /// 813 /// @param MapOldIndVar The relation, which maps original induction variables 814 /// to the ones, which are produced by schedule 815 /// transformations. 816 /// @param FirstDim, SecondDim The input dimensions that are used to define 817 /// the specified access relation. 818 /// @return The specified access relation. 819 __isl_give isl_map *getMatMulAccRel(__isl_take isl_map *MapOldIndVar, 820 unsigned FirstDim, unsigned SecondDim) { 821 auto *Ctx = isl_map_get_ctx(MapOldIndVar); 822 auto *AccessRelSpace = isl_space_alloc(Ctx, 0, 9, 3); 823 auto *AccessRel = isl_map_universe(AccessRelSpace); 824 AccessRel = isl_map_equate(AccessRel, isl_dim_in, FirstDim, isl_dim_out, 0); 825 AccessRel = isl_map_equate(AccessRel, isl_dim_in, 5, isl_dim_out, 1); 826 AccessRel = isl_map_equate(AccessRel, isl_dim_in, SecondDim, isl_dim_out, 2); 827 return isl_map_apply_range(MapOldIndVar, AccessRel); 828 } 829 830 __isl_give isl_schedule_node * 831 createExtensionNode(__isl_take isl_schedule_node *Node, 832 __isl_take isl_map *ExtensionMap) { 833 auto *Extension = isl_union_map_from_map(ExtensionMap); 834 auto *NewNode = isl_schedule_node_from_extension(Extension); 835 return isl_schedule_node_graft_before(Node, NewNode); 836 } 837 838 /// Apply the packing transformation. 839 /// 840 /// The packing transformation can be described as a data-layout 841 /// transformation that requires to introduce a new array, copy data 842 /// to the array, and change memory access locations of the compute kernel 843 /// to reference the array. 844 /// 845 /// @param Node The schedule node to be optimized. 846 /// @param MapOldIndVar The relation, which maps original induction variables 847 /// to the ones, which are produced by schedule 848 /// transformations. 849 /// @param MicroParams, MacroParams Parameters of the BLIS kernel 850 /// to be taken into account. 851 /// @return The optimized schedule node. 852 static __isl_give isl_schedule_node *optimizeDataLayoutMatrMulPattern( 853 __isl_take isl_schedule_node *Node, __isl_take isl_map *MapOldIndVar, 854 MicroKernelParamsTy MicroParams, MacroKernelParamsTy MacroParams) { 855 // Check whether memory accesses of the SCoP statement correspond to 856 // the matrix multiplication pattern and if this is true, obtain them. 857 auto InputDimsId = isl_map_get_tuple_id(MapOldIndVar, isl_dim_in); 858 auto *Stmt = static_cast<ScopStmt *>(isl_id_get_user(InputDimsId)); 859 isl_id_free(InputDimsId); 860 MemoryAccess *MemAccessA = identifyAccessA(Stmt); 861 MemoryAccess *MemAccessB = identifyAccessB(Stmt); 862 if (!MemAccessA || !MemAccessB) { 863 isl_map_free(MapOldIndVar); 864 return Node; 865 } 866 867 // Create a copy statement that corresponds to the memory access to the 868 // matrix B, the second operand of the matrix multiplication. 869 Node = isl_schedule_node_parent(isl_schedule_node_parent(Node)); 870 Node = isl_schedule_node_parent(isl_schedule_node_parent(Node)); 871 Node = isl_schedule_node_parent(Node); 872 Node = isl_schedule_node_child(isl_schedule_node_band_split(Node, 2), 0); 873 auto *AccRel = getMatMulAccRel(isl_map_copy(MapOldIndVar), 3, 7); 874 unsigned FirstDimSize = MacroParams.Nc / MicroParams.Nr; 875 unsigned SecondDimSize = MacroParams.Kc; 876 unsigned ThirdDimSize = MicroParams.Nr; 877 auto *SAI = Stmt->getParent()->createScopArrayInfo( 878 MemAccessB->getElementType(), "Packed_B", 879 {FirstDimSize, SecondDimSize, ThirdDimSize}); 880 AccRel = isl_map_set_tuple_id(AccRel, isl_dim_out, SAI->getBasePtrId()); 881 auto *OldAcc = MemAccessB->getAccessRelation(); 882 MemAccessB->setNewAccessRelation(AccRel); 883 auto *ExtMap = 884 getMatMulExt(Stmt->getIslCtx(), 0, MacroParams.Nc, MacroParams.Kc); 885 isl_map_move_dims(ExtMap, isl_dim_out, 0, isl_dim_in, 0, 1); 886 isl_map_move_dims(ExtMap, isl_dim_in, 2, isl_dim_out, 0, 1); 887 ExtMap = isl_map_project_out(ExtMap, isl_dim_in, 2, 1); 888 auto *Domain = Stmt->getDomain(); 889 890 // Restrict the domains of the copy statements to only execute when also its 891 // originating statement is executed. 892 auto *DomainId = isl_set_get_tuple_id(Domain); 893 auto *NewStmt = Stmt->getParent()->addScopStmt( 894 OldAcc, MemAccessB->getAccessRelation(), isl_set_copy(Domain)); 895 ExtMap = isl_map_set_tuple_id(ExtMap, isl_dim_out, isl_id_copy(DomainId)); 896 ExtMap = isl_map_intersect_range(ExtMap, isl_set_copy(Domain)); 897 ExtMap = isl_map_set_tuple_id(ExtMap, isl_dim_out, NewStmt->getDomainId()); 898 Node = createExtensionNode(Node, ExtMap); 899 900 // Create a copy statement that corresponds to the memory access 901 // to the matrix A, the first operand of the matrix multiplication. 902 Node = isl_schedule_node_child(Node, 0); 903 AccRel = getMatMulAccRel(MapOldIndVar, 4, 6); 904 FirstDimSize = MacroParams.Mc / MicroParams.Mr; 905 ThirdDimSize = MicroParams.Mr; 906 SAI = Stmt->getParent()->createScopArrayInfo( 907 MemAccessA->getElementType(), "Packed_A", 908 {FirstDimSize, SecondDimSize, ThirdDimSize}); 909 AccRel = isl_map_set_tuple_id(AccRel, isl_dim_out, SAI->getBasePtrId()); 910 OldAcc = MemAccessA->getAccessRelation(); 911 MemAccessA->setNewAccessRelation(AccRel); 912 ExtMap = getMatMulExt(Stmt->getIslCtx(), MacroParams.Mc, 0, MacroParams.Kc); 913 isl_map_move_dims(ExtMap, isl_dim_out, 0, isl_dim_in, 0, 1); 914 isl_map_move_dims(ExtMap, isl_dim_in, 2, isl_dim_out, 0, 1); 915 NewStmt = Stmt->getParent()->addScopStmt( 916 OldAcc, MemAccessA->getAccessRelation(), isl_set_copy(Domain)); 917 918 // Restrict the domains of the copy statements to only execute when also its 919 // originating statement is executed. 920 ExtMap = isl_map_set_tuple_id(ExtMap, isl_dim_out, DomainId); 921 ExtMap = isl_map_intersect_range(ExtMap, Domain); 922 ExtMap = isl_map_set_tuple_id(ExtMap, isl_dim_out, NewStmt->getDomainId()); 923 Node = createExtensionNode(Node, ExtMap); 924 Node = isl_schedule_node_child(isl_schedule_node_child(Node, 0), 0); 925 return isl_schedule_node_child(isl_schedule_node_child(Node, 0), 0); 926 } 927 928 /// Get a relation mapping induction variables produced by schedule 929 /// transformations to the original ones. 930 /// 931 /// @param Node The schedule node produced as the result of creation 932 /// of the BLIS kernels. 933 /// @param MicroKernelParams, MacroKernelParams Parameters of the BLIS kernel 934 /// to be taken into account. 935 /// @return The relation mapping original induction variables to the ones 936 /// produced by schedule transformation. 937 /// @see ScheduleTreeOptimizer::createMicroKernel 938 /// @see ScheduleTreeOptimizer::createMacroKernel 939 /// @see getMacroKernelParams 940 __isl_give isl_map * 941 getInductionVariablesSubstitution(__isl_take isl_schedule_node *Node, 942 MicroKernelParamsTy MicroKernelParams, 943 MacroKernelParamsTy MacroKernelParams) { 944 auto *Child = isl_schedule_node_get_child(Node, 0); 945 auto *UnMapOldIndVar = isl_schedule_node_get_prefix_schedule_union_map(Child); 946 isl_schedule_node_free(Child); 947 auto *MapOldIndVar = isl_map_from_union_map(UnMapOldIndVar); 948 if (isl_map_dim(MapOldIndVar, isl_dim_out) > 9) 949 MapOldIndVar = 950 isl_map_project_out(MapOldIndVar, isl_dim_out, 0, 951 isl_map_dim(MapOldIndVar, isl_dim_out) - 9); 952 return MapOldIndVar; 953 } 954 955 __isl_give isl_schedule_node *ScheduleTreeOptimizer::optimizeMatMulPattern( 956 __isl_take isl_schedule_node *Node, const llvm::TargetTransformInfo *TTI) { 957 assert(TTI && "The target transform info should be provided."); 958 auto MicroKernelParams = getMicroKernelParams(TTI); 959 auto MacroKernelParams = getMacroKernelParams(MicroKernelParams); 960 Node = createMacroKernel(Node, MacroKernelParams); 961 Node = createMicroKernel(Node, MicroKernelParams); 962 if (MacroKernelParams.Mc == 1 || MacroKernelParams.Nc == 1 || 963 MacroKernelParams.Kc == 1) 964 return Node; 965 auto *MapOldIndVar = getInductionVariablesSubstitution( 966 Node, MicroKernelParams, MacroKernelParams); 967 if (!MapOldIndVar) 968 return Node; 969 return optimizeDataLayoutMatrMulPattern(Node, MapOldIndVar, MicroKernelParams, 970 MacroKernelParams); 971 } 972 973 bool ScheduleTreeOptimizer::isMatrMultPattern( 974 __isl_keep isl_schedule_node *Node) { 975 auto *PartialSchedule = 976 isl_schedule_node_band_get_partial_schedule_union_map(Node); 977 if (isl_schedule_node_band_n_member(Node) != 3 || 978 isl_union_map_n_map(PartialSchedule) != 1) { 979 isl_union_map_free(PartialSchedule); 980 return false; 981 } 982 auto *NewPartialSchedule = isl_map_from_union_map(PartialSchedule); 983 NewPartialSchedule = circularShiftOutputDims(NewPartialSchedule); 984 if (containsMatrMult(NewPartialSchedule)) { 985 isl_map_free(NewPartialSchedule); 986 return true; 987 } 988 isl_map_free(NewPartialSchedule); 989 return false; 990 } 991 992 __isl_give isl_schedule_node * 993 ScheduleTreeOptimizer::optimizeBand(__isl_take isl_schedule_node *Node, 994 void *User) { 995 if (!isTileableBandNode(Node)) 996 return Node; 997 998 if (PMBasedOpts && User && isMatrMultPattern(Node)) { 999 DEBUG(dbgs() << "The matrix multiplication pattern was detected\n"); 1000 const llvm::TargetTransformInfo *TTI; 1001 TTI = static_cast<const llvm::TargetTransformInfo *>(User); 1002 Node = optimizeMatMulPattern(Node, TTI); 1003 } 1004 1005 return standardBandOpts(Node, User); 1006 } 1007 1008 __isl_give isl_schedule * 1009 ScheduleTreeOptimizer::optimizeSchedule(__isl_take isl_schedule *Schedule, 1010 const llvm::TargetTransformInfo *TTI) { 1011 isl_schedule_node *Root = isl_schedule_get_root(Schedule); 1012 Root = optimizeScheduleNode(Root, TTI); 1013 isl_schedule_free(Schedule); 1014 auto S = isl_schedule_node_get_schedule(Root); 1015 isl_schedule_node_free(Root); 1016 return S; 1017 } 1018 1019 __isl_give isl_schedule_node *ScheduleTreeOptimizer::optimizeScheduleNode( 1020 __isl_take isl_schedule_node *Node, const llvm::TargetTransformInfo *TTI) { 1021 Node = isl_schedule_node_map_descendant_bottom_up( 1022 Node, optimizeBand, const_cast<void *>(static_cast<const void *>(TTI))); 1023 return Node; 1024 } 1025 1026 bool ScheduleTreeOptimizer::isProfitableSchedule( 1027 Scop &S, __isl_keep isl_schedule *NewSchedule) { 1028 // To understand if the schedule has been optimized we check if the schedule 1029 // has changed at all. 1030 // TODO: We can improve this by tracking if any necessarily beneficial 1031 // transformations have been performed. This can e.g. be tiling, loop 1032 // interchange, or ...) We can track this either at the place where the 1033 // transformation has been performed or, in case of automatic ILP based 1034 // optimizations, by comparing (yet to be defined) performance metrics 1035 // before/after the scheduling optimizer 1036 // (e.g., #stride-one accesses) 1037 if (S.containsExtensionNode(NewSchedule)) 1038 return true; 1039 auto *NewScheduleMap = isl_schedule_get_map(NewSchedule); 1040 isl_union_map *OldSchedule = S.getSchedule(); 1041 assert(OldSchedule && "Only IslScheduleOptimizer can insert extension nodes " 1042 "that make Scop::getSchedule() return nullptr."); 1043 bool changed = !isl_union_map_is_equal(OldSchedule, NewScheduleMap); 1044 isl_union_map_free(OldSchedule); 1045 isl_union_map_free(NewScheduleMap); 1046 return changed; 1047 } 1048 1049 namespace { 1050 class IslScheduleOptimizer : public ScopPass { 1051 public: 1052 static char ID; 1053 explicit IslScheduleOptimizer() : ScopPass(ID) { LastSchedule = nullptr; } 1054 1055 ~IslScheduleOptimizer() { isl_schedule_free(LastSchedule); } 1056 1057 /// Optimize the schedule of the SCoP @p S. 1058 bool runOnScop(Scop &S) override; 1059 1060 /// Print the new schedule for the SCoP @p S. 1061 void printScop(raw_ostream &OS, Scop &S) const override; 1062 1063 /// Register all analyses and transformation required. 1064 void getAnalysisUsage(AnalysisUsage &AU) const override; 1065 1066 /// Release the internal memory. 1067 void releaseMemory() override { 1068 isl_schedule_free(LastSchedule); 1069 LastSchedule = nullptr; 1070 } 1071 1072 private: 1073 isl_schedule *LastSchedule; 1074 }; 1075 } // namespace 1076 1077 char IslScheduleOptimizer::ID = 0; 1078 1079 bool IslScheduleOptimizer::runOnScop(Scop &S) { 1080 1081 // Skip empty SCoPs but still allow code generation as it will delete the 1082 // loops present but not needed. 1083 if (S.getSize() == 0) { 1084 S.markAsOptimized(); 1085 return false; 1086 } 1087 1088 const Dependences &D = 1089 getAnalysis<DependenceInfo>().getDependences(Dependences::AL_Statement); 1090 1091 if (!D.hasValidDependences()) 1092 return false; 1093 1094 isl_schedule_free(LastSchedule); 1095 LastSchedule = nullptr; 1096 1097 // Build input data. 1098 int ValidityKinds = 1099 Dependences::TYPE_RAW | Dependences::TYPE_WAR | Dependences::TYPE_WAW; 1100 int ProximityKinds; 1101 1102 if (OptimizeDeps == "all") 1103 ProximityKinds = 1104 Dependences::TYPE_RAW | Dependences::TYPE_WAR | Dependences::TYPE_WAW; 1105 else if (OptimizeDeps == "raw") 1106 ProximityKinds = Dependences::TYPE_RAW; 1107 else { 1108 errs() << "Do not know how to optimize for '" << OptimizeDeps << "'" 1109 << " Falling back to optimizing all dependences.\n"; 1110 ProximityKinds = 1111 Dependences::TYPE_RAW | Dependences::TYPE_WAR | Dependences::TYPE_WAW; 1112 } 1113 1114 isl_union_set *Domain = S.getDomains(); 1115 1116 if (!Domain) 1117 return false; 1118 1119 isl_union_map *Validity = D.getDependences(ValidityKinds); 1120 isl_union_map *Proximity = D.getDependences(ProximityKinds); 1121 1122 // Simplify the dependences by removing the constraints introduced by the 1123 // domains. This can speed up the scheduling time significantly, as large 1124 // constant coefficients will be removed from the dependences. The 1125 // introduction of some additional dependences reduces the possible 1126 // transformations, but in most cases, such transformation do not seem to be 1127 // interesting anyway. In some cases this option may stop the scheduler to 1128 // find any schedule. 1129 if (SimplifyDeps == "yes") { 1130 Validity = isl_union_map_gist_domain(Validity, isl_union_set_copy(Domain)); 1131 Validity = isl_union_map_gist_range(Validity, isl_union_set_copy(Domain)); 1132 Proximity = 1133 isl_union_map_gist_domain(Proximity, isl_union_set_copy(Domain)); 1134 Proximity = isl_union_map_gist_range(Proximity, isl_union_set_copy(Domain)); 1135 } else if (SimplifyDeps != "no") { 1136 errs() << "warning: Option -polly-opt-simplify-deps should either be 'yes' " 1137 "or 'no'. Falling back to default: 'yes'\n"; 1138 } 1139 1140 DEBUG(dbgs() << "\n\nCompute schedule from: "); 1141 DEBUG(dbgs() << "Domain := " << stringFromIslObj(Domain) << ";\n"); 1142 DEBUG(dbgs() << "Proximity := " << stringFromIslObj(Proximity) << ";\n"); 1143 DEBUG(dbgs() << "Validity := " << stringFromIslObj(Validity) << ";\n"); 1144 1145 unsigned IslSerializeSCCs; 1146 1147 if (FusionStrategy == "max") { 1148 IslSerializeSCCs = 0; 1149 } else if (FusionStrategy == "min") { 1150 IslSerializeSCCs = 1; 1151 } else { 1152 errs() << "warning: Unknown fusion strategy. Falling back to maximal " 1153 "fusion.\n"; 1154 IslSerializeSCCs = 0; 1155 } 1156 1157 int IslMaximizeBands; 1158 1159 if (MaximizeBandDepth == "yes") { 1160 IslMaximizeBands = 1; 1161 } else if (MaximizeBandDepth == "no") { 1162 IslMaximizeBands = 0; 1163 } else { 1164 errs() << "warning: Option -polly-opt-maximize-bands should either be 'yes'" 1165 " or 'no'. Falling back to default: 'yes'\n"; 1166 IslMaximizeBands = 1; 1167 } 1168 1169 int IslOuterCoincidence; 1170 1171 if (OuterCoincidence == "yes") { 1172 IslOuterCoincidence = 1; 1173 } else if (OuterCoincidence == "no") { 1174 IslOuterCoincidence = 0; 1175 } else { 1176 errs() << "warning: Option -polly-opt-outer-coincidence should either be " 1177 "'yes' or 'no'. Falling back to default: 'no'\n"; 1178 IslOuterCoincidence = 0; 1179 } 1180 1181 isl_ctx *Ctx = S.getIslCtx(); 1182 1183 isl_options_set_schedule_outer_coincidence(Ctx, IslOuterCoincidence); 1184 isl_options_set_schedule_serialize_sccs(Ctx, IslSerializeSCCs); 1185 isl_options_set_schedule_maximize_band_depth(Ctx, IslMaximizeBands); 1186 isl_options_set_schedule_max_constant_term(Ctx, MaxConstantTerm); 1187 isl_options_set_schedule_max_coefficient(Ctx, MaxCoefficient); 1188 isl_options_set_tile_scale_tile_loops(Ctx, 0); 1189 1190 auto OnErrorStatus = isl_options_get_on_error(Ctx); 1191 isl_options_set_on_error(Ctx, ISL_ON_ERROR_CONTINUE); 1192 1193 isl_schedule_constraints *ScheduleConstraints; 1194 ScheduleConstraints = isl_schedule_constraints_on_domain(Domain); 1195 ScheduleConstraints = 1196 isl_schedule_constraints_set_proximity(ScheduleConstraints, Proximity); 1197 ScheduleConstraints = isl_schedule_constraints_set_validity( 1198 ScheduleConstraints, isl_union_map_copy(Validity)); 1199 ScheduleConstraints = 1200 isl_schedule_constraints_set_coincidence(ScheduleConstraints, Validity); 1201 isl_schedule *Schedule; 1202 Schedule = isl_schedule_constraints_compute_schedule(ScheduleConstraints); 1203 isl_options_set_on_error(Ctx, OnErrorStatus); 1204 1205 // In cases the scheduler is not able to optimize the code, we just do not 1206 // touch the schedule. 1207 if (!Schedule) 1208 return false; 1209 1210 DEBUG({ 1211 auto *P = isl_printer_to_str(Ctx); 1212 P = isl_printer_set_yaml_style(P, ISL_YAML_STYLE_BLOCK); 1213 P = isl_printer_print_schedule(P, Schedule); 1214 auto *str = isl_printer_get_str(P); 1215 dbgs() << "NewScheduleTree: \n" << str << "\n"; 1216 free(str); 1217 isl_printer_free(P); 1218 }); 1219 1220 Function &F = S.getFunction(); 1221 auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F); 1222 isl_schedule *NewSchedule = 1223 ScheduleTreeOptimizer::optimizeSchedule(Schedule, TTI); 1224 1225 if (!ScheduleTreeOptimizer::isProfitableSchedule(S, NewSchedule)) { 1226 isl_schedule_free(NewSchedule); 1227 return false; 1228 } 1229 1230 S.setScheduleTree(NewSchedule); 1231 S.markAsOptimized(); 1232 1233 if (OptimizedScops) 1234 S.dump(); 1235 1236 return false; 1237 } 1238 1239 void IslScheduleOptimizer::printScop(raw_ostream &OS, Scop &) const { 1240 isl_printer *p; 1241 char *ScheduleStr; 1242 1243 OS << "Calculated schedule:\n"; 1244 1245 if (!LastSchedule) { 1246 OS << "n/a\n"; 1247 return; 1248 } 1249 1250 p = isl_printer_to_str(isl_schedule_get_ctx(LastSchedule)); 1251 p = isl_printer_print_schedule(p, LastSchedule); 1252 ScheduleStr = isl_printer_get_str(p); 1253 isl_printer_free(p); 1254 1255 OS << ScheduleStr << "\n"; 1256 } 1257 1258 void IslScheduleOptimizer::getAnalysisUsage(AnalysisUsage &AU) const { 1259 ScopPass::getAnalysisUsage(AU); 1260 AU.addRequired<DependenceInfo>(); 1261 AU.addRequired<TargetTransformInfoWrapperPass>(); 1262 } 1263 1264 Pass *polly::createIslScheduleOptimizerPass() { 1265 return new IslScheduleOptimizer(); 1266 } 1267 1268 INITIALIZE_PASS_BEGIN(IslScheduleOptimizer, "polly-opt-isl", 1269 "Polly - Optimize schedule of SCoP", false, false); 1270 INITIALIZE_PASS_DEPENDENCY(DependenceInfo); 1271 INITIALIZE_PASS_DEPENDENCY(ScopInfoRegionPass); 1272 INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass); 1273 INITIALIZE_PASS_END(IslScheduleOptimizer, "polly-opt-isl", 1274 "Polly - Optimize schedule of SCoP", false, false) 1275