LoopVectorize.cpp revision 5d79bb8770a5a655af0dccc87b952c3ea9bad45e
1//===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===// 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 is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops 11// and generates target-independent LLVM-IR. Legalization of the IR is done 12// in the codegen. However, the vectorizer uses (will use) the codegen 13// interfaces to generate IR that is likely to result in an optimal binary. 14// 15// The loop vectorizer combines consecutive loop iterations into a single 16// 'wide' iteration. After this transformation the index is incremented 17// by the SIMD vector width, and not by one. 18// 19// This pass has three parts: 20// 1. The main loop pass that drives the different parts. 21// 2. LoopVectorizationLegality - A unit that checks for the legality 22// of the vectorization. 23// 3. InnerLoopVectorizer - A unit that performs the actual 24// widening of instructions. 25// 4. LoopVectorizationCostModel - A unit that checks for the profitability 26// of vectorization. It decides on the optimal vector width, which 27// can be one, if vectorization is not profitable. 28// 29//===----------------------------------------------------------------------===// 30// 31// The reduction-variable vectorization is based on the paper: 32// D. Nuzman and R. Henderson. Multi-platform Auto-vectorization. 33// 34// Variable uniformity checks are inspired by: 35// Karrenberg, R. and Hack, S. Whole Function Vectorization. 36// 37// Other ideas/concepts are from: 38// A. Zaks and D. Nuzman. Autovectorization in GCC-two years later. 39// 40// S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of 41// Vectorizing Compilers. 42// 43//===----------------------------------------------------------------------===// 44 45#define LV_NAME "loop-vectorize" 46#define DEBUG_TYPE LV_NAME 47 48#include "llvm/Transforms/Vectorize.h" 49#include "llvm/ADT/DenseMap.h" 50#include "llvm/ADT/MapVector.h" 51#include "llvm/ADT/SmallPtrSet.h" 52#include "llvm/ADT/SmallSet.h" 53#include "llvm/ADT/SmallVector.h" 54#include "llvm/ADT/StringExtras.h" 55#include "llvm/Analysis/AliasAnalysis.h" 56#include "llvm/Analysis/AliasSetTracker.h" 57#include "llvm/Analysis/Dominators.h" 58#include "llvm/Analysis/LoopInfo.h" 59#include "llvm/Analysis/LoopIterator.h" 60#include "llvm/Analysis/LoopPass.h" 61#include "llvm/Analysis/ScalarEvolution.h" 62#include "llvm/Analysis/ScalarEvolutionExpander.h" 63#include "llvm/Analysis/ScalarEvolutionExpressions.h" 64#include "llvm/Analysis/TargetTransformInfo.h" 65#include "llvm/Analysis/ValueTracking.h" 66#include "llvm/Analysis/Verifier.h" 67#include "llvm/IR/Constants.h" 68#include "llvm/IR/DataLayout.h" 69#include "llvm/IR/DerivedTypes.h" 70#include "llvm/IR/Function.h" 71#include "llvm/IR/IRBuilder.h" 72#include "llvm/IR/Instructions.h" 73#include "llvm/IR/IntrinsicInst.h" 74#include "llvm/IR/LLVMContext.h" 75#include "llvm/IR/Module.h" 76#include "llvm/IR/Type.h" 77#include "llvm/IR/Value.h" 78#include "llvm/Pass.h" 79#include "llvm/Support/CommandLine.h" 80#include "llvm/Support/Debug.h" 81#include "llvm/Support/raw_ostream.h" 82#include "llvm/Target/TargetLibraryInfo.h" 83#include "llvm/Transforms/Scalar.h" 84#include "llvm/Transforms/Utils/BasicBlockUtils.h" 85#include "llvm/Transforms/Utils/Local.h" 86#include <algorithm> 87#include <map> 88 89using namespace llvm; 90 91static cl::opt<unsigned> 92VectorizationFactor("force-vector-width", cl::init(0), cl::Hidden, 93 cl::desc("Sets the SIMD width. Zero is autoselect.")); 94 95static cl::opt<unsigned> 96VectorizationUnroll("force-vector-unroll", cl::init(0), cl::Hidden, 97 cl::desc("Sets the vectorization unroll count. " 98 "Zero is autoselect.")); 99 100static cl::opt<bool> 101EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden, 102 cl::desc("Enable if-conversion during vectorization.")); 103 104/// We don't vectorize loops with a known constant trip count below this number. 105static cl::opt<unsigned> 106TinyTripCountVectorThreshold("vectorizer-min-trip-count", cl::init(16), 107 cl::Hidden, 108 cl::desc("Don't vectorize loops with a constant " 109 "trip count that is smaller than this " 110 "value.")); 111 112/// We don't unroll loops with a known constant trip count below this number. 113static const unsigned TinyTripCountUnrollThreshold = 128; 114 115/// When performing a runtime memory check, do not check more than this 116/// number of pointers. Notice that the check is quadratic! 117static const unsigned RuntimeMemoryCheckThreshold = 4; 118 119namespace { 120 121// Forward declarations. 122class LoopVectorizationLegality; 123class LoopVectorizationCostModel; 124 125/// InnerLoopVectorizer vectorizes loops which contain only one basic 126/// block to a specified vectorization factor (VF). 127/// This class performs the widening of scalars into vectors, or multiple 128/// scalars. This class also implements the following features: 129/// * It inserts an epilogue loop for handling loops that don't have iteration 130/// counts that are known to be a multiple of the vectorization factor. 131/// * It handles the code generation for reduction variables. 132/// * Scalarization (implementation using scalars) of un-vectorizable 133/// instructions. 134/// InnerLoopVectorizer does not perform any vectorization-legality 135/// checks, and relies on the caller to check for the different legality 136/// aspects. The InnerLoopVectorizer relies on the 137/// LoopVectorizationLegality class to provide information about the induction 138/// and reduction variables that were found to a given vectorization factor. 139class InnerLoopVectorizer { 140public: 141 InnerLoopVectorizer(Loop *OrigLoop, ScalarEvolution *SE, LoopInfo *LI, 142 DominatorTree *DT, DataLayout *DL, 143 const TargetLibraryInfo *TLI, unsigned VecWidth, 144 unsigned UnrollFactor) 145 : OrigLoop(OrigLoop), SE(SE), LI(LI), DT(DT), DL(DL), TLI(TLI), 146 VF(VecWidth), UF(UnrollFactor), Builder(SE->getContext()), Induction(0), 147 OldInduction(0), WidenMap(UnrollFactor) {} 148 149 // Perform the actual loop widening (vectorization). 150 void vectorize(LoopVectorizationLegality *Legal) { 151 // Create a new empty loop. Unlink the old loop and connect the new one. 152 createEmptyLoop(Legal); 153 // Widen each instruction in the old loop to a new one in the new loop. 154 // Use the Legality module to find the induction and reduction variables. 155 vectorizeLoop(Legal); 156 // Register the new loop and update the analysis passes. 157 updateAnalysis(); 158 } 159 160private: 161 /// A small list of PHINodes. 162 typedef SmallVector<PHINode*, 4> PhiVector; 163 /// When we unroll loops we have multiple vector values for each scalar. 164 /// This data structure holds the unrolled and vectorized values that 165 /// originated from one scalar instruction. 166 typedef SmallVector<Value*, 2> VectorParts; 167 168 /// Add code that checks at runtime if the accessed arrays overlap. 169 /// Returns the comparator value or NULL if no check is needed. 170 Instruction *addRuntimeCheck(LoopVectorizationLegality *Legal, 171 Instruction *Loc); 172 /// Create an empty loop, based on the loop ranges of the old loop. 173 void createEmptyLoop(LoopVectorizationLegality *Legal); 174 /// Copy and widen the instructions from the old loop. 175 void vectorizeLoop(LoopVectorizationLegality *Legal); 176 177 /// A helper function that computes the predicate of the block BB, assuming 178 /// that the header block of the loop is set to True. It returns the *entry* 179 /// mask for the block BB. 180 VectorParts createBlockInMask(BasicBlock *BB); 181 /// A helper function that computes the predicate of the edge between SRC 182 /// and DST. 183 VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst); 184 185 /// A helper function to vectorize a single BB within the innermost loop. 186 void vectorizeBlockInLoop(LoopVectorizationLegality *Legal, BasicBlock *BB, 187 PhiVector *PV); 188 189 /// Insert the new loop to the loop hierarchy and pass manager 190 /// and update the analysis passes. 191 void updateAnalysis(); 192 193 /// This instruction is un-vectorizable. Implement it as a sequence 194 /// of scalars. 195 void scalarizeInstruction(Instruction *Instr); 196 197 /// Vectorize Load and Store instructions, 198 void vectorizeMemoryInstruction(Instruction *Instr, 199 LoopVectorizationLegality *Legal); 200 201 /// Create a broadcast instruction. This method generates a broadcast 202 /// instruction (shuffle) for loop invariant values and for the induction 203 /// value. If this is the induction variable then we extend it to N, N+1, ... 204 /// this is needed because each iteration in the loop corresponds to a SIMD 205 /// element. 206 Value *getBroadcastInstrs(Value *V); 207 208 /// This function adds 0, 1, 2 ... to each vector element, starting at zero. 209 /// If Negate is set then negative numbers are added e.g. (0, -1, -2, ...). 210 /// The sequence starts at StartIndex. 211 Value *getConsecutiveVector(Value* Val, unsigned StartIdx, bool Negate); 212 213 /// When we go over instructions in the basic block we rely on previous 214 /// values within the current basic block or on loop invariant values. 215 /// When we widen (vectorize) values we place them in the map. If the values 216 /// are not within the map, they have to be loop invariant, so we simply 217 /// broadcast them into a vector. 218 VectorParts &getVectorValue(Value *V); 219 220 /// Generate a shuffle sequence that will reverse the vector Vec. 221 Value *reverseVector(Value *Vec); 222 223 /// This is a helper class that holds the vectorizer state. It maps scalar 224 /// instructions to vector instructions. When the code is 'unrolled' then 225 /// then a single scalar value is mapped to multiple vector parts. The parts 226 /// are stored in the VectorPart type. 227 struct ValueMap { 228 /// C'tor. UnrollFactor controls the number of vectors ('parts') that 229 /// are mapped. 230 ValueMap(unsigned UnrollFactor) : UF(UnrollFactor) {} 231 232 /// \return True if 'Key' is saved in the Value Map. 233 bool has(Value *Key) const { return MapStorage.count(Key); } 234 235 /// Initializes a new entry in the map. Sets all of the vector parts to the 236 /// save value in 'Val'. 237 /// \return A reference to a vector with splat values. 238 VectorParts &splat(Value *Key, Value *Val) { 239 VectorParts &Entry = MapStorage[Key]; 240 Entry.assign(UF, Val); 241 return Entry; 242 } 243 244 ///\return A reference to the value that is stored at 'Key'. 245 VectorParts &get(Value *Key) { 246 VectorParts &Entry = MapStorage[Key]; 247 if (Entry.empty()) 248 Entry.resize(UF); 249 assert(Entry.size() == UF); 250 return Entry; 251 } 252 253 private: 254 /// The unroll factor. Each entry in the map stores this number of vector 255 /// elements. 256 unsigned UF; 257 258 /// Map storage. We use std::map and not DenseMap because insertions to a 259 /// dense map invalidates its iterators. 260 std::map<Value *, VectorParts> MapStorage; 261 }; 262 263 /// The original loop. 264 Loop *OrigLoop; 265 /// Scev analysis to use. 266 ScalarEvolution *SE; 267 /// Loop Info. 268 LoopInfo *LI; 269 /// Dominator Tree. 270 DominatorTree *DT; 271 /// Data Layout. 272 DataLayout *DL; 273 /// Target Library Info. 274 const TargetLibraryInfo *TLI; 275 276 /// The vectorization SIMD factor to use. Each vector will have this many 277 /// vector elements. 278 unsigned VF; 279 /// The vectorization unroll factor to use. Each scalar is vectorized to this 280 /// many different vector instructions. 281 unsigned UF; 282 283 /// The builder that we use 284 IRBuilder<> Builder; 285 286 // --- Vectorization state --- 287 288 /// The vector-loop preheader. 289 BasicBlock *LoopVectorPreHeader; 290 /// The scalar-loop preheader. 291 BasicBlock *LoopScalarPreHeader; 292 /// Middle Block between the vector and the scalar. 293 BasicBlock *LoopMiddleBlock; 294 ///The ExitBlock of the scalar loop. 295 BasicBlock *LoopExitBlock; 296 ///The vector loop body. 297 BasicBlock *LoopVectorBody; 298 ///The scalar loop body. 299 BasicBlock *LoopScalarBody; 300 /// A list of all bypass blocks. The first block is the entry of the loop. 301 SmallVector<BasicBlock *, 4> LoopBypassBlocks; 302 303 /// The new Induction variable which was added to the new block. 304 PHINode *Induction; 305 /// The induction variable of the old basic block. 306 PHINode *OldInduction; 307 /// Maps scalars to widened vectors. 308 ValueMap WidenMap; 309}; 310 311/// LoopVectorizationLegality checks if it is legal to vectorize a loop, and 312/// to what vectorization factor. 313/// This class does not look at the profitability of vectorization, only the 314/// legality. This class has two main kinds of checks: 315/// * Memory checks - The code in canVectorizeMemory checks if vectorization 316/// will change the order of memory accesses in a way that will change the 317/// correctness of the program. 318/// * Scalars checks - The code in canVectorizeInstrs and canVectorizeMemory 319/// checks for a number of different conditions, such as the availability of a 320/// single induction variable, that all types are supported and vectorize-able, 321/// etc. This code reflects the capabilities of InnerLoopVectorizer. 322/// This class is also used by InnerLoopVectorizer for identifying 323/// induction variable and the different reduction variables. 324class LoopVectorizationLegality { 325public: 326 LoopVectorizationLegality(Loop *L, ScalarEvolution *SE, DataLayout *DL, 327 DominatorTree *DT, TargetTransformInfo* TTI, 328 AliasAnalysis *AA, TargetLibraryInfo *TLI) 329 : TheLoop(L), SE(SE), DL(DL), DT(DT), TTI(TTI), AA(AA), TLI(TLI), 330 Induction(0) {} 331 332 /// This enum represents the kinds of reductions that we support. 333 enum ReductionKind { 334 RK_NoReduction, ///< Not a reduction. 335 RK_IntegerAdd, ///< Sum of integers. 336 RK_IntegerMult, ///< Product of integers. 337 RK_IntegerOr, ///< Bitwise or logical OR of numbers. 338 RK_IntegerAnd, ///< Bitwise or logical AND of numbers. 339 RK_IntegerXor, ///< Bitwise or logical XOR of numbers. 340 RK_FloatAdd, ///< Sum of floats. 341 RK_FloatMult ///< Product of floats. 342 }; 343 344 /// This enum represents the kinds of inductions that we support. 345 enum InductionKind { 346 IK_NoInduction, ///< Not an induction variable. 347 IK_IntInduction, ///< Integer induction variable. Step = 1. 348 IK_ReverseIntInduction, ///< Reverse int induction variable. Step = -1. 349 IK_PtrInduction, ///< Pointer induction var. Step = sizeof(elem). 350 IK_ReversePtrInduction ///< Reverse ptr indvar. Step = - sizeof(elem). 351 }; 352 353 /// This POD struct holds information about reduction variables. 354 struct ReductionDescriptor { 355 ReductionDescriptor() : StartValue(0), LoopExitInstr(0), 356 Kind(RK_NoReduction) {} 357 358 ReductionDescriptor(Value *Start, Instruction *Exit, ReductionKind K) 359 : StartValue(Start), LoopExitInstr(Exit), Kind(K) {} 360 361 // The starting value of the reduction. 362 // It does not have to be zero! 363 Value *StartValue; 364 // The instruction who's value is used outside the loop. 365 Instruction *LoopExitInstr; 366 // The kind of the reduction. 367 ReductionKind Kind; 368 }; 369 370 // This POD struct holds information about the memory runtime legality 371 // check that a group of pointers do not overlap. 372 struct RuntimePointerCheck { 373 RuntimePointerCheck() : Need(false) {} 374 375 /// Reset the state of the pointer runtime information. 376 void reset() { 377 Need = false; 378 Pointers.clear(); 379 Starts.clear(); 380 Ends.clear(); 381 } 382 383 /// Insert a pointer and calculate the start and end SCEVs. 384 void insert(ScalarEvolution *SE, Loop *Lp, Value *Ptr); 385 386 /// This flag indicates if we need to add the runtime check. 387 bool Need; 388 /// Holds the pointers that we need to check. 389 SmallVector<Value*, 2> Pointers; 390 /// Holds the pointer value at the beginning of the loop. 391 SmallVector<const SCEV*, 2> Starts; 392 /// Holds the pointer value at the end of the loop. 393 SmallVector<const SCEV*, 2> Ends; 394 }; 395 396 /// A POD for saving information about induction variables. 397 struct InductionInfo { 398 InductionInfo(Value *Start, InductionKind K) : StartValue(Start), IK(K) {} 399 InductionInfo() : StartValue(0), IK(IK_NoInduction) {} 400 /// Start value. 401 Value *StartValue; 402 /// Induction kind. 403 InductionKind IK; 404 }; 405 406 /// ReductionList contains the reduction descriptors for all 407 /// of the reductions that were found in the loop. 408 typedef DenseMap<PHINode*, ReductionDescriptor> ReductionList; 409 410 /// InductionList saves induction variables and maps them to the 411 /// induction descriptor. 412 typedef MapVector<PHINode*, InductionInfo> InductionList; 413 414 /// Alias(Multi)Map stores the values (GEPs or underlying objects and their 415 /// respective Store/Load instruction(s) to calculate aliasing. 416 typedef DenseMap<Value*, Instruction* > AliasMap; 417 typedef DenseMap<Value*, std::vector<Instruction*> > AliasMultiMap; 418 419 /// Returns true if it is legal to vectorize this loop. 420 /// This does not mean that it is profitable to vectorize this 421 /// loop, only that it is legal to do so. 422 bool canVectorize(); 423 424 /// Returns the Induction variable. 425 PHINode *getInduction() { return Induction; } 426 427 /// Returns the reduction variables found in the loop. 428 ReductionList *getReductionVars() { return &Reductions; } 429 430 /// Returns the induction variables found in the loop. 431 InductionList *getInductionVars() { return &Inductions; } 432 433 /// Returns True if V is an induction variable in this loop. 434 bool isInductionVariable(const Value *V); 435 436 /// Return true if the block BB needs to be predicated in order for the loop 437 /// to be vectorized. 438 bool blockNeedsPredication(BasicBlock *BB); 439 440 /// Check if this pointer is consecutive when vectorizing. This happens 441 /// when the last index of the GEP is the induction variable, or that the 442 /// pointer itself is an induction variable. 443 /// This check allows us to vectorize A[idx] into a wide load/store. 444 /// Returns: 445 /// 0 - Stride is unknown or non consecutive. 446 /// 1 - Address is consecutive. 447 /// -1 - Address is consecutive, and decreasing. 448 int isConsecutivePtr(Value *Ptr); 449 450 /// Returns true if the value V is uniform within the loop. 451 bool isUniform(Value *V); 452 453 /// Returns true if this instruction will remain scalar after vectorization. 454 bool isUniformAfterVectorization(Instruction* I) { return Uniforms.count(I); } 455 456 /// Returns the information that we collected about runtime memory check. 457 RuntimePointerCheck *getRuntimePointerCheck() { return &PtrRtCheck; } 458private: 459 /// Check if a single basic block loop is vectorizable. 460 /// At this point we know that this is a loop with a constant trip count 461 /// and we only need to check individual instructions. 462 bool canVectorizeInstrs(); 463 464 /// When we vectorize loops we may change the order in which 465 /// we read and write from memory. This method checks if it is 466 /// legal to vectorize the code, considering only memory constrains. 467 /// Returns true if the loop is vectorizable 468 bool canVectorizeMemory(); 469 470 /// Return true if we can vectorize this loop using the IF-conversion 471 /// transformation. 472 bool canVectorizeWithIfConvert(); 473 474 /// Collect the variables that need to stay uniform after vectorization. 475 void collectLoopUniforms(); 476 477 /// Return true if all of the instructions in the block can be speculatively 478 /// executed. 479 bool blockCanBePredicated(BasicBlock *BB); 480 481 /// Returns True, if 'Phi' is the kind of reduction variable for type 482 /// 'Kind'. If this is a reduction variable, it adds it to ReductionList. 483 bool AddReductionVar(PHINode *Phi, ReductionKind Kind); 484 /// Returns true if the instruction I can be a reduction variable of type 485 /// 'Kind'. 486 bool isReductionInstr(Instruction *I, ReductionKind Kind); 487 /// Returns the induction kind of Phi. This function may return NoInduction 488 /// if the PHI is not an induction variable. 489 InductionKind isInductionVariable(PHINode *Phi); 490 /// Return true if can compute the address bounds of Ptr within the loop. 491 bool hasComputableBounds(Value *Ptr); 492 /// Return true if there is the chance of write reorder. 493 bool hasPossibleGlobalWriteReorder(Value *Object, 494 Instruction *Inst, 495 AliasMultiMap &WriteObjects, 496 unsigned MaxByteWidth); 497 /// Return the AA location for a load or a store. 498 AliasAnalysis::Location getLoadStoreLocation(Instruction *Inst); 499 500 501 /// The loop that we evaluate. 502 Loop *TheLoop; 503 /// Scev analysis. 504 ScalarEvolution *SE; 505 /// DataLayout analysis. 506 DataLayout *DL; 507 /// Dominators. 508 DominatorTree *DT; 509 /// Target Info. 510 TargetTransformInfo *TTI; 511 /// Alias Analysis. 512 AliasAnalysis *AA; 513 /// Target Library Info. 514 TargetLibraryInfo *TLI; 515 516 // --- vectorization state --- // 517 518 /// Holds the integer induction variable. This is the counter of the 519 /// loop. 520 PHINode *Induction; 521 /// Holds the reduction variables. 522 ReductionList Reductions; 523 /// Holds all of the induction variables that we found in the loop. 524 /// Notice that inductions don't need to start at zero and that induction 525 /// variables can be pointers. 526 InductionList Inductions; 527 528 /// Allowed outside users. This holds the reduction 529 /// vars which can be accessed from outside the loop. 530 SmallPtrSet<Value*, 4> AllowedExit; 531 /// This set holds the variables which are known to be uniform after 532 /// vectorization. 533 SmallPtrSet<Instruction*, 4> Uniforms; 534 /// We need to check that all of the pointers in this list are disjoint 535 /// at runtime. 536 RuntimePointerCheck PtrRtCheck; 537}; 538 539/// LoopVectorizationCostModel - estimates the expected speedups due to 540/// vectorization. 541/// In many cases vectorization is not profitable. This can happen because of 542/// a number of reasons. In this class we mainly attempt to predict the 543/// expected speedup/slowdowns due to the supported instruction set. We use the 544/// TargetTransformInfo to query the different backends for the cost of 545/// different operations. 546class LoopVectorizationCostModel { 547public: 548 LoopVectorizationCostModel(Loop *L, ScalarEvolution *SE, LoopInfo *LI, 549 LoopVectorizationLegality *Legal, 550 const TargetTransformInfo &TTI, 551 DataLayout *DL, const TargetLibraryInfo *TLI) 552 : TheLoop(L), SE(SE), LI(LI), Legal(Legal), TTI(TTI), DL(DL), TLI(TLI) {} 553 554 /// Information about vectorization costs 555 struct VectorizationFactor { 556 unsigned Width; // Vector width with best cost 557 unsigned Cost; // Cost of the loop with that width 558 }; 559 /// \return The most profitable vectorization factor and the cost of that VF. 560 /// This method checks every power of two up to VF. If UserVF is not ZERO 561 /// then this vectorization factor will be selected if vectorization is 562 /// possible. 563 VectorizationFactor selectVectorizationFactor(bool OptForSize, 564 unsigned UserVF); 565 566 /// \return The size (in bits) of the widest type in the code that 567 /// needs to be vectorized. We ignore values that remain scalar such as 568 /// 64 bit loop indices. 569 unsigned getWidestType(); 570 571 /// \return The most profitable unroll factor. 572 /// If UserUF is non-zero then this method finds the best unroll-factor 573 /// based on register pressure and other parameters. 574 /// VF and LoopCost are the selected vectorization factor and the cost of the 575 /// selected VF. 576 unsigned selectUnrollFactor(bool OptForSize, unsigned UserUF, unsigned VF, 577 unsigned LoopCost); 578 579 /// \brief A struct that represents some properties of the register usage 580 /// of a loop. 581 struct RegisterUsage { 582 /// Holds the number of loop invariant values that are used in the loop. 583 unsigned LoopInvariantRegs; 584 /// Holds the maximum number of concurrent live intervals in the loop. 585 unsigned MaxLocalUsers; 586 /// Holds the number of instructions in the loop. 587 unsigned NumInstructions; 588 }; 589 590 /// \return information about the register usage of the loop. 591 RegisterUsage calculateRegisterUsage(); 592 593private: 594 /// Returns the expected execution cost. The unit of the cost does 595 /// not matter because we use the 'cost' units to compare different 596 /// vector widths. The cost that is returned is *not* normalized by 597 /// the factor width. 598 unsigned expectedCost(unsigned VF); 599 600 /// Returns the execution time cost of an instruction for a given vector 601 /// width. Vector width of one means scalar. 602 unsigned getInstructionCost(Instruction *I, unsigned VF); 603 604 /// A helper function for converting Scalar types to vector types. 605 /// If the incoming type is void, we return void. If the VF is 1, we return 606 /// the scalar type. 607 static Type* ToVectorTy(Type *Scalar, unsigned VF); 608 609 /// Returns whether the instruction is a load or store and will be a emitted 610 /// as a vector operation. 611 bool isConsecutiveLoadOrStore(Instruction *I); 612 613 /// The loop that we evaluate. 614 Loop *TheLoop; 615 /// Scev analysis. 616 ScalarEvolution *SE; 617 /// Loop Info analysis. 618 LoopInfo *LI; 619 /// Vectorization legality. 620 LoopVectorizationLegality *Legal; 621 /// Vector target information. 622 const TargetTransformInfo &TTI; 623 /// Target data layout information. 624 DataLayout *DL; 625 /// Target Library Info. 626 const TargetLibraryInfo *TLI; 627}; 628 629/// The LoopVectorize Pass. 630struct LoopVectorize : public LoopPass { 631 /// Pass identification, replacement for typeid 632 static char ID; 633 634 explicit LoopVectorize() : LoopPass(ID) { 635 initializeLoopVectorizePass(*PassRegistry::getPassRegistry()); 636 } 637 638 ScalarEvolution *SE; 639 DataLayout *DL; 640 LoopInfo *LI; 641 TargetTransformInfo *TTI; 642 DominatorTree *DT; 643 AliasAnalysis *AA; 644 TargetLibraryInfo *TLI; 645 646 virtual bool runOnLoop(Loop *L, LPPassManager &LPM) { 647 // We only vectorize innermost loops. 648 if (!L->empty()) 649 return false; 650 651 SE = &getAnalysis<ScalarEvolution>(); 652 DL = getAnalysisIfAvailable<DataLayout>(); 653 LI = &getAnalysis<LoopInfo>(); 654 TTI = &getAnalysis<TargetTransformInfo>(); 655 DT = &getAnalysis<DominatorTree>(); 656 AA = getAnalysisIfAvailable<AliasAnalysis>(); 657 TLI = getAnalysisIfAvailable<TargetLibraryInfo>(); 658 659 DEBUG(dbgs() << "LV: Checking a loop in \"" << 660 L->getHeader()->getParent()->getName() << "\"\n"); 661 662 // Check if it is legal to vectorize the loop. 663 LoopVectorizationLegality LVL(L, SE, DL, DT, TTI, AA, TLI); 664 if (!LVL.canVectorize()) { 665 DEBUG(dbgs() << "LV: Not vectorizing.\n"); 666 return false; 667 } 668 669 // Use the cost model. 670 LoopVectorizationCostModel CM(L, SE, LI, &LVL, *TTI, DL, TLI); 671 672 // Check the function attributes to find out if this function should be 673 // optimized for size. 674 Function *F = L->getHeader()->getParent(); 675 Attribute::AttrKind SzAttr = Attribute::OptimizeForSize; 676 Attribute::AttrKind FlAttr = Attribute::NoImplicitFloat; 677 unsigned FnIndex = AttributeSet::FunctionIndex; 678 bool OptForSize = F->getAttributes().hasAttribute(FnIndex, SzAttr); 679 bool NoFloat = F->getAttributes().hasAttribute(FnIndex, FlAttr); 680 681 if (NoFloat) { 682 DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat" 683 "attribute is used.\n"); 684 return false; 685 } 686 687 // Select the optimal vectorization factor. 688 LoopVectorizationCostModel::VectorizationFactor VF; 689 VF = CM.selectVectorizationFactor(OptForSize, VectorizationFactor); 690 // Select the unroll factor. 691 unsigned UF = CM.selectUnrollFactor(OptForSize, VectorizationUnroll, 692 VF.Width, VF.Cost); 693 694 if (VF.Width == 1) { 695 DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n"); 696 return false; 697 } 698 699 DEBUG(dbgs() << "LV: Found a vectorizable loop ("<< VF.Width << ") in "<< 700 F->getParent()->getModuleIdentifier()<<"\n"); 701 DEBUG(dbgs() << "LV: Unroll Factor is " << UF << "\n"); 702 703 // If we decided that it is *legal* to vectorize the loop then do it. 704 InnerLoopVectorizer LB(L, SE, LI, DT, DL, TLI, VF.Width, UF); 705 LB.vectorize(&LVL); 706 707 DEBUG(verifyFunction(*L->getHeader()->getParent())); 708 return true; 709 } 710 711 virtual void getAnalysisUsage(AnalysisUsage &AU) const { 712 LoopPass::getAnalysisUsage(AU); 713 AU.addRequiredID(LoopSimplifyID); 714 AU.addRequiredID(LCSSAID); 715 AU.addRequired<DominatorTree>(); 716 AU.addRequired<LoopInfo>(); 717 AU.addRequired<ScalarEvolution>(); 718 AU.addRequired<TargetTransformInfo>(); 719 AU.addPreserved<LoopInfo>(); 720 AU.addPreserved<DominatorTree>(); 721 } 722 723}; 724 725} // end anonymous namespace 726 727//===----------------------------------------------------------------------===// 728// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and 729// LoopVectorizationCostModel. 730//===----------------------------------------------------------------------===// 731 732void 733LoopVectorizationLegality::RuntimePointerCheck::insert(ScalarEvolution *SE, 734 Loop *Lp, Value *Ptr) { 735 const SCEV *Sc = SE->getSCEV(Ptr); 736 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Sc); 737 assert(AR && "Invalid addrec expression"); 738 const SCEV *Ex = SE->getExitCount(Lp, Lp->getLoopLatch()); 739 const SCEV *ScEnd = AR->evaluateAtIteration(Ex, *SE); 740 Pointers.push_back(Ptr); 741 Starts.push_back(AR->getStart()); 742 Ends.push_back(ScEnd); 743} 744 745Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) { 746 // Save the current insertion location. 747 Instruction *Loc = Builder.GetInsertPoint(); 748 749 // We need to place the broadcast of invariant variables outside the loop. 750 Instruction *Instr = dyn_cast<Instruction>(V); 751 bool NewInstr = (Instr && Instr->getParent() == LoopVectorBody); 752 bool Invariant = OrigLoop->isLoopInvariant(V) && !NewInstr; 753 754 // Place the code for broadcasting invariant variables in the new preheader. 755 if (Invariant) 756 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); 757 758 // Broadcast the scalar into all locations in the vector. 759 Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast"); 760 761 // Restore the builder insertion point. 762 if (Invariant) 763 Builder.SetInsertPoint(Loc); 764 765 return Shuf; 766} 767 768Value *InnerLoopVectorizer::getConsecutiveVector(Value* Val, unsigned StartIdx, 769 bool Negate) { 770 assert(Val->getType()->isVectorTy() && "Must be a vector"); 771 assert(Val->getType()->getScalarType()->isIntegerTy() && 772 "Elem must be an integer"); 773 // Create the types. 774 Type *ITy = Val->getType()->getScalarType(); 775 VectorType *Ty = cast<VectorType>(Val->getType()); 776 int VLen = Ty->getNumElements(); 777 SmallVector<Constant*, 8> Indices; 778 779 // Create a vector of consecutive numbers from zero to VF. 780 for (int i = 0; i < VLen; ++i) { 781 int Idx = Negate ? (-i): i; 782 Indices.push_back(ConstantInt::get(ITy, StartIdx + Idx)); 783 } 784 785 // Add the consecutive indices to the vector value. 786 Constant *Cv = ConstantVector::get(Indices); 787 assert(Cv->getType() == Val->getType() && "Invalid consecutive vec"); 788 return Builder.CreateAdd(Val, Cv, "induction"); 789} 790 791int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) { 792 assert(Ptr->getType()->isPointerTy() && "Unexpected non ptr"); 793 // Make sure that the pointer does not point to structs. 794 if (cast<PointerType>(Ptr->getType())->getElementType()->isAggregateType()) 795 return 0; 796 797 // If this value is a pointer induction variable we know it is consecutive. 798 PHINode *Phi = dyn_cast_or_null<PHINode>(Ptr); 799 if (Phi && Inductions.count(Phi)) { 800 InductionInfo II = Inductions[Phi]; 801 if (IK_PtrInduction == II.IK) 802 return 1; 803 else if (IK_ReversePtrInduction == II.IK) 804 return -1; 805 } 806 807 GetElementPtrInst *Gep = dyn_cast_or_null<GetElementPtrInst>(Ptr); 808 if (!Gep) 809 return 0; 810 811 unsigned NumOperands = Gep->getNumOperands(); 812 Value *LastIndex = Gep->getOperand(NumOperands - 1); 813 814 Value *GpPtr = Gep->getPointerOperand(); 815 // If this GEP value is a consecutive pointer induction variable and all of 816 // the indices are constant then we know it is consecutive. We can 817 Phi = dyn_cast<PHINode>(GpPtr); 818 if (Phi && Inductions.count(Phi)) { 819 820 // Make sure that the pointer does not point to structs. 821 PointerType *GepPtrType = cast<PointerType>(GpPtr->getType()); 822 if (GepPtrType->getElementType()->isAggregateType()) 823 return 0; 824 825 // Make sure that all of the index operands are loop invariant. 826 for (unsigned i = 1; i < NumOperands; ++i) 827 if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop)) 828 return 0; 829 830 InductionInfo II = Inductions[Phi]; 831 if (IK_PtrInduction == II.IK) 832 return 1; 833 else if (IK_ReversePtrInduction == II.IK) 834 return -1; 835 } 836 837 // Check that all of the gep indices are uniform except for the last. 838 for (unsigned i = 0; i < NumOperands - 1; ++i) 839 if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop)) 840 return 0; 841 842 // We can emit wide load/stores only if the last index is the induction 843 // variable. 844 const SCEV *Last = SE->getSCEV(LastIndex); 845 if (const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Last)) { 846 const SCEV *Step = AR->getStepRecurrence(*SE); 847 848 // The memory is consecutive because the last index is consecutive 849 // and all other indices are loop invariant. 850 if (Step->isOne()) 851 return 1; 852 if (Step->isAllOnesValue()) 853 return -1; 854 } 855 856 return 0; 857} 858 859bool LoopVectorizationLegality::isUniform(Value *V) { 860 return (SE->isLoopInvariant(SE->getSCEV(V), TheLoop)); 861} 862 863InnerLoopVectorizer::VectorParts& 864InnerLoopVectorizer::getVectorValue(Value *V) { 865 assert(V != Induction && "The new induction variable should not be used."); 866 assert(!V->getType()->isVectorTy() && "Can't widen a vector"); 867 868 // If we have this scalar in the map, return it. 869 if (WidenMap.has(V)) 870 return WidenMap.get(V); 871 872 // If this scalar is unknown, assume that it is a constant or that it is 873 // loop invariant. Broadcast V and save the value for future uses. 874 Value *B = getBroadcastInstrs(V); 875 return WidenMap.splat(V, B); 876} 877 878Value *InnerLoopVectorizer::reverseVector(Value *Vec) { 879 assert(Vec->getType()->isVectorTy() && "Invalid type"); 880 SmallVector<Constant*, 8> ShuffleMask; 881 for (unsigned i = 0; i < VF; ++i) 882 ShuffleMask.push_back(Builder.getInt32(VF - i - 1)); 883 884 return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()), 885 ConstantVector::get(ShuffleMask), 886 "reverse"); 887} 888 889 890void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr, 891 LoopVectorizationLegality *Legal) { 892 // Attempt to issue a wide load. 893 LoadInst *LI = dyn_cast<LoadInst>(Instr); 894 StoreInst *SI = dyn_cast<StoreInst>(Instr); 895 896 assert((LI || SI) && "Invalid Load/Store instruction"); 897 898 Type *ScalarDataTy = LI ? LI->getType() : SI->getValueOperand()->getType(); 899 Type *DataTy = VectorType::get(ScalarDataTy, VF); 900 Value *Ptr = LI ? LI->getPointerOperand() : SI->getPointerOperand(); 901 unsigned Alignment = LI ? LI->getAlignment() : SI->getAlignment(); 902 903 // If the pointer is loop invariant or if it is non consecutive, 904 // scalarize the load. 905 int Stride = Legal->isConsecutivePtr(Ptr); 906 bool Reverse = Stride < 0; 907 bool UniformLoad = LI && Legal->isUniform(Ptr); 908 if (Stride == 0 || UniformLoad) 909 return scalarizeInstruction(Instr); 910 911 Constant *Zero = Builder.getInt32(0); 912 VectorParts &Entry = WidenMap.get(Instr); 913 914 // Handle consecutive loads/stores. 915 GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr); 916 if (Gep && Legal->isInductionVariable(Gep->getPointerOperand())) { 917 Value *PtrOperand = Gep->getPointerOperand(); 918 Value *FirstBasePtr = getVectorValue(PtrOperand)[0]; 919 FirstBasePtr = Builder.CreateExtractElement(FirstBasePtr, Zero); 920 921 // Create the new GEP with the new induction variable. 922 GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone()); 923 Gep2->setOperand(0, FirstBasePtr); 924 Gep2->setName("gep.indvar.base"); 925 Ptr = Builder.Insert(Gep2); 926 } else if (Gep) { 927 assert(SE->isLoopInvariant(SE->getSCEV(Gep->getPointerOperand()), 928 OrigLoop) && "Base ptr must be invariant"); 929 930 // The last index does not have to be the induction. It can be 931 // consecutive and be a function of the index. For example A[I+1]; 932 unsigned NumOperands = Gep->getNumOperands(); 933 934 Value *LastGepOperand = Gep->getOperand(NumOperands - 1); 935 VectorParts &GEPParts = getVectorValue(LastGepOperand); 936 Value *LastIndex = GEPParts[0]; 937 LastIndex = Builder.CreateExtractElement(LastIndex, Zero); 938 939 // Create the new GEP with the new induction variable. 940 GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone()); 941 Gep2->setOperand(NumOperands - 1, LastIndex); 942 Gep2->setName("gep.indvar.idx"); 943 Ptr = Builder.Insert(Gep2); 944 } else { 945 // Use the induction element ptr. 946 assert(isa<PHINode>(Ptr) && "Invalid induction ptr"); 947 VectorParts &PtrVal = getVectorValue(Ptr); 948 Ptr = Builder.CreateExtractElement(PtrVal[0], Zero); 949 } 950 951 // Handle Stores: 952 if (SI) { 953 assert(!Legal->isUniform(SI->getPointerOperand()) && 954 "We do not allow storing to uniform addresses"); 955 956 VectorParts &StoredVal = getVectorValue(SI->getValueOperand()); 957 for (unsigned Part = 0; Part < UF; ++Part) { 958 // Calculate the pointer for the specific unroll-part. 959 Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF)); 960 961 if (Reverse) { 962 // If we store to reverse consecutive memory locations then we need 963 // to reverse the order of elements in the stored value. 964 StoredVal[Part] = reverseVector(StoredVal[Part]); 965 // If the address is consecutive but reversed, then the 966 // wide store needs to start at the last vector element. 967 PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF)); 968 PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF)); 969 } 970 971 Value *VecPtr = Builder.CreateBitCast(PartPtr, DataTy->getPointerTo()); 972 Builder.CreateStore(StoredVal[Part], VecPtr)->setAlignment(Alignment); 973 } 974 } 975 976 for (unsigned Part = 0; Part < UF; ++Part) { 977 // Calculate the pointer for the specific unroll-part. 978 Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF)); 979 980 if (Reverse) { 981 // If the address is consecutive but reversed, then the 982 // wide store needs to start at the last vector element. 983 PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF)); 984 PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF)); 985 } 986 987 Value *VecPtr = Builder.CreateBitCast(PartPtr, DataTy->getPointerTo()); 988 Value *LI = Builder.CreateLoad(VecPtr, "wide.load"); 989 cast<LoadInst>(LI)->setAlignment(Alignment); 990 Entry[Part] = Reverse ? reverseVector(LI) : LI; 991 } 992} 993 994void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr) { 995 assert(!Instr->getType()->isAggregateType() && "Can't handle vectors"); 996 // Holds vector parameters or scalars, in case of uniform vals. 997 SmallVector<VectorParts, 4> Params; 998 999 // Find all of the vectorized parameters. 1000 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) { 1001 Value *SrcOp = Instr->getOperand(op); 1002 1003 // If we are accessing the old induction variable, use the new one. 1004 if (SrcOp == OldInduction) { 1005 Params.push_back(getVectorValue(SrcOp)); 1006 continue; 1007 } 1008 1009 // Try using previously calculated values. 1010 Instruction *SrcInst = dyn_cast<Instruction>(SrcOp); 1011 1012 // If the src is an instruction that appeared earlier in the basic block 1013 // then it should already be vectorized. 1014 if (SrcInst && OrigLoop->contains(SrcInst)) { 1015 assert(WidenMap.has(SrcInst) && "Source operand is unavailable"); 1016 // The parameter is a vector value from earlier. 1017 Params.push_back(WidenMap.get(SrcInst)); 1018 } else { 1019 // The parameter is a scalar from outside the loop. Maybe even a constant. 1020 VectorParts Scalars; 1021 Scalars.append(UF, SrcOp); 1022 Params.push_back(Scalars); 1023 } 1024 } 1025 1026 assert(Params.size() == Instr->getNumOperands() && 1027 "Invalid number of operands"); 1028 1029 // Does this instruction return a value ? 1030 bool IsVoidRetTy = Instr->getType()->isVoidTy(); 1031 1032 Value *UndefVec = IsVoidRetTy ? 0 : 1033 UndefValue::get(VectorType::get(Instr->getType(), VF)); 1034 // Create a new entry in the WidenMap and initialize it to Undef or Null. 1035 VectorParts &VecResults = WidenMap.splat(Instr, UndefVec); 1036 1037 // For each scalar that we create: 1038 for (unsigned Width = 0; Width < VF; ++Width) { 1039 // For each vector unroll 'part': 1040 for (unsigned Part = 0; Part < UF; ++Part) { 1041 Instruction *Cloned = Instr->clone(); 1042 if (!IsVoidRetTy) 1043 Cloned->setName(Instr->getName() + ".cloned"); 1044 // Replace the operands of the cloned instrucions with extracted scalars. 1045 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) { 1046 Value *Op = Params[op][Part]; 1047 // Param is a vector. Need to extract the right lane. 1048 if (Op->getType()->isVectorTy()) 1049 Op = Builder.CreateExtractElement(Op, Builder.getInt32(Width)); 1050 Cloned->setOperand(op, Op); 1051 } 1052 1053 // Place the cloned scalar in the new loop. 1054 Builder.Insert(Cloned); 1055 1056 // If the original scalar returns a value we need to place it in a vector 1057 // so that future users will be able to use it. 1058 if (!IsVoidRetTy) 1059 VecResults[Part] = Builder.CreateInsertElement(VecResults[Part], Cloned, 1060 Builder.getInt32(Width)); 1061 } 1062 } 1063} 1064 1065Instruction * 1066InnerLoopVectorizer::addRuntimeCheck(LoopVectorizationLegality *Legal, 1067 Instruction *Loc) { 1068 LoopVectorizationLegality::RuntimePointerCheck *PtrRtCheck = 1069 Legal->getRuntimePointerCheck(); 1070 1071 if (!PtrRtCheck->Need) 1072 return NULL; 1073 1074 Instruction *MemoryRuntimeCheck = 0; 1075 unsigned NumPointers = PtrRtCheck->Pointers.size(); 1076 SmallVector<Value* , 2> Starts; 1077 SmallVector<Value* , 2> Ends; 1078 1079 SCEVExpander Exp(*SE, "induction"); 1080 1081 // Use this type for pointer arithmetic. 1082 Type* PtrArithTy = Type::getInt8PtrTy(Loc->getContext(), 0); 1083 1084 for (unsigned i = 0; i < NumPointers; ++i) { 1085 Value *Ptr = PtrRtCheck->Pointers[i]; 1086 const SCEV *Sc = SE->getSCEV(Ptr); 1087 1088 if (SE->isLoopInvariant(Sc, OrigLoop)) { 1089 DEBUG(dbgs() << "LV: Adding RT check for a loop invariant ptr:" << 1090 *Ptr <<"\n"); 1091 Starts.push_back(Ptr); 1092 Ends.push_back(Ptr); 1093 } else { 1094 DEBUG(dbgs() << "LV: Adding RT check for range:" << *Ptr <<"\n"); 1095 1096 Value *Start = Exp.expandCodeFor(PtrRtCheck->Starts[i], PtrArithTy, Loc); 1097 Value *End = Exp.expandCodeFor(PtrRtCheck->Ends[i], PtrArithTy, Loc); 1098 Starts.push_back(Start); 1099 Ends.push_back(End); 1100 } 1101 } 1102 1103 IRBuilder<> ChkBuilder(Loc); 1104 1105 for (unsigned i = 0; i < NumPointers; ++i) { 1106 for (unsigned j = i+1; j < NumPointers; ++j) { 1107 Value *Start0 = ChkBuilder.CreateBitCast(Starts[i], PtrArithTy, "bc"); 1108 Value *Start1 = ChkBuilder.CreateBitCast(Starts[j], PtrArithTy, "bc"); 1109 Value *End0 = ChkBuilder.CreateBitCast(Ends[i], PtrArithTy, "bc"); 1110 Value *End1 = ChkBuilder.CreateBitCast(Ends[j], PtrArithTy, "bc"); 1111 1112 Value *Cmp0 = ChkBuilder.CreateICmpULE(Start0, End1, "bound0"); 1113 Value *Cmp1 = ChkBuilder.CreateICmpULE(Start1, End0, "bound1"); 1114 Value *IsConflict = ChkBuilder.CreateAnd(Cmp0, Cmp1, "found.conflict"); 1115 if (MemoryRuntimeCheck) 1116 IsConflict = ChkBuilder.CreateOr(MemoryRuntimeCheck, IsConflict, 1117 "conflict.rdx"); 1118 1119 MemoryRuntimeCheck = cast<Instruction>(IsConflict); 1120 } 1121 } 1122 1123 return MemoryRuntimeCheck; 1124} 1125 1126void 1127InnerLoopVectorizer::createEmptyLoop(LoopVectorizationLegality *Legal) { 1128 /* 1129 In this function we generate a new loop. The new loop will contain 1130 the vectorized instructions while the old loop will continue to run the 1131 scalar remainder. 1132 1133 [ ] <-- vector loop bypass (may consist of multiple blocks). 1134 / | 1135 / v 1136 | [ ] <-- vector pre header. 1137 | | 1138 | v 1139 | [ ] \ 1140 | [ ]_| <-- vector loop. 1141 | | 1142 \ v 1143 >[ ] <--- middle-block. 1144 / | 1145 / v 1146 | [ ] <--- new preheader. 1147 | | 1148 | v 1149 | [ ] \ 1150 | [ ]_| <-- old scalar loop to handle remainder. 1151 \ | 1152 \ v 1153 >[ ] <-- exit block. 1154 ... 1155 */ 1156 1157 BasicBlock *OldBasicBlock = OrigLoop->getHeader(); 1158 BasicBlock *BypassBlock = OrigLoop->getLoopPreheader(); 1159 BasicBlock *ExitBlock = OrigLoop->getExitBlock(); 1160 assert(ExitBlock && "Must have an exit block"); 1161 1162 // Some loops have a single integer induction variable, while other loops 1163 // don't. One example is c++ iterators that often have multiple pointer 1164 // induction variables. In the code below we also support a case where we 1165 // don't have a single induction variable. 1166 OldInduction = Legal->getInduction(); 1167 Type *IdxTy = OldInduction ? OldInduction->getType() : 1168 DL->getIntPtrType(SE->getContext()); 1169 1170 // Find the loop boundaries. 1171 const SCEV *ExitCount = SE->getExitCount(OrigLoop, OrigLoop->getLoopLatch()); 1172 assert(ExitCount != SE->getCouldNotCompute() && "Invalid loop count"); 1173 1174 // Get the total trip count from the count by adding 1. 1175 ExitCount = SE->getAddExpr(ExitCount, 1176 SE->getConstant(ExitCount->getType(), 1)); 1177 1178 // Expand the trip count and place the new instructions in the preheader. 1179 // Notice that the pre-header does not change, only the loop body. 1180 SCEVExpander Exp(*SE, "induction"); 1181 1182 // Count holds the overall loop count (N). 1183 Value *Count = Exp.expandCodeFor(ExitCount, ExitCount->getType(), 1184 BypassBlock->getTerminator()); 1185 1186 // The loop index does not have to start at Zero. Find the original start 1187 // value from the induction PHI node. If we don't have an induction variable 1188 // then we know that it starts at zero. 1189 Value *StartIdx = OldInduction ? 1190 OldInduction->getIncomingValueForBlock(BypassBlock): 1191 ConstantInt::get(IdxTy, 0); 1192 1193 assert(BypassBlock && "Invalid loop structure"); 1194 LoopBypassBlocks.push_back(BypassBlock); 1195 1196 // Split the single block loop into the two loop structure described above. 1197 BasicBlock *VectorPH = 1198 BypassBlock->splitBasicBlock(BypassBlock->getTerminator(), "vector.ph"); 1199 BasicBlock *VecBody = 1200 VectorPH->splitBasicBlock(VectorPH->getTerminator(), "vector.body"); 1201 BasicBlock *MiddleBlock = 1202 VecBody->splitBasicBlock(VecBody->getTerminator(), "middle.block"); 1203 BasicBlock *ScalarPH = 1204 MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(), "scalar.ph"); 1205 1206 // Use this IR builder to create the loop instructions (Phi, Br, Cmp) 1207 // inside the loop. 1208 Builder.SetInsertPoint(VecBody->getFirstInsertionPt()); 1209 1210 // Generate the induction variable. 1211 Induction = Builder.CreatePHI(IdxTy, 2, "index"); 1212 // The loop step is equal to the vectorization factor (num of SIMD elements) 1213 // times the unroll factor (num of SIMD instructions). 1214 Constant *Step = ConstantInt::get(IdxTy, VF * UF); 1215 1216 // This is the IR builder that we use to add all of the logic for bypassing 1217 // the new vector loop. 1218 IRBuilder<> BypassBuilder(BypassBlock->getTerminator()); 1219 1220 // We may need to extend the index in case there is a type mismatch. 1221 // We know that the count starts at zero and does not overflow. 1222 if (Count->getType() != IdxTy) { 1223 // The exit count can be of pointer type. Convert it to the correct 1224 // integer type. 1225 if (ExitCount->getType()->isPointerTy()) 1226 Count = BypassBuilder.CreatePointerCast(Count, IdxTy, "ptrcnt.to.int"); 1227 else 1228 Count = BypassBuilder.CreateZExtOrTrunc(Count, IdxTy, "cnt.cast"); 1229 } 1230 1231 // Add the start index to the loop count to get the new end index. 1232 Value *IdxEnd = BypassBuilder.CreateAdd(Count, StartIdx, "end.idx"); 1233 1234 // Now we need to generate the expression for N - (N % VF), which is 1235 // the part that the vectorized body will execute. 1236 Value *R = BypassBuilder.CreateURem(Count, Step, "n.mod.vf"); 1237 Value *CountRoundDown = BypassBuilder.CreateSub(Count, R, "n.vec"); 1238 Value *IdxEndRoundDown = BypassBuilder.CreateAdd(CountRoundDown, StartIdx, 1239 "end.idx.rnd.down"); 1240 1241 // Now, compare the new count to zero. If it is zero skip the vector loop and 1242 // jump to the scalar loop. 1243 Value *Cmp = BypassBuilder.CreateICmpEQ(IdxEndRoundDown, StartIdx, 1244 "cmp.zero"); 1245 1246 BasicBlock *LastBypassBlock = BypassBlock; 1247 1248 // Generate the code that checks in runtime if arrays overlap. We put the 1249 // checks into a separate block to make the more common case of few elements 1250 // faster. 1251 Instruction *MemRuntimeCheck = addRuntimeCheck(Legal, 1252 BypassBlock->getTerminator()); 1253 if (MemRuntimeCheck) { 1254 // Create a new block containing the memory check. 1255 BasicBlock *CheckBlock = BypassBlock->splitBasicBlock(MemRuntimeCheck, 1256 "vector.memcheck"); 1257 LoopBypassBlocks.push_back(CheckBlock); 1258 1259 // Replace the branch into the memory check block with a conditional branch 1260 // for the "few elements case". 1261 Instruction *OldTerm = BypassBlock->getTerminator(); 1262 BranchInst::Create(MiddleBlock, CheckBlock, Cmp, OldTerm); 1263 OldTerm->eraseFromParent(); 1264 1265 Cmp = MemRuntimeCheck; 1266 LastBypassBlock = CheckBlock; 1267 } 1268 1269 LastBypassBlock->getTerminator()->eraseFromParent(); 1270 BranchInst::Create(MiddleBlock, VectorPH, Cmp, 1271 LastBypassBlock); 1272 1273 // We are going to resume the execution of the scalar loop. 1274 // Go over all of the induction variables that we found and fix the 1275 // PHIs that are left in the scalar version of the loop. 1276 // The starting values of PHI nodes depend on the counter of the last 1277 // iteration in the vectorized loop. 1278 // If we come from a bypass edge then we need to start from the original 1279 // start value. 1280 1281 // This variable saves the new starting index for the scalar loop. 1282 PHINode *ResumeIndex = 0; 1283 LoopVectorizationLegality::InductionList::iterator I, E; 1284 LoopVectorizationLegality::InductionList *List = Legal->getInductionVars(); 1285 for (I = List->begin(), E = List->end(); I != E; ++I) { 1286 PHINode *OrigPhi = I->first; 1287 LoopVectorizationLegality::InductionInfo II = I->second; 1288 PHINode *ResumeVal = PHINode::Create(OrigPhi->getType(), 2, "resume.val", 1289 MiddleBlock->getTerminator()); 1290 Value *EndValue = 0; 1291 switch (II.IK) { 1292 case LoopVectorizationLegality::IK_NoInduction: 1293 llvm_unreachable("Unknown induction"); 1294 case LoopVectorizationLegality::IK_IntInduction: { 1295 // Handle the integer induction counter: 1296 assert(OrigPhi->getType()->isIntegerTy() && "Invalid type"); 1297 assert(OrigPhi == OldInduction && "Unknown integer PHI"); 1298 // We know what the end value is. 1299 EndValue = IdxEndRoundDown; 1300 // We also know which PHI node holds it. 1301 ResumeIndex = ResumeVal; 1302 break; 1303 } 1304 case LoopVectorizationLegality::IK_ReverseIntInduction: { 1305 // Convert the CountRoundDown variable to the PHI size. 1306 unsigned CRDSize = CountRoundDown->getType()->getScalarSizeInBits(); 1307 unsigned IISize = II.StartValue->getType()->getScalarSizeInBits(); 1308 Value *CRD = CountRoundDown; 1309 if (CRDSize > IISize) 1310 CRD = CastInst::Create(Instruction::Trunc, CountRoundDown, 1311 II.StartValue->getType(), "tr.crd", 1312 LoopBypassBlocks.back()->getTerminator()); 1313 else if (CRDSize < IISize) 1314 CRD = CastInst::Create(Instruction::SExt, CountRoundDown, 1315 II.StartValue->getType(), 1316 "sext.crd", 1317 LoopBypassBlocks.back()->getTerminator()); 1318 // Handle reverse integer induction counter: 1319 EndValue = 1320 BinaryOperator::CreateSub(II.StartValue, CRD, "rev.ind.end", 1321 LoopBypassBlocks.back()->getTerminator()); 1322 break; 1323 } 1324 case LoopVectorizationLegality::IK_PtrInduction: { 1325 // For pointer induction variables, calculate the offset using 1326 // the end index. 1327 EndValue = 1328 GetElementPtrInst::Create(II.StartValue, CountRoundDown, "ptr.ind.end", 1329 LoopBypassBlocks.back()->getTerminator()); 1330 break; 1331 } 1332 case LoopVectorizationLegality::IK_ReversePtrInduction: { 1333 // The value at the end of the loop for the reverse pointer is calculated 1334 // by creating a GEP with a negative index starting from the start value. 1335 Value *Zero = ConstantInt::get(CountRoundDown->getType(), 0); 1336 Value *NegIdx = BinaryOperator::CreateSub(Zero, CountRoundDown, 1337 "rev.ind.end", 1338 LoopBypassBlocks.back()->getTerminator()); 1339 EndValue = GetElementPtrInst::Create(II.StartValue, NegIdx, 1340 "rev.ptr.ind.end", 1341 LoopBypassBlocks.back()->getTerminator()); 1342 break; 1343 } 1344 }// end of case 1345 1346 // The new PHI merges the original incoming value, in case of a bypass, 1347 // or the value at the end of the vectorized loop. 1348 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) 1349 ResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[I]); 1350 ResumeVal->addIncoming(EndValue, VecBody); 1351 1352 // Fix the scalar body counter (PHI node). 1353 unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH); 1354 OrigPhi->setIncomingValue(BlockIdx, ResumeVal); 1355 } 1356 1357 // If we are generating a new induction variable then we also need to 1358 // generate the code that calculates the exit value. This value is not 1359 // simply the end of the counter because we may skip the vectorized body 1360 // in case of a runtime check. 1361 if (!OldInduction){ 1362 assert(!ResumeIndex && "Unexpected resume value found"); 1363 ResumeIndex = PHINode::Create(IdxTy, 2, "new.indc.resume.val", 1364 MiddleBlock->getTerminator()); 1365 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) 1366 ResumeIndex->addIncoming(StartIdx, LoopBypassBlocks[I]); 1367 ResumeIndex->addIncoming(IdxEndRoundDown, VecBody); 1368 } 1369 1370 // Make sure that we found the index where scalar loop needs to continue. 1371 assert(ResumeIndex && ResumeIndex->getType()->isIntegerTy() && 1372 "Invalid resume Index"); 1373 1374 // Add a check in the middle block to see if we have completed 1375 // all of the iterations in the first vector loop. 1376 // If (N - N%VF) == N, then we *don't* need to run the remainder. 1377 Value *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, IdxEnd, 1378 ResumeIndex, "cmp.n", 1379 MiddleBlock->getTerminator()); 1380 1381 BranchInst::Create(ExitBlock, ScalarPH, CmpN, MiddleBlock->getTerminator()); 1382 // Remove the old terminator. 1383 MiddleBlock->getTerminator()->eraseFromParent(); 1384 1385 // Create i+1 and fill the PHINode. 1386 Value *NextIdx = Builder.CreateAdd(Induction, Step, "index.next"); 1387 Induction->addIncoming(StartIdx, VectorPH); 1388 Induction->addIncoming(NextIdx, VecBody); 1389 // Create the compare. 1390 Value *ICmp = Builder.CreateICmpEQ(NextIdx, IdxEndRoundDown); 1391 Builder.CreateCondBr(ICmp, MiddleBlock, VecBody); 1392 1393 // Now we have two terminators. Remove the old one from the block. 1394 VecBody->getTerminator()->eraseFromParent(); 1395 1396 // Get ready to start creating new instructions into the vectorized body. 1397 Builder.SetInsertPoint(VecBody->getFirstInsertionPt()); 1398 1399 // Create and register the new vector loop. 1400 Loop* Lp = new Loop(); 1401 Loop *ParentLoop = OrigLoop->getParentLoop(); 1402 1403 // Insert the new loop into the loop nest and register the new basic blocks. 1404 if (ParentLoop) { 1405 ParentLoop->addChildLoop(Lp); 1406 for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I) 1407 ParentLoop->addBasicBlockToLoop(LoopBypassBlocks[I], LI->getBase()); 1408 ParentLoop->addBasicBlockToLoop(ScalarPH, LI->getBase()); 1409 ParentLoop->addBasicBlockToLoop(VectorPH, LI->getBase()); 1410 ParentLoop->addBasicBlockToLoop(MiddleBlock, LI->getBase()); 1411 } else { 1412 LI->addTopLevelLoop(Lp); 1413 } 1414 1415 Lp->addBasicBlockToLoop(VecBody, LI->getBase()); 1416 1417 // Save the state. 1418 LoopVectorPreHeader = VectorPH; 1419 LoopScalarPreHeader = ScalarPH; 1420 LoopMiddleBlock = MiddleBlock; 1421 LoopExitBlock = ExitBlock; 1422 LoopVectorBody = VecBody; 1423 LoopScalarBody = OldBasicBlock; 1424} 1425 1426/// This function returns the identity element (or neutral element) for 1427/// the operation K. 1428static Constant* 1429getReductionIdentity(LoopVectorizationLegality::ReductionKind K, Type *Tp) { 1430 switch (K) { 1431 case LoopVectorizationLegality:: RK_IntegerXor: 1432 case LoopVectorizationLegality:: RK_IntegerAdd: 1433 case LoopVectorizationLegality:: RK_IntegerOr: 1434 // Adding, Xoring, Oring zero to a number does not change it. 1435 return ConstantInt::get(Tp, 0); 1436 case LoopVectorizationLegality:: RK_IntegerMult: 1437 // Multiplying a number by 1 does not change it. 1438 return ConstantInt::get(Tp, 1); 1439 case LoopVectorizationLegality:: RK_IntegerAnd: 1440 // AND-ing a number with an all-1 value does not change it. 1441 return ConstantInt::get(Tp, -1, true); 1442 case LoopVectorizationLegality:: RK_FloatMult: 1443 // Multiplying a number by 1 does not change it. 1444 return ConstantFP::get(Tp, 1.0L); 1445 case LoopVectorizationLegality:: RK_FloatAdd: 1446 // Adding zero to a number does not change it. 1447 return ConstantFP::get(Tp, 0.0L); 1448 default: 1449 llvm_unreachable("Unknown reduction kind"); 1450 } 1451} 1452 1453static Intrinsic::ID 1454getIntrinsicIDForCall(CallInst *CI, const TargetLibraryInfo *TLI) { 1455 // If we have an intrinsic call, check if it is trivially vectorizable. 1456 if (IntrinsicInst *II = dyn_cast<IntrinsicInst>(CI)) { 1457 switch (II->getIntrinsicID()) { 1458 case Intrinsic::sqrt: 1459 case Intrinsic::sin: 1460 case Intrinsic::cos: 1461 case Intrinsic::exp: 1462 case Intrinsic::exp2: 1463 case Intrinsic::log: 1464 case Intrinsic::log10: 1465 case Intrinsic::log2: 1466 case Intrinsic::fabs: 1467 case Intrinsic::floor: 1468 case Intrinsic::ceil: 1469 case Intrinsic::trunc: 1470 case Intrinsic::rint: 1471 case Intrinsic::nearbyint: 1472 case Intrinsic::pow: 1473 case Intrinsic::fma: 1474 case Intrinsic::fmuladd: 1475 return II->getIntrinsicID(); 1476 default: 1477 return Intrinsic::not_intrinsic; 1478 } 1479 } 1480 1481 if (!TLI) 1482 return Intrinsic::not_intrinsic; 1483 1484 LibFunc::Func Func; 1485 Function *F = CI->getCalledFunction(); 1486 // We're going to make assumptions on the semantics of the functions, check 1487 // that the target knows that it's available in this environment. 1488 if (!F || !TLI->getLibFunc(F->getName(), Func)) 1489 return Intrinsic::not_intrinsic; 1490 1491 // Otherwise check if we have a call to a function that can be turned into a 1492 // vector intrinsic. 1493 switch (Func) { 1494 default: 1495 break; 1496 case LibFunc::sin: 1497 case LibFunc::sinf: 1498 case LibFunc::sinl: 1499 return Intrinsic::sin; 1500 case LibFunc::cos: 1501 case LibFunc::cosf: 1502 case LibFunc::cosl: 1503 return Intrinsic::cos; 1504 case LibFunc::exp: 1505 case LibFunc::expf: 1506 case LibFunc::expl: 1507 return Intrinsic::exp; 1508 case LibFunc::exp2: 1509 case LibFunc::exp2f: 1510 case LibFunc::exp2l: 1511 return Intrinsic::exp2; 1512 case LibFunc::log: 1513 case LibFunc::logf: 1514 case LibFunc::logl: 1515 return Intrinsic::log; 1516 case LibFunc::log10: 1517 case LibFunc::log10f: 1518 case LibFunc::log10l: 1519 return Intrinsic::log10; 1520 case LibFunc::log2: 1521 case LibFunc::log2f: 1522 case LibFunc::log2l: 1523 return Intrinsic::log2; 1524 case LibFunc::fabs: 1525 case LibFunc::fabsf: 1526 case LibFunc::fabsl: 1527 return Intrinsic::fabs; 1528 case LibFunc::floor: 1529 case LibFunc::floorf: 1530 case LibFunc::floorl: 1531 return Intrinsic::floor; 1532 case LibFunc::ceil: 1533 case LibFunc::ceilf: 1534 case LibFunc::ceill: 1535 return Intrinsic::ceil; 1536 case LibFunc::trunc: 1537 case LibFunc::truncf: 1538 case LibFunc::truncl: 1539 return Intrinsic::trunc; 1540 case LibFunc::rint: 1541 case LibFunc::rintf: 1542 case LibFunc::rintl: 1543 return Intrinsic::rint; 1544 case LibFunc::nearbyint: 1545 case LibFunc::nearbyintf: 1546 case LibFunc::nearbyintl: 1547 return Intrinsic::nearbyint; 1548 case LibFunc::pow: 1549 case LibFunc::powf: 1550 case LibFunc::powl: 1551 return Intrinsic::pow; 1552 } 1553 1554 return Intrinsic::not_intrinsic; 1555} 1556 1557/// This function translates the reduction kind to an LLVM binary operator. 1558static Instruction::BinaryOps 1559getReductionBinOp(LoopVectorizationLegality::ReductionKind Kind) { 1560 switch (Kind) { 1561 case LoopVectorizationLegality::RK_IntegerAdd: 1562 return Instruction::Add; 1563 case LoopVectorizationLegality::RK_IntegerMult: 1564 return Instruction::Mul; 1565 case LoopVectorizationLegality::RK_IntegerOr: 1566 return Instruction::Or; 1567 case LoopVectorizationLegality::RK_IntegerAnd: 1568 return Instruction::And; 1569 case LoopVectorizationLegality::RK_IntegerXor: 1570 return Instruction::Xor; 1571 case LoopVectorizationLegality::RK_FloatMult: 1572 return Instruction::FMul; 1573 case LoopVectorizationLegality::RK_FloatAdd: 1574 return Instruction::FAdd; 1575 default: 1576 llvm_unreachable("Unknown reduction operation"); 1577 } 1578} 1579 1580void 1581InnerLoopVectorizer::vectorizeLoop(LoopVectorizationLegality *Legal) { 1582 //===------------------------------------------------===// 1583 // 1584 // Notice: any optimization or new instruction that go 1585 // into the code below should be also be implemented in 1586 // the cost-model. 1587 // 1588 //===------------------------------------------------===// 1589 Constant *Zero = Builder.getInt32(0); 1590 1591 // In order to support reduction variables we need to be able to vectorize 1592 // Phi nodes. Phi nodes have cycles, so we need to vectorize them in two 1593 // stages. First, we create a new vector PHI node with no incoming edges. 1594 // We use this value when we vectorize all of the instructions that use the 1595 // PHI. Next, after all of the instructions in the block are complete we 1596 // add the new incoming edges to the PHI. At this point all of the 1597 // instructions in the basic block are vectorized, so we can use them to 1598 // construct the PHI. 1599 PhiVector RdxPHIsToFix; 1600 1601 // Scan the loop in a topological order to ensure that defs are vectorized 1602 // before users. 1603 LoopBlocksDFS DFS(OrigLoop); 1604 DFS.perform(LI); 1605 1606 // Vectorize all of the blocks in the original loop. 1607 for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(), 1608 be = DFS.endRPO(); bb != be; ++bb) 1609 vectorizeBlockInLoop(Legal, *bb, &RdxPHIsToFix); 1610 1611 // At this point every instruction in the original loop is widened to 1612 // a vector form. We are almost done. Now, we need to fix the PHI nodes 1613 // that we vectorized. The PHI nodes are currently empty because we did 1614 // not want to introduce cycles. Notice that the remaining PHI nodes 1615 // that we need to fix are reduction variables. 1616 1617 // Create the 'reduced' values for each of the induction vars. 1618 // The reduced values are the vector values that we scalarize and combine 1619 // after the loop is finished. 1620 for (PhiVector::iterator it = RdxPHIsToFix.begin(), e = RdxPHIsToFix.end(); 1621 it != e; ++it) { 1622 PHINode *RdxPhi = *it; 1623 assert(RdxPhi && "Unable to recover vectorized PHI"); 1624 1625 // Find the reduction variable descriptor. 1626 assert(Legal->getReductionVars()->count(RdxPhi) && 1627 "Unable to find the reduction variable"); 1628 LoopVectorizationLegality::ReductionDescriptor RdxDesc = 1629 (*Legal->getReductionVars())[RdxPhi]; 1630 1631 // We need to generate a reduction vector from the incoming scalar. 1632 // To do so, we need to generate the 'identity' vector and overide 1633 // one of the elements with the incoming scalar reduction. We need 1634 // to do it in the vector-loop preheader. 1635 Builder.SetInsertPoint(LoopBypassBlocks.back()->getTerminator()); 1636 1637 // This is the vector-clone of the value that leaves the loop. 1638 VectorParts &VectorExit = getVectorValue(RdxDesc.LoopExitInstr); 1639 Type *VecTy = VectorExit[0]->getType(); 1640 1641 // Find the reduction identity variable. Zero for addition, or, xor, 1642 // one for multiplication, -1 for And. 1643 Constant *Iden = getReductionIdentity(RdxDesc.Kind, VecTy->getScalarType()); 1644 Constant *Identity = ConstantVector::getSplat(VF, Iden); 1645 1646 // This vector is the Identity vector where the first element is the 1647 // incoming scalar reduction. 1648 Value *VectorStart = Builder.CreateInsertElement(Identity, 1649 RdxDesc.StartValue, Zero); 1650 1651 // Fix the vector-loop phi. 1652 // We created the induction variable so we know that the 1653 // preheader is the first entry. 1654 BasicBlock *VecPreheader = Induction->getIncomingBlock(0); 1655 1656 // Reductions do not have to start at zero. They can start with 1657 // any loop invariant values. 1658 VectorParts &VecRdxPhi = WidenMap.get(RdxPhi); 1659 BasicBlock *Latch = OrigLoop->getLoopLatch(); 1660 Value *LoopVal = RdxPhi->getIncomingValueForBlock(Latch); 1661 VectorParts &Val = getVectorValue(LoopVal); 1662 for (unsigned part = 0; part < UF; ++part) { 1663 // Make sure to add the reduction stat value only to the 1664 // first unroll part. 1665 Value *StartVal = (part == 0) ? VectorStart : Identity; 1666 cast<PHINode>(VecRdxPhi[part])->addIncoming(StartVal, VecPreheader); 1667 cast<PHINode>(VecRdxPhi[part])->addIncoming(Val[part], LoopVectorBody); 1668 } 1669 1670 // Before each round, move the insertion point right between 1671 // the PHIs and the values we are going to write. 1672 // This allows us to write both PHINodes and the extractelement 1673 // instructions. 1674 Builder.SetInsertPoint(LoopMiddleBlock->getFirstInsertionPt()); 1675 1676 VectorParts RdxParts; 1677 for (unsigned part = 0; part < UF; ++part) { 1678 // This PHINode contains the vectorized reduction variable, or 1679 // the initial value vector, if we bypass the vector loop. 1680 VectorParts &RdxExitVal = getVectorValue(RdxDesc.LoopExitInstr); 1681 PHINode *NewPhi = Builder.CreatePHI(VecTy, 2, "rdx.vec.exit.phi"); 1682 Value *StartVal = (part == 0) ? VectorStart : Identity; 1683 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) 1684 NewPhi->addIncoming(StartVal, LoopBypassBlocks[I]); 1685 NewPhi->addIncoming(RdxExitVal[part], LoopVectorBody); 1686 RdxParts.push_back(NewPhi); 1687 } 1688 1689 // Reduce all of the unrolled parts into a single vector. 1690 Value *ReducedPartRdx = RdxParts[0]; 1691 for (unsigned part = 1; part < UF; ++part) { 1692 Instruction::BinaryOps Op = getReductionBinOp(RdxDesc.Kind); 1693 ReducedPartRdx = Builder.CreateBinOp(Op, RdxParts[part], ReducedPartRdx, 1694 "bin.rdx"); 1695 } 1696 1697 // VF is a power of 2 so we can emit the reduction using log2(VF) shuffles 1698 // and vector ops, reducing the set of values being computed by half each 1699 // round. 1700 assert(isPowerOf2_32(VF) && 1701 "Reduction emission only supported for pow2 vectors!"); 1702 Value *TmpVec = ReducedPartRdx; 1703 SmallVector<Constant*, 32> ShuffleMask(VF, 0); 1704 for (unsigned i = VF; i != 1; i >>= 1) { 1705 // Move the upper half of the vector to the lower half. 1706 for (unsigned j = 0; j != i/2; ++j) 1707 ShuffleMask[j] = Builder.getInt32(i/2 + j); 1708 1709 // Fill the rest of the mask with undef. 1710 std::fill(&ShuffleMask[i/2], ShuffleMask.end(), 1711 UndefValue::get(Builder.getInt32Ty())); 1712 1713 Value *Shuf = 1714 Builder.CreateShuffleVector(TmpVec, 1715 UndefValue::get(TmpVec->getType()), 1716 ConstantVector::get(ShuffleMask), 1717 "rdx.shuf"); 1718 1719 Instruction::BinaryOps Op = getReductionBinOp(RdxDesc.Kind); 1720 TmpVec = Builder.CreateBinOp(Op, TmpVec, Shuf, "bin.rdx"); 1721 } 1722 1723 // The result is in the first element of the vector. 1724 Value *Scalar0 = Builder.CreateExtractElement(TmpVec, Builder.getInt32(0)); 1725 1726 // Now, we need to fix the users of the reduction variable 1727 // inside and outside of the scalar remainder loop. 1728 // We know that the loop is in LCSSA form. We need to update the 1729 // PHI nodes in the exit blocks. 1730 for (BasicBlock::iterator LEI = LoopExitBlock->begin(), 1731 LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) { 1732 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI); 1733 if (!LCSSAPhi) continue; 1734 1735 // All PHINodes need to have a single entry edge, or two if 1736 // we already fixed them. 1737 assert(LCSSAPhi->getNumIncomingValues() < 3 && "Invalid LCSSA PHI"); 1738 1739 // We found our reduction value exit-PHI. Update it with the 1740 // incoming bypass edge. 1741 if (LCSSAPhi->getIncomingValue(0) == RdxDesc.LoopExitInstr) { 1742 // Add an edge coming from the bypass. 1743 LCSSAPhi->addIncoming(Scalar0, LoopMiddleBlock); 1744 break; 1745 } 1746 }// end of the LCSSA phi scan. 1747 1748 // Fix the scalar loop reduction variable with the incoming reduction sum 1749 // from the vector body and from the backedge value. 1750 int IncomingEdgeBlockIdx = 1751 (RdxPhi)->getBasicBlockIndex(OrigLoop->getLoopLatch()); 1752 assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index"); 1753 // Pick the other block. 1754 int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1); 1755 (RdxPhi)->setIncomingValue(SelfEdgeBlockIdx, Scalar0); 1756 (RdxPhi)->setIncomingValue(IncomingEdgeBlockIdx, RdxDesc.LoopExitInstr); 1757 }// end of for each redux variable. 1758 1759 // The Loop exit block may have single value PHI nodes where the incoming 1760 // value is 'undef'. While vectorizing we only handled real values that 1761 // were defined inside the loop. Here we handle the 'undef case'. 1762 // See PR14725. 1763 for (BasicBlock::iterator LEI = LoopExitBlock->begin(), 1764 LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) { 1765 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI); 1766 if (!LCSSAPhi) continue; 1767 if (LCSSAPhi->getNumIncomingValues() == 1) 1768 LCSSAPhi->addIncoming(UndefValue::get(LCSSAPhi->getType()), 1769 LoopMiddleBlock); 1770 } 1771} 1772 1773InnerLoopVectorizer::VectorParts 1774InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) { 1775 assert(std::find(pred_begin(Dst), pred_end(Dst), Src) != pred_end(Dst) && 1776 "Invalid edge"); 1777 1778 VectorParts SrcMask = createBlockInMask(Src); 1779 1780 // The terminator has to be a branch inst! 1781 BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator()); 1782 assert(BI && "Unexpected terminator found"); 1783 1784 if (BI->isConditional()) { 1785 VectorParts EdgeMask = getVectorValue(BI->getCondition()); 1786 1787 if (BI->getSuccessor(0) != Dst) 1788 for (unsigned part = 0; part < UF; ++part) 1789 EdgeMask[part] = Builder.CreateNot(EdgeMask[part]); 1790 1791 for (unsigned part = 0; part < UF; ++part) 1792 EdgeMask[part] = Builder.CreateAnd(EdgeMask[part], SrcMask[part]); 1793 return EdgeMask; 1794 } 1795 1796 return SrcMask; 1797} 1798 1799InnerLoopVectorizer::VectorParts 1800InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) { 1801 assert(OrigLoop->contains(BB) && "Block is not a part of a loop"); 1802 1803 // Loop incoming mask is all-one. 1804 if (OrigLoop->getHeader() == BB) { 1805 Value *C = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 1); 1806 return getVectorValue(C); 1807 } 1808 1809 // This is the block mask. We OR all incoming edges, and with zero. 1810 Value *Zero = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 0); 1811 VectorParts BlockMask = getVectorValue(Zero); 1812 1813 // For each pred: 1814 for (pred_iterator it = pred_begin(BB), e = pred_end(BB); it != e; ++it) { 1815 VectorParts EM = createEdgeMask(*it, BB); 1816 for (unsigned part = 0; part < UF; ++part) 1817 BlockMask[part] = Builder.CreateOr(BlockMask[part], EM[part]); 1818 } 1819 1820 return BlockMask; 1821} 1822 1823void 1824InnerLoopVectorizer::vectorizeBlockInLoop(LoopVectorizationLegality *Legal, 1825 BasicBlock *BB, PhiVector *PV) { 1826 // For each instruction in the old loop. 1827 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 1828 VectorParts &Entry = WidenMap.get(it); 1829 switch (it->getOpcode()) { 1830 case Instruction::Br: 1831 // Nothing to do for PHIs and BR, since we already took care of the 1832 // loop control flow instructions. 1833 continue; 1834 case Instruction::PHI:{ 1835 PHINode* P = cast<PHINode>(it); 1836 // Handle reduction variables: 1837 if (Legal->getReductionVars()->count(P)) { 1838 for (unsigned part = 0; part < UF; ++part) { 1839 // This is phase one of vectorizing PHIs. 1840 Type *VecTy = VectorType::get(it->getType(), VF); 1841 Entry[part] = PHINode::Create(VecTy, 2, "vec.phi", 1842 LoopVectorBody-> getFirstInsertionPt()); 1843 } 1844 PV->push_back(P); 1845 continue; 1846 } 1847 1848 // Check for PHI nodes that are lowered to vector selects. 1849 if (P->getParent() != OrigLoop->getHeader()) { 1850 // We know that all PHIs in non header blocks are converted into 1851 // selects, so we don't have to worry about the insertion order and we 1852 // can just use the builder. 1853 1854 // At this point we generate the predication tree. There may be 1855 // duplications since this is a simple recursive scan, but future 1856 // optimizations will clean it up. 1857 VectorParts Cond = createEdgeMask(P->getIncomingBlock(0), 1858 P->getParent()); 1859 1860 for (unsigned part = 0; part < UF; ++part) { 1861 VectorParts &In0 = getVectorValue(P->getIncomingValue(0)); 1862 VectorParts &In1 = getVectorValue(P->getIncomingValue(1)); 1863 Entry[part] = Builder.CreateSelect(Cond[part], In0[part], In1[part], 1864 "predphi"); 1865 } 1866 continue; 1867 } 1868 1869 // This PHINode must be an induction variable. 1870 // Make sure that we know about it. 1871 assert(Legal->getInductionVars()->count(P) && 1872 "Not an induction variable"); 1873 1874 LoopVectorizationLegality::InductionInfo II = 1875 Legal->getInductionVars()->lookup(P); 1876 1877 switch (II.IK) { 1878 case LoopVectorizationLegality::IK_NoInduction: 1879 llvm_unreachable("Unknown induction"); 1880 case LoopVectorizationLegality::IK_IntInduction: { 1881 assert(P == OldInduction && "Unexpected PHI"); 1882 Value *Broadcasted = getBroadcastInstrs(Induction); 1883 // After broadcasting the induction variable we need to make the 1884 // vector consecutive by adding 0, 1, 2 ... 1885 for (unsigned part = 0; part < UF; ++part) 1886 Entry[part] = getConsecutiveVector(Broadcasted, VF * part, false); 1887 continue; 1888 } 1889 case LoopVectorizationLegality::IK_ReverseIntInduction: 1890 case LoopVectorizationLegality::IK_PtrInduction: 1891 case LoopVectorizationLegality::IK_ReversePtrInduction: 1892 // Handle reverse integer and pointer inductions. 1893 Value *StartIdx = 0; 1894 // If we have a single integer induction variable then use it. 1895 // Otherwise, start counting at zero. 1896 if (OldInduction) { 1897 LoopVectorizationLegality::InductionInfo OldII = 1898 Legal->getInductionVars()->lookup(OldInduction); 1899 StartIdx = OldII.StartValue; 1900 } else { 1901 StartIdx = ConstantInt::get(Induction->getType(), 0); 1902 } 1903 // This is the normalized GEP that starts counting at zero. 1904 Value *NormalizedIdx = Builder.CreateSub(Induction, StartIdx, 1905 "normalized.idx"); 1906 1907 // Handle the reverse integer induction variable case. 1908 if (LoopVectorizationLegality::IK_ReverseIntInduction == II.IK) { 1909 IntegerType *DstTy = cast<IntegerType>(II.StartValue->getType()); 1910 Value *CNI = Builder.CreateSExtOrTrunc(NormalizedIdx, DstTy, 1911 "resize.norm.idx"); 1912 Value *ReverseInd = Builder.CreateSub(II.StartValue, CNI, 1913 "reverse.idx"); 1914 1915 // This is a new value so do not hoist it out. 1916 Value *Broadcasted = getBroadcastInstrs(ReverseInd); 1917 // After broadcasting the induction variable we need to make the 1918 // vector consecutive by adding ... -3, -2, -1, 0. 1919 for (unsigned part = 0; part < UF; ++part) 1920 Entry[part] = getConsecutiveVector(Broadcasted, -VF * part, true); 1921 continue; 1922 } 1923 1924 // Handle the pointer induction variable case. 1925 assert(P->getType()->isPointerTy() && "Unexpected type."); 1926 1927 // Is this a reverse induction ptr or a consecutive induction ptr. 1928 bool Reverse = (LoopVectorizationLegality::IK_ReversePtrInduction == 1929 II.IK); 1930 1931 // This is the vector of results. Notice that we don't generate 1932 // vector geps because scalar geps result in better code. 1933 for (unsigned part = 0; part < UF; ++part) { 1934 Value *VecVal = UndefValue::get(VectorType::get(P->getType(), VF)); 1935 for (unsigned int i = 0; i < VF; ++i) { 1936 int EltIndex = (i + part * VF) * (Reverse ? -1 : 1); 1937 Constant *Idx = ConstantInt::get(Induction->getType(), EltIndex); 1938 Value *GlobalIdx; 1939 if (!Reverse) 1940 GlobalIdx = Builder.CreateAdd(NormalizedIdx, Idx, "gep.idx"); 1941 else 1942 GlobalIdx = Builder.CreateSub(Idx, NormalizedIdx, "gep.ridx"); 1943 1944 Value *SclrGep = Builder.CreateGEP(II.StartValue, GlobalIdx, 1945 "next.gep"); 1946 VecVal = Builder.CreateInsertElement(VecVal, SclrGep, 1947 Builder.getInt32(i), 1948 "insert.gep"); 1949 } 1950 Entry[part] = VecVal; 1951 } 1952 continue; 1953 } 1954 1955 }// End of PHI. 1956 1957 case Instruction::Add: 1958 case Instruction::FAdd: 1959 case Instruction::Sub: 1960 case Instruction::FSub: 1961 case Instruction::Mul: 1962 case Instruction::FMul: 1963 case Instruction::UDiv: 1964 case Instruction::SDiv: 1965 case Instruction::FDiv: 1966 case Instruction::URem: 1967 case Instruction::SRem: 1968 case Instruction::FRem: 1969 case Instruction::Shl: 1970 case Instruction::LShr: 1971 case Instruction::AShr: 1972 case Instruction::And: 1973 case Instruction::Or: 1974 case Instruction::Xor: { 1975 // Just widen binops. 1976 BinaryOperator *BinOp = dyn_cast<BinaryOperator>(it); 1977 VectorParts &A = getVectorValue(it->getOperand(0)); 1978 VectorParts &B = getVectorValue(it->getOperand(1)); 1979 1980 // Use this vector value for all users of the original instruction. 1981 for (unsigned Part = 0; Part < UF; ++Part) { 1982 Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A[Part], B[Part]); 1983 1984 // Update the NSW, NUW and Exact flags. Notice: V can be an Undef. 1985 BinaryOperator *VecOp = dyn_cast<BinaryOperator>(V); 1986 if (VecOp && isa<OverflowingBinaryOperator>(BinOp)) { 1987 VecOp->setHasNoSignedWrap(BinOp->hasNoSignedWrap()); 1988 VecOp->setHasNoUnsignedWrap(BinOp->hasNoUnsignedWrap()); 1989 } 1990 if (VecOp && isa<PossiblyExactOperator>(VecOp)) 1991 VecOp->setIsExact(BinOp->isExact()); 1992 1993 Entry[Part] = V; 1994 } 1995 break; 1996 } 1997 case Instruction::Select: { 1998 // Widen selects. 1999 // If the selector is loop invariant we can create a select 2000 // instruction with a scalar condition. Otherwise, use vector-select. 2001 bool InvariantCond = SE->isLoopInvariant(SE->getSCEV(it->getOperand(0)), 2002 OrigLoop); 2003 2004 // The condition can be loop invariant but still defined inside the 2005 // loop. This means that we can't just use the original 'cond' value. 2006 // We have to take the 'vectorized' value and pick the first lane. 2007 // Instcombine will make this a no-op. 2008 VectorParts &Cond = getVectorValue(it->getOperand(0)); 2009 VectorParts &Op0 = getVectorValue(it->getOperand(1)); 2010 VectorParts &Op1 = getVectorValue(it->getOperand(2)); 2011 Value *ScalarCond = Builder.CreateExtractElement(Cond[0], 2012 Builder.getInt32(0)); 2013 for (unsigned Part = 0; Part < UF; ++Part) { 2014 Entry[Part] = Builder.CreateSelect( 2015 InvariantCond ? ScalarCond : Cond[Part], 2016 Op0[Part], 2017 Op1[Part]); 2018 } 2019 break; 2020 } 2021 2022 case Instruction::ICmp: 2023 case Instruction::FCmp: { 2024 // Widen compares. Generate vector compares. 2025 bool FCmp = (it->getOpcode() == Instruction::FCmp); 2026 CmpInst *Cmp = dyn_cast<CmpInst>(it); 2027 VectorParts &A = getVectorValue(it->getOperand(0)); 2028 VectorParts &B = getVectorValue(it->getOperand(1)); 2029 for (unsigned Part = 0; Part < UF; ++Part) { 2030 Value *C = 0; 2031 if (FCmp) 2032 C = Builder.CreateFCmp(Cmp->getPredicate(), A[Part], B[Part]); 2033 else 2034 C = Builder.CreateICmp(Cmp->getPredicate(), A[Part], B[Part]); 2035 Entry[Part] = C; 2036 } 2037 break; 2038 } 2039 2040 case Instruction::Store: 2041 case Instruction::Load: 2042 vectorizeMemoryInstruction(it, Legal); 2043 break; 2044 case Instruction::ZExt: 2045 case Instruction::SExt: 2046 case Instruction::FPToUI: 2047 case Instruction::FPToSI: 2048 case Instruction::FPExt: 2049 case Instruction::PtrToInt: 2050 case Instruction::IntToPtr: 2051 case Instruction::SIToFP: 2052 case Instruction::UIToFP: 2053 case Instruction::Trunc: 2054 case Instruction::FPTrunc: 2055 case Instruction::BitCast: { 2056 CastInst *CI = dyn_cast<CastInst>(it); 2057 /// Optimize the special case where the source is the induction 2058 /// variable. Notice that we can only optimize the 'trunc' case 2059 /// because: a. FP conversions lose precision, b. sext/zext may wrap, 2060 /// c. other casts depend on pointer size. 2061 if (CI->getOperand(0) == OldInduction && 2062 it->getOpcode() == Instruction::Trunc) { 2063 Value *ScalarCast = Builder.CreateCast(CI->getOpcode(), Induction, 2064 CI->getType()); 2065 Value *Broadcasted = getBroadcastInstrs(ScalarCast); 2066 for (unsigned Part = 0; Part < UF; ++Part) 2067 Entry[Part] = getConsecutiveVector(Broadcasted, VF * Part, false); 2068 break; 2069 } 2070 /// Vectorize casts. 2071 Type *DestTy = VectorType::get(CI->getType()->getScalarType(), VF); 2072 2073 VectorParts &A = getVectorValue(it->getOperand(0)); 2074 for (unsigned Part = 0; Part < UF; ++Part) 2075 Entry[Part] = Builder.CreateCast(CI->getOpcode(), A[Part], DestTy); 2076 break; 2077 } 2078 2079 case Instruction::Call: { 2080 Module *M = BB->getParent()->getParent(); 2081 CallInst *CI = cast<CallInst>(it); 2082 Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI); 2083 assert(ID && "Not an intrinsic call!"); 2084 for (unsigned Part = 0; Part < UF; ++Part) { 2085 SmallVector<Value*, 4> Args; 2086 for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) { 2087 VectorParts &Arg = getVectorValue(CI->getArgOperand(i)); 2088 Args.push_back(Arg[Part]); 2089 } 2090 Type *Tys[] = { VectorType::get(CI->getType()->getScalarType(), VF) }; 2091 Function *F = Intrinsic::getDeclaration(M, ID, Tys); 2092 Entry[Part] = Builder.CreateCall(F, Args); 2093 } 2094 break; 2095 } 2096 2097 default: 2098 // All other instructions are unsupported. Scalarize them. 2099 scalarizeInstruction(it); 2100 break; 2101 }// end of switch. 2102 }// end of for_each instr. 2103} 2104 2105void InnerLoopVectorizer::updateAnalysis() { 2106 // Forget the original basic block. 2107 SE->forgetLoop(OrigLoop); 2108 2109 // Update the dominator tree information. 2110 assert(DT->properlyDominates(LoopBypassBlocks.front(), LoopExitBlock) && 2111 "Entry does not dominate exit."); 2112 2113 for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I) 2114 DT->addNewBlock(LoopBypassBlocks[I], LoopBypassBlocks[I-1]); 2115 DT->addNewBlock(LoopVectorPreHeader, LoopBypassBlocks.back()); 2116 DT->addNewBlock(LoopVectorBody, LoopVectorPreHeader); 2117 DT->addNewBlock(LoopMiddleBlock, LoopBypassBlocks.front()); 2118 DT->addNewBlock(LoopScalarPreHeader, LoopMiddleBlock); 2119 DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader); 2120 DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock); 2121 2122 DEBUG(DT->verifyAnalysis()); 2123} 2124 2125bool LoopVectorizationLegality::canVectorizeWithIfConvert() { 2126 if (!EnableIfConversion) 2127 return false; 2128 2129 assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable"); 2130 std::vector<BasicBlock*> &LoopBlocks = TheLoop->getBlocksVector(); 2131 2132 // Collect the blocks that need predication. 2133 for (unsigned i = 0, e = LoopBlocks.size(); i < e; ++i) { 2134 BasicBlock *BB = LoopBlocks[i]; 2135 2136 // We don't support switch statements inside loops. 2137 if (!isa<BranchInst>(BB->getTerminator())) 2138 return false; 2139 2140 // We must have at most two predecessors because we need to convert 2141 // all PHIs to selects. 2142 unsigned Preds = std::distance(pred_begin(BB), pred_end(BB)); 2143 if (Preds > 2) 2144 return false; 2145 2146 // We must be able to predicate all blocks that need to be predicated. 2147 if (blockNeedsPredication(BB) && !blockCanBePredicated(BB)) 2148 return false; 2149 } 2150 2151 // We can if-convert this loop. 2152 return true; 2153} 2154 2155bool LoopVectorizationLegality::canVectorize() { 2156 assert(TheLoop->getLoopPreheader() && "No preheader!!"); 2157 2158 // We can only vectorize innermost loops. 2159 if (TheLoop->getSubLoopsVector().size()) 2160 return false; 2161 2162 // We must have a single backedge. 2163 if (TheLoop->getNumBackEdges() != 1) 2164 return false; 2165 2166 // We must have a single exiting block. 2167 if (!TheLoop->getExitingBlock()) 2168 return false; 2169 2170 unsigned NumBlocks = TheLoop->getNumBlocks(); 2171 2172 // Check if we can if-convert non single-bb loops. 2173 if (NumBlocks != 1 && !canVectorizeWithIfConvert()) { 2174 DEBUG(dbgs() << "LV: Can't if-convert the loop.\n"); 2175 return false; 2176 } 2177 2178 // We need to have a loop header. 2179 BasicBlock *Latch = TheLoop->getLoopLatch(); 2180 DEBUG(dbgs() << "LV: Found a loop: " << 2181 TheLoop->getHeader()->getName() << "\n"); 2182 2183 // ScalarEvolution needs to be able to find the exit count. 2184 const SCEV *ExitCount = SE->getExitCount(TheLoop, Latch); 2185 if (ExitCount == SE->getCouldNotCompute()) { 2186 DEBUG(dbgs() << "LV: SCEV could not compute the loop exit count.\n"); 2187 return false; 2188 } 2189 2190 // Do not loop-vectorize loops with a tiny trip count. 2191 unsigned TC = SE->getSmallConstantTripCount(TheLoop, Latch); 2192 if (TC > 0u && TC < TinyTripCountVectorThreshold) { 2193 DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " << 2194 "This loop is not worth vectorizing.\n"); 2195 return false; 2196 } 2197 2198 // Check if we can vectorize the instructions and CFG in this loop. 2199 if (!canVectorizeInstrs()) { 2200 DEBUG(dbgs() << "LV: Can't vectorize the instructions or CFG\n"); 2201 return false; 2202 } 2203 2204 // Go over each instruction and look at memory deps. 2205 if (!canVectorizeMemory()) { 2206 DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n"); 2207 return false; 2208 } 2209 2210 // Collect all of the variables that remain uniform after vectorization. 2211 collectLoopUniforms(); 2212 2213 DEBUG(dbgs() << "LV: We can vectorize this loop" << 2214 (PtrRtCheck.Need ? " (with a runtime bound check)" : "") 2215 <<"!\n"); 2216 2217 // Okay! We can vectorize. At this point we don't have any other mem analysis 2218 // which may limit our maximum vectorization factor, so just return true with 2219 // no restrictions. 2220 return true; 2221} 2222 2223bool LoopVectorizationLegality::canVectorizeInstrs() { 2224 BasicBlock *PreHeader = TheLoop->getLoopPreheader(); 2225 BasicBlock *Header = TheLoop->getHeader(); 2226 2227 // For each block in the loop. 2228 for (Loop::block_iterator bb = TheLoop->block_begin(), 2229 be = TheLoop->block_end(); bb != be; ++bb) { 2230 2231 // Scan the instructions in the block and look for hazards. 2232 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e; 2233 ++it) { 2234 2235 if (PHINode *Phi = dyn_cast<PHINode>(it)) { 2236 // This should not happen because the loop should be normalized. 2237 if (Phi->getNumIncomingValues() != 2) { 2238 DEBUG(dbgs() << "LV: Found an invalid PHI.\n"); 2239 return false; 2240 } 2241 2242 // Check that this PHI type is allowed. 2243 if (!Phi->getType()->isIntegerTy() && 2244 !Phi->getType()->isFloatingPointTy() && 2245 !Phi->getType()->isPointerTy()) { 2246 DEBUG(dbgs() << "LV: Found an non-int non-pointer PHI.\n"); 2247 return false; 2248 } 2249 2250 // If this PHINode is not in the header block, then we know that we 2251 // can convert it to select during if-conversion. No need to check if 2252 // the PHIs in this block are induction or reduction variables. 2253 if (*bb != Header) 2254 continue; 2255 2256 // This is the value coming from the preheader. 2257 Value *StartValue = Phi->getIncomingValueForBlock(PreHeader); 2258 // Check if this is an induction variable. 2259 InductionKind IK = isInductionVariable(Phi); 2260 2261 if (IK_NoInduction != IK) { 2262 // Int inductions are special because we only allow one IV. 2263 if (IK == IK_IntInduction) { 2264 if (Induction) { 2265 DEBUG(dbgs() << "LV: Found too many inductions."<< *Phi <<"\n"); 2266 return false; 2267 } 2268 Induction = Phi; 2269 } 2270 2271 DEBUG(dbgs() << "LV: Found an induction variable.\n"); 2272 Inductions[Phi] = InductionInfo(StartValue, IK); 2273 continue; 2274 } 2275 2276 if (AddReductionVar(Phi, RK_IntegerAdd)) { 2277 DEBUG(dbgs() << "LV: Found an ADD reduction PHI."<< *Phi <<"\n"); 2278 continue; 2279 } 2280 if (AddReductionVar(Phi, RK_IntegerMult)) { 2281 DEBUG(dbgs() << "LV: Found a MUL reduction PHI."<< *Phi <<"\n"); 2282 continue; 2283 } 2284 if (AddReductionVar(Phi, RK_IntegerOr)) { 2285 DEBUG(dbgs() << "LV: Found an OR reduction PHI."<< *Phi <<"\n"); 2286 continue; 2287 } 2288 if (AddReductionVar(Phi, RK_IntegerAnd)) { 2289 DEBUG(dbgs() << "LV: Found an AND reduction PHI."<< *Phi <<"\n"); 2290 continue; 2291 } 2292 if (AddReductionVar(Phi, RK_IntegerXor)) { 2293 DEBUG(dbgs() << "LV: Found a XOR reduction PHI."<< *Phi <<"\n"); 2294 continue; 2295 } 2296 if (AddReductionVar(Phi, RK_FloatMult)) { 2297 DEBUG(dbgs() << "LV: Found an FMult reduction PHI."<< *Phi <<"\n"); 2298 continue; 2299 } 2300 if (AddReductionVar(Phi, RK_FloatAdd)) { 2301 DEBUG(dbgs() << "LV: Found an FAdd reduction PHI."<< *Phi <<"\n"); 2302 continue; 2303 } 2304 2305 DEBUG(dbgs() << "LV: Found an unidentified PHI."<< *Phi <<"\n"); 2306 return false; 2307 }// end of PHI handling 2308 2309 // We still don't handle functions. 2310 CallInst *CI = dyn_cast<CallInst>(it); 2311 if (CI && !getIntrinsicIDForCall(CI, TLI)) { 2312 DEBUG(dbgs() << "LV: Found a call site.\n"); 2313 return false; 2314 } 2315 2316 // Check that the instruction return type is vectorizable. 2317 if (!VectorType::isValidElementType(it->getType()) && 2318 !it->getType()->isVoidTy()) { 2319 DEBUG(dbgs() << "LV: Found unvectorizable type." << "\n"); 2320 return false; 2321 } 2322 2323 // Check that the stored type is vectorizable. 2324 if (StoreInst *ST = dyn_cast<StoreInst>(it)) { 2325 Type *T = ST->getValueOperand()->getType(); 2326 if (!VectorType::isValidElementType(T)) 2327 return false; 2328 } 2329 2330 // Reduction instructions are allowed to have exit users. 2331 // All other instructions must not have external users. 2332 if (!AllowedExit.count(it)) 2333 //Check that all of the users of the loop are inside the BB. 2334 for (Value::use_iterator I = it->use_begin(), E = it->use_end(); 2335 I != E; ++I) { 2336 Instruction *U = cast<Instruction>(*I); 2337 // This user may be a reduction exit value. 2338 if (!TheLoop->contains(U)) { 2339 DEBUG(dbgs() << "LV: Found an outside user for : "<< *U << "\n"); 2340 return false; 2341 } 2342 } 2343 } // next instr. 2344 2345 } 2346 2347 if (!Induction) { 2348 DEBUG(dbgs() << "LV: Did not find one integer induction var.\n"); 2349 assert(getInductionVars()->size() && "No induction variables"); 2350 } 2351 2352 return true; 2353} 2354 2355void LoopVectorizationLegality::collectLoopUniforms() { 2356 // We now know that the loop is vectorizable! 2357 // Collect variables that will remain uniform after vectorization. 2358 std::vector<Value*> Worklist; 2359 BasicBlock *Latch = TheLoop->getLoopLatch(); 2360 2361 // Start with the conditional branch and walk up the block. 2362 Worklist.push_back(Latch->getTerminator()->getOperand(0)); 2363 2364 while (Worklist.size()) { 2365 Instruction *I = dyn_cast<Instruction>(Worklist.back()); 2366 Worklist.pop_back(); 2367 2368 // Look at instructions inside this loop. 2369 // Stop when reaching PHI nodes. 2370 // TODO: we need to follow values all over the loop, not only in this block. 2371 if (!I || !TheLoop->contains(I) || isa<PHINode>(I)) 2372 continue; 2373 2374 // This is a known uniform. 2375 Uniforms.insert(I); 2376 2377 // Insert all operands. 2378 for (int i = 0, Op = I->getNumOperands(); i < Op; ++i) { 2379 Worklist.push_back(I->getOperand(i)); 2380 } 2381 } 2382} 2383 2384AliasAnalysis::Location 2385LoopVectorizationLegality::getLoadStoreLocation(Instruction *Inst) { 2386 if (StoreInst *Store = dyn_cast<StoreInst>(Inst)) 2387 return AA->getLocation(Store); 2388 else if (LoadInst *Load = dyn_cast<LoadInst>(Inst)) 2389 return AA->getLocation(Load); 2390 2391 llvm_unreachable("Should be either load or store instruction"); 2392} 2393 2394bool 2395LoopVectorizationLegality::hasPossibleGlobalWriteReorder( 2396 Value *Object, 2397 Instruction *Inst, 2398 AliasMultiMap& WriteObjects, 2399 unsigned MaxByteWidth) { 2400 2401 AliasAnalysis::Location ThisLoc = getLoadStoreLocation(Inst); 2402 2403 std::vector<Instruction*>::iterator 2404 it = WriteObjects[Object].begin(), 2405 end = WriteObjects[Object].end(); 2406 2407 for (; it != end; ++it) { 2408 Instruction* I = *it; 2409 if (I == Inst) 2410 continue; 2411 2412 AliasAnalysis::Location ThatLoc = getLoadStoreLocation(I); 2413 if (AA->alias(ThisLoc.getWithNewSize(MaxByteWidth), 2414 ThatLoc.getWithNewSize(MaxByteWidth))) 2415 return true; 2416 } 2417 return false; 2418} 2419 2420bool LoopVectorizationLegality::canVectorizeMemory() { 2421 2422 if (TheLoop->isAnnotatedParallel()) { 2423 DEBUG(dbgs() 2424 << "LV: A loop annotated parallel, ignore memory dependency " 2425 << "checks.\n"); 2426 return true; 2427 } 2428 2429 typedef SmallVector<Value*, 16> ValueVector; 2430 typedef SmallPtrSet<Value*, 16> ValueSet; 2431 // Holds the Load and Store *instructions*. 2432 ValueVector Loads; 2433 ValueVector Stores; 2434 PtrRtCheck.Pointers.clear(); 2435 PtrRtCheck.Need = false; 2436 2437 // For each block. 2438 for (Loop::block_iterator bb = TheLoop->block_begin(), 2439 be = TheLoop->block_end(); bb != be; ++bb) { 2440 2441 // Scan the BB and collect legal loads and stores. 2442 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e; 2443 ++it) { 2444 2445 // If this is a load, save it. If this instruction can read from memory 2446 // but is not a load, then we quit. Notice that we don't handle function 2447 // calls that read or write. 2448 if (it->mayReadFromMemory()) { 2449 LoadInst *Ld = dyn_cast<LoadInst>(it); 2450 if (!Ld) return false; 2451 if (!Ld->isSimple()) { 2452 DEBUG(dbgs() << "LV: Found a non-simple load.\n"); 2453 return false; 2454 } 2455 Loads.push_back(Ld); 2456 continue; 2457 } 2458 2459 // Save 'store' instructions. Abort if other instructions write to memory. 2460 if (it->mayWriteToMemory()) { 2461 StoreInst *St = dyn_cast<StoreInst>(it); 2462 if (!St) return false; 2463 if (!St->isSimple()) { 2464 DEBUG(dbgs() << "LV: Found a non-simple store.\n"); 2465 return false; 2466 } 2467 Stores.push_back(St); 2468 } 2469 } // next instr. 2470 } // next block. 2471 2472 // Now we have two lists that hold the loads and the stores. 2473 // Next, we find the pointers that they use. 2474 2475 // Check if we see any stores. If there are no stores, then we don't 2476 // care if the pointers are *restrict*. 2477 if (!Stores.size()) { 2478 DEBUG(dbgs() << "LV: Found a read-only loop!\n"); 2479 return true; 2480 } 2481 2482 // Holds the read and read-write *pointers* that we find. These maps hold 2483 // unique values for pointers (so no need for multi-map). 2484 AliasMap Reads; 2485 AliasMap ReadWrites; 2486 2487 // Holds the analyzed pointers. We don't want to call GetUnderlyingObjects 2488 // multiple times on the same object. If the ptr is accessed twice, once 2489 // for read and once for write, it will only appear once (on the write 2490 // list). This is okay, since we are going to check for conflicts between 2491 // writes and between reads and writes, but not between reads and reads. 2492 ValueSet Seen; 2493 2494 ValueVector::iterator I, IE; 2495 for (I = Stores.begin(), IE = Stores.end(); I != IE; ++I) { 2496 StoreInst *ST = cast<StoreInst>(*I); 2497 Value* Ptr = ST->getPointerOperand(); 2498 2499 if (isUniform(Ptr)) { 2500 DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n"); 2501 return false; 2502 } 2503 2504 // If we did *not* see this pointer before, insert it to 2505 // the read-write list. At this phase it is only a 'write' list. 2506 if (Seen.insert(Ptr)) 2507 ReadWrites.insert(std::make_pair(Ptr, ST)); 2508 } 2509 2510 for (I = Loads.begin(), IE = Loads.end(); I != IE; ++I) { 2511 LoadInst *LD = cast<LoadInst>(*I); 2512 Value* Ptr = LD->getPointerOperand(); 2513 // If we did *not* see this pointer before, insert it to the 2514 // read list. If we *did* see it before, then it is already in 2515 // the read-write list. This allows us to vectorize expressions 2516 // such as A[i] += x; Because the address of A[i] is a read-write 2517 // pointer. This only works if the index of A[i] is consecutive. 2518 // If the address of i is unknown (for example A[B[i]]) then we may 2519 // read a few words, modify, and write a few words, and some of the 2520 // words may be written to the same address. 2521 if (Seen.insert(Ptr) || 0 == isConsecutivePtr(Ptr)) 2522 Reads.insert(std::make_pair(Ptr, LD)); 2523 } 2524 2525 // If we write (or read-write) to a single destination and there are no 2526 // other reads in this loop then is it safe to vectorize. 2527 if (ReadWrites.size() == 1 && Reads.size() == 0) { 2528 DEBUG(dbgs() << "LV: Found a write-only loop!\n"); 2529 return true; 2530 } 2531 2532 // Find pointers with computable bounds. We are going to use this information 2533 // to place a runtime bound check. 2534 bool CanDoRT = true; 2535 AliasMap::iterator MI, ME; 2536 for (MI = ReadWrites.begin(), ME = ReadWrites.end(); MI != ME; ++MI) { 2537 Value *V = (*MI).first; 2538 if (hasComputableBounds(V)) { 2539 PtrRtCheck.insert(SE, TheLoop, V); 2540 DEBUG(dbgs() << "LV: Found a runtime check ptr:" << *V <<"\n"); 2541 } else { 2542 CanDoRT = false; 2543 break; 2544 } 2545 } 2546 for (MI = Reads.begin(), ME = Reads.end(); MI != ME; ++MI) { 2547 Value *V = (*MI).first; 2548 if (hasComputableBounds(V)) { 2549 PtrRtCheck.insert(SE, TheLoop, V); 2550 DEBUG(dbgs() << "LV: Found a runtime check ptr:" << *V <<"\n"); 2551 } else { 2552 CanDoRT = false; 2553 break; 2554 } 2555 } 2556 2557 // Check that we did not collect too many pointers or found a 2558 // unsizeable pointer. 2559 if (!CanDoRT || PtrRtCheck.Pointers.size() > RuntimeMemoryCheckThreshold) { 2560 PtrRtCheck.reset(); 2561 CanDoRT = false; 2562 } 2563 2564 if (CanDoRT) { 2565 DEBUG(dbgs() << "LV: We can perform a memory runtime check if needed.\n"); 2566 } 2567 2568 bool NeedRTCheck = false; 2569 2570 // Biggest vectorized access possible, vector width * unroll factor. 2571 // TODO: We're being very pessimistic here, find a way to know the 2572 // real access width before getting here. 2573 unsigned MaxByteWidth = (TTI->getRegisterBitWidth(true) / 8) * 2574 TTI->getMaximumUnrollFactor(); 2575 // Now that the pointers are in two lists (Reads and ReadWrites), we 2576 // can check that there are no conflicts between each of the writes and 2577 // between the writes to the reads. 2578 // Note that WriteObjects duplicates the stores (indexed now by underlying 2579 // objects) to avoid pointing to elements inside ReadWrites. 2580 // TODO: Maybe create a new type where they can interact without duplication. 2581 AliasMultiMap WriteObjects; 2582 ValueVector TempObjects; 2583 2584 // Check that the read-writes do not conflict with other read-write 2585 // pointers. 2586 bool AllWritesIdentified = true; 2587 for (MI = ReadWrites.begin(), ME = ReadWrites.end(); MI != ME; ++MI) { 2588 Value *Val = (*MI).first; 2589 Instruction *Inst = (*MI).second; 2590 2591 GetUnderlyingObjects(Val, TempObjects, DL); 2592 for (ValueVector::iterator UI=TempObjects.begin(), UE=TempObjects.end(); 2593 UI != UE; ++UI) { 2594 if (!isIdentifiedObject(*UI)) { 2595 DEBUG(dbgs() << "LV: Found an unidentified write ptr:"<< **UI <<"\n"); 2596 NeedRTCheck = true; 2597 AllWritesIdentified = false; 2598 } 2599 2600 // Never seen it before, can't alias. 2601 if (WriteObjects[*UI].empty()) { 2602 DEBUG(dbgs() << "LV: Adding Underlying value:" << **UI <<"\n"); 2603 WriteObjects[*UI].push_back(Inst); 2604 continue; 2605 } 2606 // Direct alias found. 2607 if (!AA || dyn_cast<GlobalValue>(*UI) == NULL) { 2608 DEBUG(dbgs() << "LV: Found a possible write-write reorder:" 2609 << **UI <<"\n"); 2610 return false; 2611 } 2612 DEBUG(dbgs() << "LV: Found a conflicting global value:" 2613 << **UI <<"\n"); 2614 DEBUG(dbgs() << "LV: While examining store:" << *Inst <<"\n"); 2615 DEBUG(dbgs() << "LV: On value:" << *Val <<"\n"); 2616 2617 // If global alias, make sure they do alias. 2618 if (hasPossibleGlobalWriteReorder(*UI, 2619 Inst, 2620 WriteObjects, 2621 MaxByteWidth)) { 2622 DEBUG(dbgs() << "LV: Found a possible write-write reorder:" 2623 << *UI <<"\n"); 2624 return false; 2625 } 2626 2627 // Didn't alias, insert into map for further reference. 2628 WriteObjects[*UI].push_back(Inst); 2629 } 2630 TempObjects.clear(); 2631 } 2632 2633 /// Check that the reads don't conflict with the read-writes. 2634 for (MI = Reads.begin(), ME = Reads.end(); MI != ME; ++MI) { 2635 Value *Val = (*MI).first; 2636 GetUnderlyingObjects(Val, TempObjects, DL); 2637 for (ValueVector::iterator UI=TempObjects.begin(), UE=TempObjects.end(); 2638 UI != UE; ++UI) { 2639 // If all of the writes are identified then we don't care if the read 2640 // pointer is identified or not. 2641 if (!AllWritesIdentified && !isIdentifiedObject(*UI)) { 2642 DEBUG(dbgs() << "LV: Found an unidentified read ptr:"<< **UI <<"\n"); 2643 NeedRTCheck = true; 2644 } 2645 2646 // Never seen it before, can't alias. 2647 if (WriteObjects[*UI].empty()) 2648 continue; 2649 // Direct alias found. 2650 if (!AA || dyn_cast<GlobalValue>(*UI) == NULL) { 2651 DEBUG(dbgs() << "LV: Found a possible write-write reorder:" 2652 << **UI <<"\n"); 2653 return false; 2654 } 2655 DEBUG(dbgs() << "LV: Found a global value: " 2656 << **UI <<"\n"); 2657 Instruction *Inst = (*MI).second; 2658 DEBUG(dbgs() << "LV: While examining load:" << *Inst <<"\n"); 2659 DEBUG(dbgs() << "LV: On value:" << *Val <<"\n"); 2660 2661 // If global alias, make sure they do alias. 2662 if (hasPossibleGlobalWriteReorder(*UI, 2663 Inst, 2664 WriteObjects, 2665 MaxByteWidth)) { 2666 DEBUG(dbgs() << "LV: Found a possible read-write reorder:" 2667 << *UI <<"\n"); 2668 return false; 2669 } 2670 } 2671 TempObjects.clear(); 2672 } 2673 2674 PtrRtCheck.Need = NeedRTCheck; 2675 if (NeedRTCheck && !CanDoRT) { 2676 DEBUG(dbgs() << "LV: We can't vectorize because we can't find " << 2677 "the array bounds.\n"); 2678 PtrRtCheck.reset(); 2679 return false; 2680 } 2681 2682 DEBUG(dbgs() << "LV: We "<< (NeedRTCheck ? "" : "don't") << 2683 " need a runtime memory check.\n"); 2684 return true; 2685} 2686 2687bool LoopVectorizationLegality::AddReductionVar(PHINode *Phi, 2688 ReductionKind Kind) { 2689 if (Phi->getNumIncomingValues() != 2) 2690 return false; 2691 2692 // Reduction variables are only found in the loop header block. 2693 if (Phi->getParent() != TheLoop->getHeader()) 2694 return false; 2695 2696 // Obtain the reduction start value from the value that comes from the loop 2697 // preheader. 2698 Value *RdxStart = Phi->getIncomingValueForBlock(TheLoop->getLoopPreheader()); 2699 2700 // ExitInstruction is the single value which is used outside the loop. 2701 // We only allow for a single reduction value to be used outside the loop. 2702 // This includes users of the reduction, variables (which form a cycle 2703 // which ends in the phi node). 2704 Instruction *ExitInstruction = 0; 2705 // Indicates that we found a binary operation in our scan. 2706 bool FoundBinOp = false; 2707 2708 // Iter is our iterator. We start with the PHI node and scan for all of the 2709 // users of this instruction. All users must be instructions that can be 2710 // used as reduction variables (such as ADD). We may have a single 2711 // out-of-block user. The cycle must end with the original PHI. 2712 Instruction *Iter = Phi; 2713 while (true) { 2714 // If the instruction has no users then this is a broken 2715 // chain and can't be a reduction variable. 2716 if (Iter->use_empty()) 2717 return false; 2718 2719 // Did we find a user inside this loop already ? 2720 bool FoundInBlockUser = false; 2721 // Did we reach the initial PHI node already ? 2722 bool FoundStartPHI = false; 2723 2724 // Is this a bin op ? 2725 FoundBinOp |= !isa<PHINode>(Iter); 2726 2727 // Remember the current instruction. 2728 Instruction *OldIter = Iter; 2729 2730 // For each of the *users* of iter. 2731 for (Value::use_iterator it = Iter->use_begin(), e = Iter->use_end(); 2732 it != e; ++it) { 2733 Instruction *U = cast<Instruction>(*it); 2734 // We already know that the PHI is a user. 2735 if (U == Phi) { 2736 FoundStartPHI = true; 2737 continue; 2738 } 2739 2740 // Check if we found the exit user. 2741 BasicBlock *Parent = U->getParent(); 2742 if (!TheLoop->contains(Parent)) { 2743 // Exit if you find multiple outside users. 2744 if (ExitInstruction != 0) 2745 return false; 2746 ExitInstruction = Iter; 2747 } 2748 2749 // We allow in-loop PHINodes which are not the original reduction PHI 2750 // node. If this PHI is the only user of Iter (happens in IF w/ no ELSE 2751 // structure) then don't skip this PHI. 2752 if (isa<PHINode>(Iter) && isa<PHINode>(U) && 2753 U->getParent() != TheLoop->getHeader() && 2754 TheLoop->contains(U) && 2755 Iter->hasNUsesOrMore(2)) 2756 continue; 2757 2758 // We can't have multiple inside users. 2759 if (FoundInBlockUser) 2760 return false; 2761 FoundInBlockUser = true; 2762 2763 // Any reduction instr must be of one of the allowed kinds. 2764 if (!isReductionInstr(U, Kind)) 2765 return false; 2766 2767 // Reductions of instructions such as Div, and Sub is only 2768 // possible if the LHS is the reduction variable. 2769 if (!U->isCommutative() && !isa<PHINode>(U) && U->getOperand(0) != Iter) 2770 return false; 2771 2772 Iter = U; 2773 } 2774 2775 // If all uses were skipped this can't be a reduction variable. 2776 if (Iter == OldIter) 2777 return false; 2778 2779 // We found a reduction var if we have reached the original 2780 // phi node and we only have a single instruction with out-of-loop 2781 // users. 2782 if (FoundStartPHI) { 2783 // This instruction is allowed to have out-of-loop users. 2784 AllowedExit.insert(ExitInstruction); 2785 2786 // Save the description of this reduction variable. 2787 ReductionDescriptor RD(RdxStart, ExitInstruction, Kind); 2788 Reductions[Phi] = RD; 2789 // We've ended the cycle. This is a reduction variable if we have an 2790 // outside user and it has a binary op. 2791 return FoundBinOp && ExitInstruction; 2792 } 2793 } 2794} 2795 2796bool 2797LoopVectorizationLegality::isReductionInstr(Instruction *I, 2798 ReductionKind Kind) { 2799 bool FP = I->getType()->isFloatingPointTy(); 2800 bool FastMath = (FP && I->isCommutative() && I->isAssociative()); 2801 2802 switch (I->getOpcode()) { 2803 default: 2804 return false; 2805 case Instruction::PHI: 2806 if (FP && (Kind != RK_FloatMult && Kind != RK_FloatAdd)) 2807 return false; 2808 // possibly. 2809 return true; 2810 case Instruction::Sub: 2811 case Instruction::Add: 2812 return Kind == RK_IntegerAdd; 2813 case Instruction::SDiv: 2814 case Instruction::UDiv: 2815 case Instruction::Mul: 2816 return Kind == RK_IntegerMult; 2817 case Instruction::And: 2818 return Kind == RK_IntegerAnd; 2819 case Instruction::Or: 2820 return Kind == RK_IntegerOr; 2821 case Instruction::Xor: 2822 return Kind == RK_IntegerXor; 2823 case Instruction::FMul: 2824 return Kind == RK_FloatMult && FastMath; 2825 case Instruction::FAdd: 2826 return Kind == RK_FloatAdd && FastMath; 2827 } 2828} 2829 2830LoopVectorizationLegality::InductionKind 2831LoopVectorizationLegality::isInductionVariable(PHINode *Phi) { 2832 Type *PhiTy = Phi->getType(); 2833 // We only handle integer and pointer inductions variables. 2834 if (!PhiTy->isIntegerTy() && !PhiTy->isPointerTy()) 2835 return IK_NoInduction; 2836 2837 // Check that the PHI is consecutive. 2838 const SCEV *PhiScev = SE->getSCEV(Phi); 2839 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev); 2840 if (!AR) { 2841 DEBUG(dbgs() << "LV: PHI is not a poly recurrence.\n"); 2842 return IK_NoInduction; 2843 } 2844 const SCEV *Step = AR->getStepRecurrence(*SE); 2845 2846 // Integer inductions need to have a stride of one. 2847 if (PhiTy->isIntegerTy()) { 2848 if (Step->isOne()) 2849 return IK_IntInduction; 2850 if (Step->isAllOnesValue()) 2851 return IK_ReverseIntInduction; 2852 return IK_NoInduction; 2853 } 2854 2855 // Calculate the pointer stride and check if it is consecutive. 2856 const SCEVConstant *C = dyn_cast<SCEVConstant>(Step); 2857 if (!C) 2858 return IK_NoInduction; 2859 2860 assert(PhiTy->isPointerTy() && "The PHI must be a pointer"); 2861 uint64_t Size = DL->getTypeAllocSize(PhiTy->getPointerElementType()); 2862 if (C->getValue()->equalsInt(Size)) 2863 return IK_PtrInduction; 2864 else if (C->getValue()->equalsInt(0 - Size)) 2865 return IK_ReversePtrInduction; 2866 2867 return IK_NoInduction; 2868} 2869 2870bool LoopVectorizationLegality::isInductionVariable(const Value *V) { 2871 Value *In0 = const_cast<Value*>(V); 2872 PHINode *PN = dyn_cast_or_null<PHINode>(In0); 2873 if (!PN) 2874 return false; 2875 2876 return Inductions.count(PN); 2877} 2878 2879bool LoopVectorizationLegality::blockNeedsPredication(BasicBlock *BB) { 2880 assert(TheLoop->contains(BB) && "Unknown block used"); 2881 2882 // Blocks that do not dominate the latch need predication. 2883 BasicBlock* Latch = TheLoop->getLoopLatch(); 2884 return !DT->dominates(BB, Latch); 2885} 2886 2887bool LoopVectorizationLegality::blockCanBePredicated(BasicBlock *BB) { 2888 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 2889 // We don't predicate loads/stores at the moment. 2890 if (it->mayReadFromMemory() || it->mayWriteToMemory() || it->mayThrow()) 2891 return false; 2892 2893 // The instructions below can trap. 2894 switch (it->getOpcode()) { 2895 default: continue; 2896 case Instruction::UDiv: 2897 case Instruction::SDiv: 2898 case Instruction::URem: 2899 case Instruction::SRem: 2900 return false; 2901 } 2902 } 2903 2904 return true; 2905} 2906 2907bool LoopVectorizationLegality::hasComputableBounds(Value *Ptr) { 2908 const SCEV *PhiScev = SE->getSCEV(Ptr); 2909 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev); 2910 if (!AR) 2911 return false; 2912 2913 return AR->isAffine(); 2914} 2915 2916LoopVectorizationCostModel::VectorizationFactor 2917LoopVectorizationCostModel::selectVectorizationFactor(bool OptForSize, 2918 unsigned UserVF) { 2919 // Width 1 means no vectorize 2920 VectorizationFactor Factor = { 1U, 0U }; 2921 if (OptForSize && Legal->getRuntimePointerCheck()->Need) { 2922 DEBUG(dbgs() << "LV: Aborting. Runtime ptr check is required in Os.\n"); 2923 return Factor; 2924 } 2925 2926 // Find the trip count. 2927 unsigned TC = SE->getSmallConstantTripCount(TheLoop, TheLoop->getLoopLatch()); 2928 DEBUG(dbgs() << "LV: Found trip count:"<<TC<<"\n"); 2929 2930 unsigned WidestType = getWidestType(); 2931 unsigned WidestRegister = TTI.getRegisterBitWidth(true); 2932 unsigned MaxVectorSize = WidestRegister / WidestType; 2933 DEBUG(dbgs() << "LV: The Widest type: " << WidestType << " bits.\n"); 2934 DEBUG(dbgs() << "LV: The Widest register is:" << WidestRegister << "bits.\n"); 2935 2936 if (MaxVectorSize == 0) { 2937 DEBUG(dbgs() << "LV: The target has no vector registers.\n"); 2938 MaxVectorSize = 1; 2939 } 2940 2941 assert(MaxVectorSize <= 32 && "Did not expect to pack so many elements" 2942 " into one vector!"); 2943 2944 unsigned VF = MaxVectorSize; 2945 2946 // If we optimize the program for size, avoid creating the tail loop. 2947 if (OptForSize) { 2948 // If we are unable to calculate the trip count then don't try to vectorize. 2949 if (TC < 2) { 2950 DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n"); 2951 return Factor; 2952 } 2953 2954 // Find the maximum SIMD width that can fit within the trip count. 2955 VF = TC % MaxVectorSize; 2956 2957 if (VF == 0) 2958 VF = MaxVectorSize; 2959 2960 // If the trip count that we found modulo the vectorization factor is not 2961 // zero then we require a tail. 2962 if (VF < 2) { 2963 DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n"); 2964 return Factor; 2965 } 2966 } 2967 2968 if (UserVF != 0) { 2969 assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two"); 2970 DEBUG(dbgs() << "LV: Using user VF "<<UserVF<<".\n"); 2971 2972 Factor.Width = UserVF; 2973 return Factor; 2974 } 2975 2976 float Cost = expectedCost(1); 2977 unsigned Width = 1; 2978 DEBUG(dbgs() << "LV: Scalar loop costs: "<< (int)Cost << ".\n"); 2979 for (unsigned i=2; i <= VF; i*=2) { 2980 // Notice that the vector loop needs to be executed less times, so 2981 // we need to divide the cost of the vector loops by the width of 2982 // the vector elements. 2983 float VectorCost = expectedCost(i) / (float)i; 2984 DEBUG(dbgs() << "LV: Vector loop of width "<< i << " costs: " << 2985 (int)VectorCost << ".\n"); 2986 if (VectorCost < Cost) { 2987 Cost = VectorCost; 2988 Width = i; 2989 } 2990 } 2991 2992 DEBUG(dbgs() << "LV: Selecting VF = : "<< Width << ".\n"); 2993 Factor.Width = Width; 2994 Factor.Cost = Width * Cost; 2995 return Factor; 2996} 2997 2998unsigned LoopVectorizationCostModel::getWidestType() { 2999 unsigned MaxWidth = 8; 3000 3001 // For each block. 3002 for (Loop::block_iterator bb = TheLoop->block_begin(), 3003 be = TheLoop->block_end(); bb != be; ++bb) { 3004 BasicBlock *BB = *bb; 3005 3006 // For each instruction in the loop. 3007 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 3008 Type *T = it->getType(); 3009 3010 // Only examine Loads, Stores and PHINodes. 3011 if (!isa<LoadInst>(it) && !isa<StoreInst>(it) && !isa<PHINode>(it)) 3012 continue; 3013 3014 // Examine PHI nodes that are reduction variables. 3015 if (PHINode *PN = dyn_cast<PHINode>(it)) 3016 if (!Legal->getReductionVars()->count(PN)) 3017 continue; 3018 3019 // Examine the stored values. 3020 if (StoreInst *ST = dyn_cast<StoreInst>(it)) 3021 T = ST->getValueOperand()->getType(); 3022 3023 // Ignore loaded pointer types and stored pointer types that are not 3024 // consecutive. However, we do want to take consecutive stores/loads of 3025 // pointer vectors into account. 3026 if (T->isPointerTy() && !isConsecutiveLoadOrStore(it)) 3027 continue; 3028 3029 MaxWidth = std::max(MaxWidth, 3030 (unsigned)DL->getTypeSizeInBits(T->getScalarType())); 3031 } 3032 } 3033 3034 return MaxWidth; 3035} 3036 3037unsigned 3038LoopVectorizationCostModel::selectUnrollFactor(bool OptForSize, 3039 unsigned UserUF, 3040 unsigned VF, 3041 unsigned LoopCost) { 3042 3043 // -- The unroll heuristics -- 3044 // We unroll the loop in order to expose ILP and reduce the loop overhead. 3045 // There are many micro-architectural considerations that we can't predict 3046 // at this level. For example frontend pressure (on decode or fetch) due to 3047 // code size, or the number and capabilities of the execution ports. 3048 // 3049 // We use the following heuristics to select the unroll factor: 3050 // 1. If the code has reductions the we unroll in order to break the cross 3051 // iteration dependency. 3052 // 2. If the loop is really small then we unroll in order to reduce the loop 3053 // overhead. 3054 // 3. We don't unroll if we think that we will spill registers to memory due 3055 // to the increased register pressure. 3056 3057 // Use the user preference, unless 'auto' is selected. 3058 if (UserUF != 0) 3059 return UserUF; 3060 3061 // When we optimize for size we don't unroll. 3062 if (OptForSize) 3063 return 1; 3064 3065 // Do not unroll loops with a relatively small trip count. 3066 unsigned TC = SE->getSmallConstantTripCount(TheLoop, 3067 TheLoop->getLoopLatch()); 3068 if (TC > 1 && TC < TinyTripCountUnrollThreshold) 3069 return 1; 3070 3071 unsigned TargetVectorRegisters = TTI.getNumberOfRegisters(true); 3072 DEBUG(dbgs() << "LV: The target has " << TargetVectorRegisters << 3073 " vector registers\n"); 3074 3075 LoopVectorizationCostModel::RegisterUsage R = calculateRegisterUsage(); 3076 // We divide by these constants so assume that we have at least one 3077 // instruction that uses at least one register. 3078 R.MaxLocalUsers = std::max(R.MaxLocalUsers, 1U); 3079 R.NumInstructions = std::max(R.NumInstructions, 1U); 3080 3081 // We calculate the unroll factor using the following formula. 3082 // Subtract the number of loop invariants from the number of available 3083 // registers. These registers are used by all of the unrolled instances. 3084 // Next, divide the remaining registers by the number of registers that is 3085 // required by the loop, in order to estimate how many parallel instances 3086 // fit without causing spills. 3087 unsigned UF = (TargetVectorRegisters - R.LoopInvariantRegs) / R.MaxLocalUsers; 3088 3089 // Clamp the unroll factor ranges to reasonable factors. 3090 unsigned MaxUnrollSize = TTI.getMaximumUnrollFactor(); 3091 3092 // If we did not calculate the cost for VF (because the user selected the VF) 3093 // then we calculate the cost of VF here. 3094 if (LoopCost == 0) 3095 LoopCost = expectedCost(VF); 3096 3097 // Clamp the calculated UF to be between the 1 and the max unroll factor 3098 // that the target allows. 3099 if (UF > MaxUnrollSize) 3100 UF = MaxUnrollSize; 3101 else if (UF < 1) 3102 UF = 1; 3103 3104 if (Legal->getReductionVars()->size()) { 3105 DEBUG(dbgs() << "LV: Unrolling because of reductions. \n"); 3106 return UF; 3107 } 3108 3109 // We want to unroll tiny loops in order to reduce the loop overhead. 3110 // We assume that the cost overhead is 1 and we use the cost model 3111 // to estimate the cost of the loop and unroll until the cost of the 3112 // loop overhead is about 5% of the cost of the loop. 3113 DEBUG(dbgs() << "LV: Loop cost is "<< LoopCost <<" \n"); 3114 if (LoopCost < 20) { 3115 DEBUG(dbgs() << "LV: Unrolling to reduce branch cost. \n"); 3116 unsigned NewUF = 20/LoopCost + 1; 3117 return std::min(NewUF, UF); 3118 } 3119 3120 DEBUG(dbgs() << "LV: Not Unrolling. \n"); 3121 return 1; 3122} 3123 3124LoopVectorizationCostModel::RegisterUsage 3125LoopVectorizationCostModel::calculateRegisterUsage() { 3126 // This function calculates the register usage by measuring the highest number 3127 // of values that are alive at a single location. Obviously, this is a very 3128 // rough estimation. We scan the loop in a topological order in order and 3129 // assign a number to each instruction. We use RPO to ensure that defs are 3130 // met before their users. We assume that each instruction that has in-loop 3131 // users starts an interval. We record every time that an in-loop value is 3132 // used, so we have a list of the first and last occurrences of each 3133 // instruction. Next, we transpose this data structure into a multi map that 3134 // holds the list of intervals that *end* at a specific location. This multi 3135 // map allows us to perform a linear search. We scan the instructions linearly 3136 // and record each time that a new interval starts, by placing it in a set. 3137 // If we find this value in the multi-map then we remove it from the set. 3138 // The max register usage is the maximum size of the set. 3139 // We also search for instructions that are defined outside the loop, but are 3140 // used inside the loop. We need this number separately from the max-interval 3141 // usage number because when we unroll, loop-invariant values do not take 3142 // more register. 3143 LoopBlocksDFS DFS(TheLoop); 3144 DFS.perform(LI); 3145 3146 RegisterUsage R; 3147 R.NumInstructions = 0; 3148 3149 // Each 'key' in the map opens a new interval. The values 3150 // of the map are the index of the 'last seen' usage of the 3151 // instruction that is the key. 3152 typedef DenseMap<Instruction*, unsigned> IntervalMap; 3153 // Maps instruction to its index. 3154 DenseMap<unsigned, Instruction*> IdxToInstr; 3155 // Marks the end of each interval. 3156 IntervalMap EndPoint; 3157 // Saves the list of instruction indices that are used in the loop. 3158 SmallSet<Instruction*, 8> Ends; 3159 // Saves the list of values that are used in the loop but are 3160 // defined outside the loop, such as arguments and constants. 3161 SmallPtrSet<Value*, 8> LoopInvariants; 3162 3163 unsigned Index = 0; 3164 for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(), 3165 be = DFS.endRPO(); bb != be; ++bb) { 3166 R.NumInstructions += (*bb)->size(); 3167 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e; 3168 ++it) { 3169 Instruction *I = it; 3170 IdxToInstr[Index++] = I; 3171 3172 // Save the end location of each USE. 3173 for (unsigned i = 0; i < I->getNumOperands(); ++i) { 3174 Value *U = I->getOperand(i); 3175 Instruction *Instr = dyn_cast<Instruction>(U); 3176 3177 // Ignore non-instruction values such as arguments, constants, etc. 3178 if (!Instr) continue; 3179 3180 // If this instruction is outside the loop then record it and continue. 3181 if (!TheLoop->contains(Instr)) { 3182 LoopInvariants.insert(Instr); 3183 continue; 3184 } 3185 3186 // Overwrite previous end points. 3187 EndPoint[Instr] = Index; 3188 Ends.insert(Instr); 3189 } 3190 } 3191 } 3192 3193 // Saves the list of intervals that end with the index in 'key'. 3194 typedef SmallVector<Instruction*, 2> InstrList; 3195 DenseMap<unsigned, InstrList> TransposeEnds; 3196 3197 // Transpose the EndPoints to a list of values that end at each index. 3198 for (IntervalMap::iterator it = EndPoint.begin(), e = EndPoint.end(); 3199 it != e; ++it) 3200 TransposeEnds[it->second].push_back(it->first); 3201 3202 SmallSet<Instruction*, 8> OpenIntervals; 3203 unsigned MaxUsage = 0; 3204 3205 3206 DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n"); 3207 for (unsigned int i = 0; i < Index; ++i) { 3208 Instruction *I = IdxToInstr[i]; 3209 // Ignore instructions that are never used within the loop. 3210 if (!Ends.count(I)) continue; 3211 3212 // Remove all of the instructions that end at this location. 3213 InstrList &List = TransposeEnds[i]; 3214 for (unsigned int j=0, e = List.size(); j < e; ++j) 3215 OpenIntervals.erase(List[j]); 3216 3217 // Count the number of live interals. 3218 MaxUsage = std::max(MaxUsage, OpenIntervals.size()); 3219 3220 DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " << 3221 OpenIntervals.size() <<"\n"); 3222 3223 // Add the current instruction to the list of open intervals. 3224 OpenIntervals.insert(I); 3225 } 3226 3227 unsigned Invariant = LoopInvariants.size(); 3228 DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsage << " \n"); 3229 DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << " \n"); 3230 DEBUG(dbgs() << "LV(REG): LoopSize: " << R.NumInstructions << " \n"); 3231 3232 R.LoopInvariantRegs = Invariant; 3233 R.MaxLocalUsers = MaxUsage; 3234 return R; 3235} 3236 3237unsigned LoopVectorizationCostModel::expectedCost(unsigned VF) { 3238 unsigned Cost = 0; 3239 3240 // For each block. 3241 for (Loop::block_iterator bb = TheLoop->block_begin(), 3242 be = TheLoop->block_end(); bb != be; ++bb) { 3243 unsigned BlockCost = 0; 3244 BasicBlock *BB = *bb; 3245 3246 // For each instruction in the old loop. 3247 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 3248 unsigned C = getInstructionCost(it, VF); 3249 Cost += C; 3250 DEBUG(dbgs() << "LV: Found an estimated cost of "<< C <<" for VF " << 3251 VF << " For instruction: "<< *it << "\n"); 3252 } 3253 3254 // We assume that if-converted blocks have a 50% chance of being executed. 3255 // When the code is scalar then some of the blocks are avoided due to CF. 3256 // When the code is vectorized we execute all code paths. 3257 if (Legal->blockNeedsPredication(*bb) && VF == 1) 3258 BlockCost /= 2; 3259 3260 Cost += BlockCost; 3261 } 3262 3263 return Cost; 3264} 3265 3266unsigned 3267LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) { 3268 // If we know that this instruction will remain uniform, check the cost of 3269 // the scalar version. 3270 if (Legal->isUniformAfterVectorization(I)) 3271 VF = 1; 3272 3273 Type *RetTy = I->getType(); 3274 Type *VectorTy = ToVectorTy(RetTy, VF); 3275 3276 // TODO: We need to estimate the cost of intrinsic calls. 3277 switch (I->getOpcode()) { 3278 case Instruction::GetElementPtr: 3279 // We mark this instruction as zero-cost because the cost of GEPs in 3280 // vectorized code depends on whether the corresponding memory instruction 3281 // is scalarized or not. Therefore, we handle GEPs with the memory 3282 // instruction cost. 3283 return 0; 3284 case Instruction::Br: { 3285 return TTI.getCFInstrCost(I->getOpcode()); 3286 } 3287 case Instruction::PHI: 3288 //TODO: IF-converted IFs become selects. 3289 return 0; 3290 case Instruction::Add: 3291 case Instruction::FAdd: 3292 case Instruction::Sub: 3293 case Instruction::FSub: 3294 case Instruction::Mul: 3295 case Instruction::FMul: 3296 case Instruction::UDiv: 3297 case Instruction::SDiv: 3298 case Instruction::FDiv: 3299 case Instruction::URem: 3300 case Instruction::SRem: 3301 case Instruction::FRem: 3302 case Instruction::Shl: 3303 case Instruction::LShr: 3304 case Instruction::AShr: 3305 case Instruction::And: 3306 case Instruction::Or: 3307 case Instruction::Xor: 3308 return TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy); 3309 case Instruction::Select: { 3310 SelectInst *SI = cast<SelectInst>(I); 3311 const SCEV *CondSCEV = SE->getSCEV(SI->getCondition()); 3312 bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop)); 3313 Type *CondTy = SI->getCondition()->getType(); 3314 if (ScalarCond) 3315 CondTy = VectorType::get(CondTy, VF); 3316 3317 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy); 3318 } 3319 case Instruction::ICmp: 3320 case Instruction::FCmp: { 3321 Type *ValTy = I->getOperand(0)->getType(); 3322 VectorTy = ToVectorTy(ValTy, VF); 3323 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy); 3324 } 3325 case Instruction::Store: 3326 case Instruction::Load: { 3327 StoreInst *SI = dyn_cast<StoreInst>(I); 3328 LoadInst *LI = dyn_cast<LoadInst>(I); 3329 Type *ValTy = (SI ? SI->getValueOperand()->getType() : 3330 LI->getType()); 3331 VectorTy = ToVectorTy(ValTy, VF); 3332 3333 unsigned Alignment = SI ? SI->getAlignment() : LI->getAlignment(); 3334 unsigned AS = SI ? SI->getPointerAddressSpace() : 3335 LI->getPointerAddressSpace(); 3336 Value *Ptr = SI ? SI->getPointerOperand() : LI->getPointerOperand(); 3337 // We add the cost of address computation here instead of with the gep 3338 // instruction because only here we know whether the operation is 3339 // scalarized. 3340 if (VF == 1) 3341 return TTI.getAddressComputationCost(VectorTy) + 3342 TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS); 3343 3344 // Scalarized loads/stores. 3345 int Stride = Legal->isConsecutivePtr(Ptr); 3346 bool Reverse = Stride < 0; 3347 if (0 == Stride) { 3348 unsigned Cost = 0; 3349 // The cost of extracting from the value vector and pointer vector. 3350 Type *PtrTy = ToVectorTy(Ptr->getType(), VF); 3351 for (unsigned i = 0; i < VF; ++i) { 3352 // The cost of extracting the pointer operand. 3353 Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, PtrTy, i); 3354 // In case of STORE, the cost of ExtractElement from the vector. 3355 // In case of LOAD, the cost of InsertElement into the returned 3356 // vector. 3357 Cost += TTI.getVectorInstrCost(SI ? Instruction::ExtractElement : 3358 Instruction::InsertElement, 3359 VectorTy, i); 3360 } 3361 3362 // The cost of the scalar loads/stores. 3363 Cost += VF * TTI.getAddressComputationCost(ValTy->getScalarType()); 3364 Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), 3365 Alignment, AS); 3366 return Cost; 3367 } 3368 3369 // Wide load/stores. 3370 unsigned Cost = TTI.getAddressComputationCost(VectorTy); 3371 Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS); 3372 3373 if (Reverse) 3374 Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, 3375 VectorTy, 0); 3376 return Cost; 3377 } 3378 case Instruction::ZExt: 3379 case Instruction::SExt: 3380 case Instruction::FPToUI: 3381 case Instruction::FPToSI: 3382 case Instruction::FPExt: 3383 case Instruction::PtrToInt: 3384 case Instruction::IntToPtr: 3385 case Instruction::SIToFP: 3386 case Instruction::UIToFP: 3387 case Instruction::Trunc: 3388 case Instruction::FPTrunc: 3389 case Instruction::BitCast: { 3390 // We optimize the truncation of induction variable. 3391 // The cost of these is the same as the scalar operation. 3392 if (I->getOpcode() == Instruction::Trunc && 3393 Legal->isInductionVariable(I->getOperand(0))) 3394 return TTI.getCastInstrCost(I->getOpcode(), I->getType(), 3395 I->getOperand(0)->getType()); 3396 3397 Type *SrcVecTy = ToVectorTy(I->getOperand(0)->getType(), VF); 3398 return TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy); 3399 } 3400 case Instruction::Call: { 3401 CallInst *CI = cast<CallInst>(I); 3402 Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI); 3403 assert(ID && "Not an intrinsic call!"); 3404 Type *RetTy = ToVectorTy(CI->getType(), VF); 3405 SmallVector<Type*, 4> Tys; 3406 for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) 3407 Tys.push_back(ToVectorTy(CI->getArgOperand(i)->getType(), VF)); 3408 return TTI.getIntrinsicInstrCost(ID, RetTy, Tys); 3409 } 3410 default: { 3411 // We are scalarizing the instruction. Return the cost of the scalar 3412 // instruction, plus the cost of insert and extract into vector 3413 // elements, times the vector width. 3414 unsigned Cost = 0; 3415 3416 if (!RetTy->isVoidTy() && VF != 1) { 3417 unsigned InsCost = TTI.getVectorInstrCost(Instruction::InsertElement, 3418 VectorTy); 3419 unsigned ExtCost = TTI.getVectorInstrCost(Instruction::ExtractElement, 3420 VectorTy); 3421 3422 // The cost of inserting the results plus extracting each one of the 3423 // operands. 3424 Cost += VF * (InsCost + ExtCost * I->getNumOperands()); 3425 } 3426 3427 // The cost of executing VF copies of the scalar instruction. This opcode 3428 // is unknown. Assume that it is the same as 'mul'. 3429 Cost += VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy); 3430 return Cost; 3431 } 3432 }// end of switch. 3433} 3434 3435Type* LoopVectorizationCostModel::ToVectorTy(Type *Scalar, unsigned VF) { 3436 if (Scalar->isVoidTy() || VF == 1) 3437 return Scalar; 3438 return VectorType::get(Scalar, VF); 3439} 3440 3441char LoopVectorize::ID = 0; 3442static const char lv_name[] = "Loop Vectorization"; 3443INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false) 3444INITIALIZE_AG_DEPENDENCY(AliasAnalysis) 3445INITIALIZE_AG_DEPENDENCY(TargetTransformInfo) 3446INITIALIZE_PASS_DEPENDENCY(ScalarEvolution) 3447INITIALIZE_PASS_DEPENDENCY(LoopSimplify) 3448INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false) 3449 3450namespace llvm { 3451 Pass *createLoopVectorizePass() { 3452 return new LoopVectorize(); 3453 } 3454} 3455 3456bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) { 3457 // Check for a store. 3458 if (StoreInst *ST = dyn_cast<StoreInst>(Inst)) 3459 return Legal->isConsecutivePtr(ST->getPointerOperand()) != 0; 3460 3461 // Check for a load. 3462 if (LoadInst *LI = dyn_cast<LoadInst>(Inst)) 3463 return Legal->isConsecutivePtr(LI->getPointerOperand()) != 0; 3464 3465 return false; 3466} 3467