LoopVectorize.cpp revision 111e5fe7e089e0ffe73873848315ea5358120dfa
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 vectorizes 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 iteration 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/Transforms/Scalar.h" 83#include "llvm/Transforms/Utils/BasicBlockUtils.h" 84#include "llvm/Transforms/Utils/Local.h" 85#include <algorithm> 86#include <map> 87 88using namespace llvm; 89 90static cl::opt<unsigned> 91VectorizationFactor("force-vector-width", cl::init(0), cl::Hidden, 92 cl::desc("Sets the SIMD width. Zero is autoselect.")); 93 94static cl::opt<unsigned> 95VectorizationUnroll("force-vector-unroll", cl::init(0), cl::Hidden, 96 cl::desc("Sets the vectorization unroll count. " 97 "Zero is autoselect.")); 98 99static cl::opt<bool> 100EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden, 101 cl::desc("Enable if-conversion during vectorization.")); 102 103/// We don't vectorize loops with a known constant trip count below this number. 104static const unsigned TinyTripCountVectorThreshold = 16; 105 106/// We don't unroll loops with a known constant trip count below this number. 107static const unsigned TinyTripCountUnrollThreshold = 128; 108 109/// We don't unroll loops that are larget than this threshold. 110static const unsigned MaxLoopSizeThreshold = 32; 111 112/// When performing a runtime memory check, do not check more than this 113/// number of pointers. Notice that the check is quadratic! 114static const unsigned RuntimeMemoryCheckThreshold = 4; 115 116/// This is the highest vector width that we try to generate. 117static const unsigned MaxVectorSize = 8; 118 119/// This is the highest Unroll Factor. 120static const unsigned MaxUnrollSize = 4; 121 122namespace { 123 124// Forward declarations. 125class LoopVectorizationLegality; 126class LoopVectorizationCostModel; 127 128/// InnerLoopVectorizer vectorizes loops which contain only one basic 129/// block to a specified vectorization factor (VF). 130/// This class performs the widening of scalars into vectors, or multiple 131/// scalars. This class also implements the following features: 132/// * It inserts an epilogue loop for handling loops that don't have iteration 133/// counts that are known to be a multiple of the vectorization factor. 134/// * It handles the code generation for reduction variables. 135/// * Scalarization (implementation using scalars) of un-vectorizable 136/// instructions. 137/// InnerLoopVectorizer does not perform any vectorization-legality 138/// checks, and relies on the caller to check for the different legality 139/// aspects. The InnerLoopVectorizer relies on the 140/// LoopVectorizationLegality class to provide information about the induction 141/// and reduction variables that were found to a given vectorization factor. 142class InnerLoopVectorizer { 143public: 144 InnerLoopVectorizer(Loop *OrigLoop, ScalarEvolution *SE, LoopInfo *LI, 145 DominatorTree *DT, DataLayout *DL, unsigned VecWidth, 146 unsigned UnrollFactor) 147 : OrigLoop(OrigLoop), SE(SE), LI(LI), DT(DT), DL(DL), VF(VecWidth), 148 UF(UnrollFactor), Builder(SE->getContext()), Induction(0), 149 OldInduction(0), WidenMap(UnrollFactor) {} 150 151 // Perform the actual loop widening (vectorization). 152 void vectorize(LoopVectorizationLegality *Legal) { 153 // Create a new empty loop. Unlink the old loop and connect the new one. 154 createEmptyLoop(Legal); 155 // Widen each instruction in the old loop to a new one in the new loop. 156 // Use the Legality module to find the induction and reduction variables. 157 vectorizeLoop(Legal); 158 // Register the new loop and update the analysis passes. 159 updateAnalysis(); 160 } 161 162private: 163 /// A small list of PHINodes. 164 typedef SmallVector<PHINode*, 4> PhiVector; 165 /// When we unroll loops we have multiple vector values for each scalar. 166 /// This data structure holds the unrolled and vectorized values that 167 /// originated from one scalar instruction. 168 typedef SmallVector<Value*, 2> VectorParts; 169 170 /// Add code that checks at runtime if the accessed arrays overlap. 171 /// Returns the comparator value or NULL if no check is needed. 172 Value *addRuntimeCheck(LoopVectorizationLegality *Legal, 173 Instruction *Loc); 174 /// Create an empty loop, based on the loop ranges of the old loop. 175 void createEmptyLoop(LoopVectorizationLegality *Legal); 176 /// Copy and widen the instructions from the old loop. 177 void vectorizeLoop(LoopVectorizationLegality *Legal); 178 179 /// A helper function that computes the predicate of the block BB, assuming 180 /// that the header block of the loop is set to True. It returns the *entry* 181 /// mask for the block BB. 182 VectorParts createBlockInMask(BasicBlock *BB); 183 /// A helper function that computes the predicate of the edge between SRC 184 /// and DST. 185 VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst); 186 187 /// A helper function to vectorize a single BB within the innermost loop. 188 void vectorizeBlockInLoop(LoopVectorizationLegality *Legal, BasicBlock *BB, 189 PhiVector *PV); 190 191 /// Insert the new loop to the loop hierarchy and pass manager 192 /// and update the analysis passes. 193 void updateAnalysis(); 194 195 /// This instruction is un-vectorizable. Implement it as a sequence 196 /// of scalars. 197 void scalarizeInstruction(Instruction *Instr); 198 199 /// Create a broadcast instruction. This method generates a broadcast 200 /// instruction (shuffle) for loop invariant values and for the induction 201 /// value. If this is the induction variable then we extend it to N, N+1, ... 202 /// this is needed because each iteration in the loop corresponds to a SIMD 203 /// element. 204 Value *getBroadcastInstrs(Value *V); 205 206 /// This function adds 0, 1, 2 ... to each vector element, starting at zero. 207 /// If Negate is set then negative numbers are added e.g. (0, -1, -2, ...). 208 /// The sequence starts at StartIndex. 209 Value *getConsecutiveVector(Value* Val, unsigned StartIdx, bool Negate); 210 211 /// When we go over instructions in the basic block we rely on previous 212 /// values within the current basic block or on loop invariant values. 213 /// When we widen (vectorize) values we place them in the map. If the values 214 /// are not within the map, they have to be loop invariant, so we simply 215 /// broadcast them into a vector. 216 VectorParts &getVectorValue(Value *V); 217 218 /// Generate a shuffle sequence that will reverse the vector Vec. 219 Value *reverseVector(Value *Vec); 220 221 /// This is a helper class that holds the vectorizer state. It maps scalar 222 /// instructions to vector instructions. When the code is 'unrolled' then 223 /// then a single scalar value is mapped to multiple vector parts. The parts 224 /// are stored in the VectorPart type. 225 struct ValueMap { 226 /// C'tor. UnrollFactor controls the number of vectors ('parts') that 227 /// are mapped. 228 ValueMap(unsigned UnrollFactor) : UF(UnrollFactor) {} 229 230 /// \return True if 'Key' is saved in the Value Map. 231 bool has(Value *Key) { return MapStoreage.count(Key); } 232 233 /// Initializes a new entry in the map. Sets all of the vector parts to the 234 /// save value in 'Val'. 235 /// \return A reference to a vector with splat values. 236 VectorParts &splat(Value *Key, Value *Val) { 237 MapStoreage[Key].clear(); 238 MapStoreage[Key].append(UF, Val); 239 return MapStoreage[Key]; 240 } 241 242 ///\return A reference to the value that is stored at 'Key'. 243 VectorParts &get(Value *Key) { 244 if (!has(Key)) 245 MapStoreage[Key].resize(UF); 246 return MapStoreage[Key]; 247 } 248 249 /// The unroll factor. Each entry in the map stores this number of vector 250 /// elements. 251 unsigned UF; 252 253 /// Map storage. We use std::map and not DenseMap because insertions to a 254 /// dense map invalidates its iterators. 255 std::map<Value*, VectorParts> MapStoreage; 256 }; 257 258 /// The original loop. 259 Loop *OrigLoop; 260 /// Scev analysis to use. 261 ScalarEvolution *SE; 262 /// Loop Info. 263 LoopInfo *LI; 264 /// Dominator Tree. 265 DominatorTree *DT; 266 /// Data Layout. 267 DataLayout *DL; 268 /// The vectorization SIMD factor to use. Each vector will have this many 269 /// vector elements. 270 unsigned VF; 271 /// The vectorization unroll factor to use. Each scalar is vectorized to this 272 /// many different vector instructions. 273 unsigned UF; 274 275 /// The builder that we use 276 IRBuilder<> Builder; 277 278 // --- Vectorization state --- 279 280 /// The vector-loop preheader. 281 BasicBlock *LoopVectorPreHeader; 282 /// The scalar-loop preheader. 283 BasicBlock *LoopScalarPreHeader; 284 /// Middle Block between the vector and the scalar. 285 BasicBlock *LoopMiddleBlock; 286 ///The ExitBlock of the scalar loop. 287 BasicBlock *LoopExitBlock; 288 ///The vector loop body. 289 BasicBlock *LoopVectorBody; 290 ///The scalar loop body. 291 BasicBlock *LoopScalarBody; 292 ///The first bypass block. 293 BasicBlock *LoopBypassBlock; 294 295 /// The new Induction variable which was added to the new block. 296 PHINode *Induction; 297 /// The induction variable of the old basic block. 298 PHINode *OldInduction; 299 /// Maps scalars to widened vectors. 300 ValueMap WidenMap; 301}; 302 303/// LoopVectorizationLegality checks if it is legal to vectorize a loop, and 304/// to what vectorization factor. 305/// This class does not look at the profitability of vectorization, only the 306/// legality. This class has two main kinds of checks: 307/// * Memory checks - The code in canVectorizeMemory checks if vectorization 308/// will change the order of memory accesses in a way that will change the 309/// correctness of the program. 310/// * Scalars checks - The code in canVectorizeInstrs and canVectorizeMemory 311/// checks for a number of different conditions, such as the availability of a 312/// single induction variable, that all types are supported and vectorize-able, 313/// etc. This code reflects the capabilities of InnerLoopVectorizer. 314/// This class is also used by InnerLoopVectorizer for identifying 315/// induction variable and the different reduction variables. 316class LoopVectorizationLegality { 317public: 318 LoopVectorizationLegality(Loop *L, ScalarEvolution *SE, DataLayout *DL, 319 DominatorTree *DT) 320 : TheLoop(L), SE(SE), DL(DL), DT(DT), Induction(0) {} 321 322 /// This enum represents the kinds of reductions that we support. 323 enum ReductionKind { 324 RK_NoReduction, ///< Not a reduction. 325 RK_IntegerAdd, ///< Sum of integers. 326 RK_IntegerMult, ///< Product of integers. 327 RK_IntegerOr, ///< Bitwise or logical OR of numbers. 328 RK_IntegerAnd, ///< Bitwise or logical AND of numbers. 329 RK_IntegerXor, ///< Bitwise or logical XOR of numbers. 330 RK_FloatAdd, ///< Sum of floats. 331 RK_FloatMult ///< Product of floats. 332 }; 333 334 /// This enum represents the kinds of inductions that we support. 335 enum InductionKind { 336 NoInduction, ///< Not an induction variable. 337 IntInduction, ///< Integer induction variable. Step = 1. 338 ReverseIntInduction, ///< Reverse int induction variable. Step = -1. 339 PtrInduction ///< Pointer induction variable. Step = sizeof(elem). 340 }; 341 342 /// This POD struct holds information about reduction variables. 343 struct ReductionDescriptor { 344 ReductionDescriptor() : StartValue(0), LoopExitInstr(0), 345 Kind(RK_NoReduction) {} 346 347 ReductionDescriptor(Value *Start, Instruction *Exit, ReductionKind K) 348 : StartValue(Start), LoopExitInstr(Exit), Kind(K) {} 349 350 // The starting value of the reduction. 351 // It does not have to be zero! 352 Value *StartValue; 353 // The instruction who's value is used outside the loop. 354 Instruction *LoopExitInstr; 355 // The kind of the reduction. 356 ReductionKind Kind; 357 }; 358 359 // This POD struct holds information about the memory runtime legality 360 // check that a group of pointers do not overlap. 361 struct RuntimePointerCheck { 362 RuntimePointerCheck() : Need(false) {} 363 364 /// Reset the state of the pointer runtime information. 365 void reset() { 366 Need = false; 367 Pointers.clear(); 368 Starts.clear(); 369 Ends.clear(); 370 } 371 372 /// Insert a pointer and calculate the start and end SCEVs. 373 void insert(ScalarEvolution *SE, Loop *Lp, Value *Ptr); 374 375 /// This flag indicates if we need to add the runtime check. 376 bool Need; 377 /// Holds the pointers that we need to check. 378 SmallVector<Value*, 2> Pointers; 379 /// Holds the pointer value at the beginning of the loop. 380 SmallVector<const SCEV*, 2> Starts; 381 /// Holds the pointer value at the end of the loop. 382 SmallVector<const SCEV*, 2> Ends; 383 }; 384 385 /// A POD for saving information about induction variables. 386 struct InductionInfo { 387 InductionInfo(Value *Start, InductionKind K) : StartValue(Start), IK(K) {} 388 InductionInfo() : StartValue(0), IK(NoInduction) {} 389 /// Start value. 390 Value *StartValue; 391 /// Induction kind. 392 InductionKind IK; 393 }; 394 395 /// ReductionList contains the reduction descriptors for all 396 /// of the reductions that were found in the loop. 397 typedef DenseMap<PHINode*, ReductionDescriptor> ReductionList; 398 399 /// InductionList saves induction variables and maps them to the 400 /// induction descriptor. 401 typedef MapVector<PHINode*, InductionInfo> InductionList; 402 403 /// Returns true if it is legal to vectorize this loop. 404 /// This does not mean that it is profitable to vectorize this 405 /// loop, only that it is legal to do so. 406 bool canVectorize(); 407 408 /// Returns the Induction variable. 409 PHINode *getInduction() { return Induction; } 410 411 /// Returns the reduction variables found in the loop. 412 ReductionList *getReductionVars() { return &Reductions; } 413 414 /// Returns the induction variables found in the loop. 415 InductionList *getInductionVars() { return &Inductions; } 416 417 /// Returns True if V is an induction variable in this loop. 418 bool isInductionVariable(const Value *V); 419 420 /// Return true if the block BB needs to be predicated in order for the loop 421 /// to be vectorized. 422 bool blockNeedsPredication(BasicBlock *BB); 423 424 /// Check if this pointer is consecutive when vectorizing. This happens 425 /// when the last index of the GEP is the induction variable, or that the 426 /// pointer itself is an induction variable. 427 /// This check allows us to vectorize A[idx] into a wide load/store. 428 /// Returns: 429 /// 0 - Stride is unknown or non consecutive. 430 /// 1 - Address is consecutive. 431 /// -1 - Address is consecutive, and decreasing. 432 int isConsecutivePtr(Value *Ptr); 433 434 /// Returns true if the value V is uniform within the loop. 435 bool isUniform(Value *V); 436 437 /// Returns true if this instruction will remain scalar after vectorization. 438 bool isUniformAfterVectorization(Instruction* I) { return Uniforms.count(I); } 439 440 /// Returns the information that we collected about runtime memory check. 441 RuntimePointerCheck *getRuntimePointerCheck() { return &PtrRtCheck; } 442private: 443 /// Check if a single basic block loop is vectorizable. 444 /// At this point we know that this is a loop with a constant trip count 445 /// and we only need to check individual instructions. 446 bool canVectorizeInstrs(); 447 448 /// When we vectorize loops we may change the order in which 449 /// we read and write from memory. This method checks if it is 450 /// legal to vectorize the code, considering only memory constrains. 451 /// Returns true if the loop is vectorizable 452 bool canVectorizeMemory(); 453 454 /// Return true if we can vectorize this loop using the IF-conversion 455 /// transformation. 456 bool canVectorizeWithIfConvert(); 457 458 /// Collect the variables that need to stay uniform after vectorization. 459 void collectLoopUniforms(); 460 461 /// Return true if all of the instructions in the block can be speculatively 462 /// executed. 463 bool blockCanBePredicated(BasicBlock *BB); 464 465 /// Returns True, if 'Phi' is the kind of reduction variable for type 466 /// 'Kind'. If this is a reduction variable, it adds it to ReductionList. 467 bool AddReductionVar(PHINode *Phi, ReductionKind Kind); 468 /// Returns true if the instruction I can be a reduction variable of type 469 /// 'Kind'. 470 bool isReductionInstr(Instruction *I, ReductionKind Kind); 471 /// Returns the induction kind of Phi. This function may return NoInduction 472 /// if the PHI is not an induction variable. 473 InductionKind isInductionVariable(PHINode *Phi); 474 /// Return true if can compute the address bounds of Ptr within the loop. 475 bool hasComputableBounds(Value *Ptr); 476 477 /// The loop that we evaluate. 478 Loop *TheLoop; 479 /// Scev analysis. 480 ScalarEvolution *SE; 481 /// DataLayout analysis. 482 DataLayout *DL; 483 // Dominators. 484 DominatorTree *DT; 485 486 // --- vectorization state --- // 487 488 /// Holds the integer induction variable. This is the counter of the 489 /// loop. 490 PHINode *Induction; 491 /// Holds the reduction variables. 492 ReductionList Reductions; 493 /// Holds all of the induction variables that we found in the loop. 494 /// Notice that inductions don't need to start at zero and that induction 495 /// variables can be pointers. 496 InductionList Inductions; 497 498 /// Allowed outside users. This holds the reduction 499 /// vars which can be accessed from outside the loop. 500 SmallPtrSet<Value*, 4> AllowedExit; 501 /// This set holds the variables which are known to be uniform after 502 /// vectorization. 503 SmallPtrSet<Instruction*, 4> Uniforms; 504 /// We need to check that all of the pointers in this list are disjoint 505 /// at runtime. 506 RuntimePointerCheck PtrRtCheck; 507}; 508 509/// LoopVectorizationCostModel - estimates the expected speedups due to 510/// vectorization. 511/// In many cases vectorization is not profitable. This can happen because of 512/// a number of reasons. In this class we mainly attempt to predict the 513/// expected speedup/slowdowns due to the supported instruction set. We use the 514/// TargetTransformInfo to query the different backends for the cost of 515/// different operations. 516class LoopVectorizationCostModel { 517public: 518 LoopVectorizationCostModel(Loop *L, ScalarEvolution *SE, LoopInfo *LI, 519 LoopVectorizationLegality *Legal, 520 const TargetTransformInfo &TTI) 521 : TheLoop(L), SE(SE), LI(LI), Legal(Legal), TTI(TTI) {} 522 523 /// \return The most profitable vectorization factor. 524 /// This method checks every power of two up to VF. If UserVF is not ZERO 525 /// then this vectorization factor will be selected if vectorization is 526 /// possible. 527 unsigned selectVectorizationFactor(bool OptForSize, unsigned UserVF); 528 529 530 /// \return The most profitable unroll factor. 531 /// If UserUF is non-zero then this method finds the best unroll-factor 532 /// based on register pressure and other parameters. 533 unsigned selectUnrollFactor(bool OptForSize, unsigned UserUF); 534 535 /// \brief A struct that represents some properties of the register usage 536 /// of a loop. 537 struct RegisterUsage { 538 /// Holds the number of loop invariant values that are used in the loop. 539 unsigned LoopInvariantRegs; 540 /// Holds the maximum number of concurrent live intervals in the loop. 541 unsigned MaxLocalUsers; 542 /// Holds the number of instructions in the loop. 543 unsigned NumInstructions; 544 }; 545 546 /// \return information about the register usage of the loop. 547 RegisterUsage calculateRegisterUsage(); 548 549private: 550 /// Returns the expected execution cost. The unit of the cost does 551 /// not matter because we use the 'cost' units to compare different 552 /// vector widths. The cost that is returned is *not* normalized by 553 /// the factor width. 554 unsigned expectedCost(unsigned VF); 555 556 /// Returns the execution time cost of an instruction for a given vector 557 /// width. Vector width of one means scalar. 558 unsigned getInstructionCost(Instruction *I, unsigned VF); 559 560 /// A helper function for converting Scalar types to vector types. 561 /// If the incoming type is void, we return void. If the VF is 1, we return 562 /// the scalar type. 563 static Type* ToVectorTy(Type *Scalar, unsigned VF); 564 565 /// The loop that we evaluate. 566 Loop *TheLoop; 567 /// Scev analysis. 568 ScalarEvolution *SE; 569 /// Loop Info analysis. 570 LoopInfo *LI; 571 /// Vectorization legality. 572 LoopVectorizationLegality *Legal; 573 /// Vector target information. 574 const TargetTransformInfo &TTI; 575}; 576 577/// The LoopVectorize Pass. 578struct LoopVectorize : public LoopPass { 579 /// Pass identification, replacement for typeid 580 static char ID; 581 582 explicit LoopVectorize() : LoopPass(ID) { 583 initializeLoopVectorizePass(*PassRegistry::getPassRegistry()); 584 } 585 586 ScalarEvolution *SE; 587 DataLayout *DL; 588 LoopInfo *LI; 589 TargetTransformInfo *TTI; 590 DominatorTree *DT; 591 592 virtual bool runOnLoop(Loop *L, LPPassManager &LPM) { 593 // We only vectorize innermost loops. 594 if (!L->empty()) 595 return false; 596 597 SE = &getAnalysis<ScalarEvolution>(); 598 DL = getAnalysisIfAvailable<DataLayout>(); 599 LI = &getAnalysis<LoopInfo>(); 600 TTI = &getAnalysis<TargetTransformInfo>(); 601 DT = &getAnalysis<DominatorTree>(); 602 603 DEBUG(dbgs() << "LV: Checking a loop in \"" << 604 L->getHeader()->getParent()->getName() << "\"\n"); 605 606 // Check if it is legal to vectorize the loop. 607 LoopVectorizationLegality LVL(L, SE, DL, DT); 608 if (!LVL.canVectorize()) { 609 DEBUG(dbgs() << "LV: Not vectorizing.\n"); 610 return false; 611 } 612 613 // Use the cost model. 614 LoopVectorizationCostModel CM(L, SE, LI, &LVL, *TTI); 615 616 // Check the function attribues to find out if this function should be 617 // optimized for size. 618 Function *F = L->getHeader()->getParent(); 619 Attribute::AttrKind SzAttr = Attribute::OptimizeForSize; 620 Attribute::AttrKind FlAttr = Attribute::NoImplicitFloat; 621 unsigned FnIndex = AttributeSet::FunctionIndex; 622 bool OptForSize = F->getAttributes().hasAttribute(FnIndex, SzAttr); 623 bool NoFloat = F->getAttributes().hasAttribute(FnIndex, FlAttr); 624 625 if (NoFloat) { 626 DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat" 627 "attribute is used.\n"); 628 return false; 629 } 630 631 unsigned VF = CM.selectVectorizationFactor(OptForSize, VectorizationFactor); 632 unsigned UF = CM.selectUnrollFactor(OptForSize, VectorizationUnroll); 633 634 if (VF == 1) { 635 DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n"); 636 return false; 637 } 638 639 DEBUG(dbgs() << "LV: Found a vectorizable loop ("<< VF << ") in "<< 640 F->getParent()->getModuleIdentifier()<<"\n"); 641 DEBUG(dbgs() << "LV: Unroll Factor is " << UF << "\n"); 642 643 // If we decided that it is *legal* to vectorizer the loop then do it. 644 InnerLoopVectorizer LB(L, SE, LI, DT, DL, VF, UF); 645 LB.vectorize(&LVL); 646 647 DEBUG(verifyFunction(*L->getHeader()->getParent())); 648 return true; 649 } 650 651 virtual void getAnalysisUsage(AnalysisUsage &AU) const { 652 LoopPass::getAnalysisUsage(AU); 653 AU.addRequiredID(LoopSimplifyID); 654 AU.addRequiredID(LCSSAID); 655 AU.addRequired<DominatorTree>(); 656 AU.addRequired<LoopInfo>(); 657 AU.addRequired<ScalarEvolution>(); 658 AU.addRequired<TargetTransformInfo>(); 659 AU.addPreserved<LoopInfo>(); 660 AU.addPreserved<DominatorTree>(); 661 } 662 663}; 664 665} // end anonymous namespace 666 667//===----------------------------------------------------------------------===// 668// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and 669// LoopVectorizationCostModel. 670//===----------------------------------------------------------------------===// 671 672void 673LoopVectorizationLegality::RuntimePointerCheck::insert(ScalarEvolution *SE, 674 Loop *Lp, Value *Ptr) { 675 const SCEV *Sc = SE->getSCEV(Ptr); 676 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Sc); 677 assert(AR && "Invalid addrec expression"); 678 const SCEV *Ex = SE->getExitCount(Lp, Lp->getLoopLatch()); 679 const SCEV *ScEnd = AR->evaluateAtIteration(Ex, *SE); 680 Pointers.push_back(Ptr); 681 Starts.push_back(AR->getStart()); 682 Ends.push_back(ScEnd); 683} 684 685Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) { 686 // Save the current insertion location. 687 Instruction *Loc = Builder.GetInsertPoint(); 688 689 // We need to place the broadcast of invariant variables outside the loop. 690 Instruction *Instr = dyn_cast<Instruction>(V); 691 bool NewInstr = (Instr && Instr->getParent() == LoopVectorBody); 692 bool Invariant = OrigLoop->isLoopInvariant(V) && !NewInstr; 693 694 // Place the code for broadcasting invariant variables in the new preheader. 695 if (Invariant) 696 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); 697 698 // Broadcast the scalar into all locations in the vector. 699 Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast"); 700 701 // Restore the builder insertion point. 702 if (Invariant) 703 Builder.SetInsertPoint(Loc); 704 705 return Shuf; 706} 707 708Value *InnerLoopVectorizer::getConsecutiveVector(Value* Val, unsigned StartIdx, 709 bool Negate) { 710 assert(Val->getType()->isVectorTy() && "Must be a vector"); 711 assert(Val->getType()->getScalarType()->isIntegerTy() && 712 "Elem must be an integer"); 713 // Create the types. 714 Type *ITy = Val->getType()->getScalarType(); 715 VectorType *Ty = cast<VectorType>(Val->getType()); 716 int VLen = Ty->getNumElements(); 717 SmallVector<Constant*, 8> Indices; 718 719 // Create a vector of consecutive numbers from zero to VF. 720 for (int i = 0; i < VLen; ++i) { 721 int Idx = Negate ? (-i): i; 722 Indices.push_back(ConstantInt::get(ITy, StartIdx + Idx)); 723 } 724 725 // Add the consecutive indices to the vector value. 726 Constant *Cv = ConstantVector::get(Indices); 727 assert(Cv->getType() == Val->getType() && "Invalid consecutive vec"); 728 return Builder.CreateAdd(Val, Cv, "induction"); 729} 730 731int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) { 732 assert(Ptr->getType()->isPointerTy() && "Unexpected non ptr"); 733 734 // If this value is a pointer induction variable we know it is consecutive. 735 PHINode *Phi = dyn_cast_or_null<PHINode>(Ptr); 736 if (Phi && Inductions.count(Phi)) { 737 InductionInfo II = Inductions[Phi]; 738 if (PtrInduction == II.IK) 739 return 1; 740 } 741 742 GetElementPtrInst *Gep = dyn_cast_or_null<GetElementPtrInst>(Ptr); 743 if (!Gep) 744 return 0; 745 746 unsigned NumOperands = Gep->getNumOperands(); 747 Value *LastIndex = Gep->getOperand(NumOperands - 1); 748 749 // Check that all of the gep indices are uniform except for the last. 750 for (unsigned i = 0; i < NumOperands - 1; ++i) 751 if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop)) 752 return 0; 753 754 // We can emit wide load/stores only if the last index is the induction 755 // variable. 756 const SCEV *Last = SE->getSCEV(LastIndex); 757 if (const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Last)) { 758 const SCEV *Step = AR->getStepRecurrence(*SE); 759 760 // The memory is consecutive because the last index is consecutive 761 // and all other indices are loop invariant. 762 if (Step->isOne()) 763 return 1; 764 if (Step->isAllOnesValue()) 765 return -1; 766 } 767 768 return 0; 769} 770 771bool LoopVectorizationLegality::isUniform(Value *V) { 772 return (SE->isLoopInvariant(SE->getSCEV(V), TheLoop)); 773} 774 775InnerLoopVectorizer::VectorParts& 776InnerLoopVectorizer::getVectorValue(Value *V) { 777 assert(V != Induction && "The new induction variable should not be used."); 778 assert(!V->getType()->isVectorTy() && "Can't widen a vector"); 779 780 // If we have this scalar in the map, return it. 781 if (WidenMap.has(V)) 782 return WidenMap.get(V); 783 784 // If this scalar is unknown, assume that it is a constant or that it is 785 // loop invariant. Broadcast V and save the value for future uses. 786 Value *B = getBroadcastInstrs(V); 787 WidenMap.splat(V, B); 788 return WidenMap.get(V); 789} 790 791Value *InnerLoopVectorizer::reverseVector(Value *Vec) { 792 assert(Vec->getType()->isVectorTy() && "Invalid type"); 793 SmallVector<Constant*, 8> ShuffleMask; 794 for (unsigned i = 0; i < VF; ++i) 795 ShuffleMask.push_back(Builder.getInt32(VF - i - 1)); 796 797 return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()), 798 ConstantVector::get(ShuffleMask), 799 "reverse"); 800} 801 802void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr) { 803 assert(!Instr->getType()->isAggregateType() && "Can't handle vectors"); 804 // Holds vector parameters or scalars, in case of uniform vals. 805 SmallVector<VectorParts, 4> Params; 806 807 // Find all of the vectorized parameters. 808 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) { 809 Value *SrcOp = Instr->getOperand(op); 810 811 // If we are accessing the old induction variable, use the new one. 812 if (SrcOp == OldInduction) { 813 Params.push_back(getVectorValue(SrcOp)); 814 continue; 815 } 816 817 // Try using previously calculated values. 818 Instruction *SrcInst = dyn_cast<Instruction>(SrcOp); 819 820 // If the src is an instruction that appeared earlier in the basic block 821 // then it should already be vectorized. 822 if (SrcInst && OrigLoop->contains(SrcInst)) { 823 assert(WidenMap.has(SrcInst) && "Source operand is unavailable"); 824 // The parameter is a vector value from earlier. 825 Params.push_back(WidenMap.get(SrcInst)); 826 } else { 827 // The parameter is a scalar from outside the loop. Maybe even a constant. 828 VectorParts Scalars; 829 Scalars.append(UF, SrcOp); 830 Params.push_back(Scalars); 831 } 832 } 833 834 assert(Params.size() == Instr->getNumOperands() && 835 "Invalid number of operands"); 836 837 // Does this instruction return a value ? 838 bool IsVoidRetTy = Instr->getType()->isVoidTy(); 839 840 Value *UndefVec = IsVoidRetTy ? 0 : 841 UndefValue::get(VectorType::get(Instr->getType(), VF)); 842 // Create a new entry in the WidenMap and initialize it to Undef or Null. 843 VectorParts &VecResults = WidenMap.splat(Instr, UndefVec); 844 845 // For each scalar that we create: 846 for (unsigned Width = 0; Width < VF; ++Width) { 847 // For each vector unroll 'part': 848 for (unsigned Part = 0; Part < UF; ++Part) { 849 Instruction *Cloned = Instr->clone(); 850 if (!IsVoidRetTy) 851 Cloned->setName(Instr->getName() + ".cloned"); 852 // Replace the operands of the cloned instrucions with extracted scalars. 853 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) { 854 Value *Op = Params[op][Part]; 855 // Param is a vector. Need to extract the right lane. 856 if (Op->getType()->isVectorTy()) 857 Op = Builder.CreateExtractElement(Op, Builder.getInt32(Width)); 858 Cloned->setOperand(op, Op); 859 } 860 861 // Place the cloned scalar in the new loop. 862 Builder.Insert(Cloned); 863 864 // If the original scalar returns a value we need to place it in a vector 865 // so that future users will be able to use it. 866 if (!IsVoidRetTy) 867 VecResults[Part] = Builder.CreateInsertElement(VecResults[Part], Cloned, 868 Builder.getInt32(Width)); 869 } 870 } 871} 872 873Value* 874InnerLoopVectorizer::addRuntimeCheck(LoopVectorizationLegality *Legal, 875 Instruction *Loc) { 876 LoopVectorizationLegality::RuntimePointerCheck *PtrRtCheck = 877 Legal->getRuntimePointerCheck(); 878 879 if (!PtrRtCheck->Need) 880 return NULL; 881 882 Value *MemoryRuntimeCheck = 0; 883 unsigned NumPointers = PtrRtCheck->Pointers.size(); 884 SmallVector<Value* , 2> Starts; 885 SmallVector<Value* , 2> Ends; 886 887 SCEVExpander Exp(*SE, "induction"); 888 889 // Use this type for pointer arithmetic. 890 Type* PtrArithTy = Type::getInt8PtrTy(Loc->getContext(), 0); 891 892 for (unsigned i = 0; i < NumPointers; ++i) { 893 Value *Ptr = PtrRtCheck->Pointers[i]; 894 const SCEV *Sc = SE->getSCEV(Ptr); 895 896 if (SE->isLoopInvariant(Sc, OrigLoop)) { 897 DEBUG(dbgs() << "LV: Adding RT check for a loop invariant ptr:" << 898 *Ptr <<"\n"); 899 Starts.push_back(Ptr); 900 Ends.push_back(Ptr); 901 } else { 902 DEBUG(dbgs() << "LV: Adding RT check for range:" << *Ptr <<"\n"); 903 904 Value *Start = Exp.expandCodeFor(PtrRtCheck->Starts[i], PtrArithTy, Loc); 905 Value *End = Exp.expandCodeFor(PtrRtCheck->Ends[i], PtrArithTy, Loc); 906 Starts.push_back(Start); 907 Ends.push_back(End); 908 } 909 } 910 911 for (unsigned i = 0; i < NumPointers; ++i) { 912 for (unsigned j = i+1; j < NumPointers; ++j) { 913 Instruction::CastOps Op = Instruction::BitCast; 914 Value *Start0 = CastInst::Create(Op, Starts[i], PtrArithTy, "bc", Loc); 915 Value *Start1 = CastInst::Create(Op, Starts[j], PtrArithTy, "bc", Loc); 916 Value *End0 = CastInst::Create(Op, Ends[i], PtrArithTy, "bc", Loc); 917 Value *End1 = CastInst::Create(Op, Ends[j], PtrArithTy, "bc", Loc); 918 919 Value *Cmp0 = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_ULE, 920 Start0, End1, "bound0", Loc); 921 Value *Cmp1 = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_ULE, 922 Start1, End0, "bound1", Loc); 923 Value *IsConflict = BinaryOperator::Create(Instruction::And, Cmp0, Cmp1, 924 "found.conflict", Loc); 925 if (MemoryRuntimeCheck) 926 MemoryRuntimeCheck = BinaryOperator::Create(Instruction::Or, 927 MemoryRuntimeCheck, 928 IsConflict, 929 "conflict.rdx", Loc); 930 else 931 MemoryRuntimeCheck = IsConflict; 932 933 } 934 } 935 936 return MemoryRuntimeCheck; 937} 938 939void 940InnerLoopVectorizer::createEmptyLoop(LoopVectorizationLegality *Legal) { 941 /* 942 In this function we generate a new loop. The new loop will contain 943 the vectorized instructions while the old loop will continue to run the 944 scalar remainder. 945 946 [ ] <-- vector loop bypass. 947 / | 948 / v 949 | [ ] <-- vector pre header. 950 | | 951 | v 952 | [ ] \ 953 | [ ]_| <-- vector loop. 954 | | 955 \ v 956 >[ ] <--- middle-block. 957 / | 958 / v 959 | [ ] <--- new preheader. 960 | | 961 | v 962 | [ ] \ 963 | [ ]_| <-- old scalar loop to handle remainder. 964 \ | 965 \ v 966 >[ ] <-- exit block. 967 ... 968 */ 969 970 BasicBlock *OldBasicBlock = OrigLoop->getHeader(); 971 BasicBlock *BypassBlock = OrigLoop->getLoopPreheader(); 972 BasicBlock *ExitBlock = OrigLoop->getExitBlock(); 973 assert(ExitBlock && "Must have an exit block"); 974 975 // Some loops have a single integer induction variable, while other loops 976 // don't. One example is c++ iterators that often have multiple pointer 977 // induction variables. In the code below we also support a case where we 978 // don't have a single induction variable. 979 OldInduction = Legal->getInduction(); 980 Type *IdxTy = OldInduction ? OldInduction->getType() : 981 DL->getIntPtrType(SE->getContext()); 982 983 // Find the loop boundaries. 984 const SCEV *ExitCount = SE->getExitCount(OrigLoop, OrigLoop->getLoopLatch()); 985 assert(ExitCount != SE->getCouldNotCompute() && "Invalid loop count"); 986 987 // Get the total trip count from the count by adding 1. 988 ExitCount = SE->getAddExpr(ExitCount, 989 SE->getConstant(ExitCount->getType(), 1)); 990 991 // Expand the trip count and place the new instructions in the preheader. 992 // Notice that the pre-header does not change, only the loop body. 993 SCEVExpander Exp(*SE, "induction"); 994 995 // Count holds the overall loop count (N). 996 Value *Count = Exp.expandCodeFor(ExitCount, ExitCount->getType(), 997 BypassBlock->getTerminator()); 998 999 // The loop index does not have to start at Zero. Find the original start 1000 // value from the induction PHI node. If we don't have an induction variable 1001 // then we know that it starts at zero. 1002 Value *StartIdx = OldInduction ? 1003 OldInduction->getIncomingValueForBlock(BypassBlock): 1004 ConstantInt::get(IdxTy, 0); 1005 1006 assert(BypassBlock && "Invalid loop structure"); 1007 1008 // Generate the code that checks in runtime if arrays overlap. 1009 Value *MemoryRuntimeCheck = addRuntimeCheck(Legal, 1010 BypassBlock->getTerminator()); 1011 1012 // Split the single block loop into the two loop structure described above. 1013 BasicBlock *VectorPH = 1014 BypassBlock->splitBasicBlock(BypassBlock->getTerminator(), "vector.ph"); 1015 BasicBlock *VecBody = 1016 VectorPH->splitBasicBlock(VectorPH->getTerminator(), "vector.body"); 1017 BasicBlock *MiddleBlock = 1018 VecBody->splitBasicBlock(VecBody->getTerminator(), "middle.block"); 1019 BasicBlock *ScalarPH = 1020 MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(), "scalar.ph"); 1021 1022 // This is the location in which we add all of the logic for bypassing 1023 // the new vector loop. 1024 Instruction *Loc = BypassBlock->getTerminator(); 1025 1026 // Use this IR builder to create the loop instructions (Phi, Br, Cmp) 1027 // inside the loop. 1028 Builder.SetInsertPoint(VecBody->getFirstInsertionPt()); 1029 1030 // Generate the induction variable. 1031 Induction = Builder.CreatePHI(IdxTy, 2, "index"); 1032 // The loop step is equal to the vectorization factor (num of SIMD elements) 1033 // times the unroll factor (num of SIMD instructions). 1034 Constant *Step = ConstantInt::get(IdxTy, VF * UF); 1035 1036 // We may need to extend the index in case there is a type mismatch. 1037 // We know that the count starts at zero and does not overflow. 1038 if (Count->getType() != IdxTy) { 1039 // The exit count can be of pointer type. Convert it to the correct 1040 // integer type. 1041 if (ExitCount->getType()->isPointerTy()) 1042 Count = CastInst::CreatePointerCast(Count, IdxTy, "ptrcnt.to.int", Loc); 1043 else 1044 Count = CastInst::CreateZExtOrBitCast(Count, IdxTy, "zext.cnt", Loc); 1045 } 1046 1047 // Add the start index to the loop count to get the new end index. 1048 Value *IdxEnd = BinaryOperator::CreateAdd(Count, StartIdx, "end.idx", Loc); 1049 1050 // Now we need to generate the expression for N - (N % VF), which is 1051 // the part that the vectorized body will execute. 1052 Value *R = BinaryOperator::CreateURem(Count, Step, "n.mod.vf", Loc); 1053 Value *CountRoundDown = BinaryOperator::CreateSub(Count, R, "n.vec", Loc); 1054 Value *IdxEndRoundDown = BinaryOperator::CreateAdd(CountRoundDown, StartIdx, 1055 "end.idx.rnd.down", Loc); 1056 1057 // Now, compare the new count to zero. If it is zero skip the vector loop and 1058 // jump to the scalar loop. 1059 Value *Cmp = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, 1060 IdxEndRoundDown, 1061 StartIdx, 1062 "cmp.zero", Loc); 1063 1064 // If we are using memory runtime checks, include them in. 1065 if (MemoryRuntimeCheck) 1066 Cmp = BinaryOperator::Create(Instruction::Or, Cmp, MemoryRuntimeCheck, 1067 "CntOrMem", Loc); 1068 1069 BranchInst::Create(MiddleBlock, VectorPH, Cmp, Loc); 1070 // Remove the old terminator. 1071 Loc->eraseFromParent(); 1072 1073 // We are going to resume the execution of the scalar loop. 1074 // Go over all of the induction variables that we found and fix the 1075 // PHIs that are left in the scalar version of the loop. 1076 // The starting values of PHI nodes depend on the counter of the last 1077 // iteration in the vectorized loop. 1078 // If we come from a bypass edge then we need to start from the original 1079 // start value. 1080 1081 // This variable saves the new starting index for the scalar loop. 1082 PHINode *ResumeIndex = 0; 1083 LoopVectorizationLegality::InductionList::iterator I, E; 1084 LoopVectorizationLegality::InductionList *List = Legal->getInductionVars(); 1085 for (I = List->begin(), E = List->end(); I != E; ++I) { 1086 PHINode *OrigPhi = I->first; 1087 LoopVectorizationLegality::InductionInfo II = I->second; 1088 PHINode *ResumeVal = PHINode::Create(OrigPhi->getType(), 2, "resume.val", 1089 MiddleBlock->getTerminator()); 1090 Value *EndValue = 0; 1091 switch (II.IK) { 1092 case LoopVectorizationLegality::NoInduction: 1093 llvm_unreachable("Unknown induction"); 1094 case LoopVectorizationLegality::IntInduction: { 1095 // Handle the integer induction counter: 1096 assert(OrigPhi->getType()->isIntegerTy() && "Invalid type"); 1097 assert(OrigPhi == OldInduction && "Unknown integer PHI"); 1098 // We know what the end value is. 1099 EndValue = IdxEndRoundDown; 1100 // We also know which PHI node holds it. 1101 ResumeIndex = ResumeVal; 1102 break; 1103 } 1104 case LoopVectorizationLegality::ReverseIntInduction: { 1105 // Convert the CountRoundDown variable to the PHI size. 1106 unsigned CRDSize = CountRoundDown->getType()->getScalarSizeInBits(); 1107 unsigned IISize = II.StartValue->getType()->getScalarSizeInBits(); 1108 Value *CRD = CountRoundDown; 1109 if (CRDSize > IISize) 1110 CRD = CastInst::Create(Instruction::Trunc, CountRoundDown, 1111 II.StartValue->getType(), 1112 "tr.crd", BypassBlock->getTerminator()); 1113 else if (CRDSize < IISize) 1114 CRD = CastInst::Create(Instruction::SExt, CountRoundDown, 1115 II.StartValue->getType(), 1116 "sext.crd", BypassBlock->getTerminator()); 1117 // Handle reverse integer induction counter: 1118 EndValue = BinaryOperator::CreateSub(II.StartValue, CRD, "rev.ind.end", 1119 BypassBlock->getTerminator()); 1120 break; 1121 } 1122 case LoopVectorizationLegality::PtrInduction: { 1123 // For pointer induction variables, calculate the offset using 1124 // the end index. 1125 EndValue = GetElementPtrInst::Create(II.StartValue, CountRoundDown, 1126 "ptr.ind.end", 1127 BypassBlock->getTerminator()); 1128 break; 1129 } 1130 }// end of case 1131 1132 // The new PHI merges the original incoming value, in case of a bypass, 1133 // or the value at the end of the vectorized loop. 1134 ResumeVal->addIncoming(II.StartValue, BypassBlock); 1135 ResumeVal->addIncoming(EndValue, VecBody); 1136 1137 // Fix the scalar body counter (PHI node). 1138 unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH); 1139 OrigPhi->setIncomingValue(BlockIdx, ResumeVal); 1140 } 1141 1142 // If we are generating a new induction variable then we also need to 1143 // generate the code that calculates the exit value. This value is not 1144 // simply the end of the counter because we may skip the vectorized body 1145 // in case of a runtime check. 1146 if (!OldInduction){ 1147 assert(!ResumeIndex && "Unexpected resume value found"); 1148 ResumeIndex = PHINode::Create(IdxTy, 2, "new.indc.resume.val", 1149 MiddleBlock->getTerminator()); 1150 ResumeIndex->addIncoming(StartIdx, BypassBlock); 1151 ResumeIndex->addIncoming(IdxEndRoundDown, VecBody); 1152 } 1153 1154 // Make sure that we found the index where scalar loop needs to continue. 1155 assert(ResumeIndex && ResumeIndex->getType()->isIntegerTy() && 1156 "Invalid resume Index"); 1157 1158 // Add a check in the middle block to see if we have completed 1159 // all of the iterations in the first vector loop. 1160 // If (N - N%VF) == N, then we *don't* need to run the remainder. 1161 Value *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, IdxEnd, 1162 ResumeIndex, "cmp.n", 1163 MiddleBlock->getTerminator()); 1164 1165 BranchInst::Create(ExitBlock, ScalarPH, CmpN, MiddleBlock->getTerminator()); 1166 // Remove the old terminator. 1167 MiddleBlock->getTerminator()->eraseFromParent(); 1168 1169 // Create i+1 and fill the PHINode. 1170 Value *NextIdx = Builder.CreateAdd(Induction, Step, "index.next"); 1171 Induction->addIncoming(StartIdx, VectorPH); 1172 Induction->addIncoming(NextIdx, VecBody); 1173 // Create the compare. 1174 Value *ICmp = Builder.CreateICmpEQ(NextIdx, IdxEndRoundDown); 1175 Builder.CreateCondBr(ICmp, MiddleBlock, VecBody); 1176 1177 // Now we have two terminators. Remove the old one from the block. 1178 VecBody->getTerminator()->eraseFromParent(); 1179 1180 // Get ready to start creating new instructions into the vectorized body. 1181 Builder.SetInsertPoint(VecBody->getFirstInsertionPt()); 1182 1183 // Create and register the new vector loop. 1184 Loop* Lp = new Loop(); 1185 Loop *ParentLoop = OrigLoop->getParentLoop(); 1186 1187 // Insert the new loop into the loop nest and register the new basic blocks. 1188 if (ParentLoop) { 1189 ParentLoop->addChildLoop(Lp); 1190 ParentLoop->addBasicBlockToLoop(ScalarPH, LI->getBase()); 1191 ParentLoop->addBasicBlockToLoop(VectorPH, LI->getBase()); 1192 ParentLoop->addBasicBlockToLoop(MiddleBlock, LI->getBase()); 1193 } else { 1194 LI->addTopLevelLoop(Lp); 1195 } 1196 1197 Lp->addBasicBlockToLoop(VecBody, LI->getBase()); 1198 1199 // Save the state. 1200 LoopVectorPreHeader = VectorPH; 1201 LoopScalarPreHeader = ScalarPH; 1202 LoopMiddleBlock = MiddleBlock; 1203 LoopExitBlock = ExitBlock; 1204 LoopVectorBody = VecBody; 1205 LoopScalarBody = OldBasicBlock; 1206 LoopBypassBlock = BypassBlock; 1207} 1208 1209/// This function returns the identity element (or neutral element) for 1210/// the operation K. 1211static Constant* 1212getReductionIdentity(LoopVectorizationLegality::ReductionKind K, Type *Tp) { 1213 switch (K) { 1214 case LoopVectorizationLegality:: RK_IntegerXor: 1215 case LoopVectorizationLegality:: RK_IntegerAdd: 1216 case LoopVectorizationLegality:: RK_IntegerOr: 1217 // Adding, Xoring, Oring zero to a number does not change it. 1218 return ConstantInt::get(Tp, 0); 1219 case LoopVectorizationLegality:: RK_IntegerMult: 1220 // Multiplying a number by 1 does not change it. 1221 return ConstantInt::get(Tp, 1); 1222 case LoopVectorizationLegality:: RK_IntegerAnd: 1223 // AND-ing a number with an all-1 value does not change it. 1224 return ConstantInt::get(Tp, -1, true); 1225 case LoopVectorizationLegality:: RK_FloatMult: 1226 // Multiplying a number by 1 does not change it. 1227 return ConstantFP::get(Tp, 1.0L); 1228 case LoopVectorizationLegality:: RK_FloatAdd: 1229 // Adding zero to a number does not change it. 1230 return ConstantFP::get(Tp, 0.0L); 1231 default: 1232 llvm_unreachable("Unknown reduction kind"); 1233 } 1234} 1235 1236static bool 1237isTriviallyVectorizableIntrinsic(Instruction *Inst) { 1238 IntrinsicInst *II = dyn_cast<IntrinsicInst>(Inst); 1239 if (!II) 1240 return false; 1241 switch (II->getIntrinsicID()) { 1242 case Intrinsic::sqrt: 1243 case Intrinsic::sin: 1244 case Intrinsic::cos: 1245 case Intrinsic::exp: 1246 case Intrinsic::exp2: 1247 case Intrinsic::log: 1248 case Intrinsic::log10: 1249 case Intrinsic::log2: 1250 case Intrinsic::fabs: 1251 case Intrinsic::floor: 1252 case Intrinsic::ceil: 1253 case Intrinsic::trunc: 1254 case Intrinsic::rint: 1255 case Intrinsic::nearbyint: 1256 case Intrinsic::pow: 1257 case Intrinsic::fma: 1258 case Intrinsic::fmuladd: 1259 return true; 1260 default: 1261 return false; 1262 } 1263 return false; 1264} 1265 1266void 1267InnerLoopVectorizer::vectorizeLoop(LoopVectorizationLegality *Legal) { 1268 //===------------------------------------------------===// 1269 // 1270 // Notice: any optimization or new instruction that go 1271 // into the code below should be also be implemented in 1272 // the cost-model. 1273 // 1274 //===------------------------------------------------===// 1275 BasicBlock &BB = *OrigLoop->getHeader(); 1276 Constant *Zero = 1277 ConstantInt::get(IntegerType::getInt32Ty(BB.getContext()), 0); 1278 1279 // In order to support reduction variables we need to be able to vectorize 1280 // Phi nodes. Phi nodes have cycles, so we need to vectorize them in two 1281 // stages. First, we create a new vector PHI node with no incoming edges. 1282 // We use this value when we vectorize all of the instructions that use the 1283 // PHI. Next, after all of the instructions in the block are complete we 1284 // add the new incoming edges to the PHI. At this point all of the 1285 // instructions in the basic block are vectorized, so we can use them to 1286 // construct the PHI. 1287 PhiVector RdxPHIsToFix; 1288 1289 // Scan the loop in a topological order to ensure that defs are vectorized 1290 // before users. 1291 LoopBlocksDFS DFS(OrigLoop); 1292 DFS.perform(LI); 1293 1294 // Vectorize all of the blocks in the original loop. 1295 for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(), 1296 be = DFS.endRPO(); bb != be; ++bb) 1297 vectorizeBlockInLoop(Legal, *bb, &RdxPHIsToFix); 1298 1299 // At this point every instruction in the original loop is widened to 1300 // a vector form. We are almost done. Now, we need to fix the PHI nodes 1301 // that we vectorized. The PHI nodes are currently empty because we did 1302 // not want to introduce cycles. Notice that the remaining PHI nodes 1303 // that we need to fix are reduction variables. 1304 1305 // Create the 'reduced' values for each of the induction vars. 1306 // The reduced values are the vector values that we scalarize and combine 1307 // after the loop is finished. 1308 for (PhiVector::iterator it = RdxPHIsToFix.begin(), e = RdxPHIsToFix.end(); 1309 it != e; ++it) { 1310 PHINode *RdxPhi = *it; 1311 assert(RdxPhi && "Unable to recover vectorized PHI"); 1312 1313 // Find the reduction variable descriptor. 1314 assert(Legal->getReductionVars()->count(RdxPhi) && 1315 "Unable to find the reduction variable"); 1316 LoopVectorizationLegality::ReductionDescriptor RdxDesc = 1317 (*Legal->getReductionVars())[RdxPhi]; 1318 1319 // We need to generate a reduction vector from the incoming scalar. 1320 // To do so, we need to generate the 'identity' vector and overide 1321 // one of the elements with the incoming scalar reduction. We need 1322 // to do it in the vector-loop preheader. 1323 Builder.SetInsertPoint(LoopBypassBlock->getTerminator()); 1324 1325 // This is the vector-clone of the value that leaves the loop. 1326 VectorParts &VectorExit = getVectorValue(RdxDesc.LoopExitInstr); 1327 Type *VecTy = VectorExit[0]->getType(); 1328 1329 // Find the reduction identity variable. Zero for addition, or, xor, 1330 // one for multiplication, -1 for And. 1331 Constant *Iden = getReductionIdentity(RdxDesc.Kind, VecTy->getScalarType()); 1332 Constant *Identity = ConstantVector::getSplat(VF, Iden); 1333 1334 // This vector is the Identity vector where the first element is the 1335 // incoming scalar reduction. 1336 Value *VectorStart = Builder.CreateInsertElement(Identity, 1337 RdxDesc.StartValue, Zero); 1338 1339 // Fix the vector-loop phi. 1340 // We created the induction variable so we know that the 1341 // preheader is the first entry. 1342 BasicBlock *VecPreheader = Induction->getIncomingBlock(0); 1343 1344 // Reductions do not have to start at zero. They can start with 1345 // any loop invariant values. 1346 VectorParts &VecRdxPhi = WidenMap.get(RdxPhi); 1347 BasicBlock *Latch = OrigLoop->getLoopLatch(); 1348 Value *LoopVal = RdxPhi->getIncomingValueForBlock(Latch); 1349 VectorParts &Val = getVectorValue(LoopVal); 1350 for (unsigned part = 0; part < UF; ++part) { 1351 // Make sure to add the reduction stat value only to the 1352 // first unroll part. 1353 Value *StartVal = (part == 0) ? VectorStart : Identity; 1354 cast<PHINode>(VecRdxPhi[part])->addIncoming(StartVal, VecPreheader); 1355 cast<PHINode>(VecRdxPhi[part])->addIncoming(Val[part], LoopVectorBody); 1356 } 1357 1358 // Before each round, move the insertion point right between 1359 // the PHIs and the values we are going to write. 1360 // This allows us to write both PHINodes and the extractelement 1361 // instructions. 1362 Builder.SetInsertPoint(LoopMiddleBlock->getFirstInsertionPt()); 1363 1364 VectorParts RdxParts; 1365 for (unsigned part = 0; part < UF; ++part) { 1366 // This PHINode contains the vectorized reduction variable, or 1367 // the initial value vector, if we bypass the vector loop. 1368 VectorParts &RdxExitVal = getVectorValue(RdxDesc.LoopExitInstr); 1369 PHINode *NewPhi = Builder.CreatePHI(VecTy, 2, "rdx.vec.exit.phi"); 1370 Value *StartVal = (part == 0) ? VectorStart : Identity; 1371 NewPhi->addIncoming(StartVal, LoopBypassBlock); 1372 NewPhi->addIncoming(RdxExitVal[part], LoopVectorBody); 1373 RdxParts.push_back(NewPhi); 1374 } 1375 1376 // Reduce all of the unrolled parts into a single vector. 1377 Value *ReducedPartRdx = RdxParts[0]; 1378 for (unsigned part = 1; part < UF; ++part) { 1379 switch (RdxDesc.Kind) { 1380 case LoopVectorizationLegality::RK_IntegerAdd: 1381 ReducedPartRdx = 1382 Builder.CreateAdd(RdxParts[part], ReducedPartRdx, "add.rdx"); 1383 break; 1384 case LoopVectorizationLegality::RK_IntegerMult: 1385 ReducedPartRdx = 1386 Builder.CreateMul(RdxParts[part], ReducedPartRdx, "mul.rdx"); 1387 break; 1388 case LoopVectorizationLegality::RK_IntegerOr: 1389 ReducedPartRdx = 1390 Builder.CreateOr(RdxParts[part], ReducedPartRdx, "or.rdx"); 1391 break; 1392 case LoopVectorizationLegality::RK_IntegerAnd: 1393 ReducedPartRdx = 1394 Builder.CreateAnd(RdxParts[part], ReducedPartRdx, "and.rdx"); 1395 break; 1396 case LoopVectorizationLegality::RK_IntegerXor: 1397 ReducedPartRdx = 1398 Builder.CreateXor(RdxParts[part], ReducedPartRdx, "xor.rdx"); 1399 break; 1400 case LoopVectorizationLegality::RK_FloatMult: 1401 ReducedPartRdx = 1402 Builder.CreateFMul(RdxParts[part], ReducedPartRdx, "fmul.rdx"); 1403 break; 1404 case LoopVectorizationLegality::RK_FloatAdd: 1405 ReducedPartRdx = 1406 Builder.CreateFAdd(RdxParts[part], ReducedPartRdx, "fadd.rdx"); 1407 break; 1408 default: 1409 llvm_unreachable("Unknown reduction operation"); 1410 } 1411 } 1412 1413 1414 // VF is a power of 2 so we can emit the reduction using log2(VF) shuffles 1415 // and vector ops, reducing the set of values being computed by half each 1416 // round. 1417 assert(isPowerOf2_32(VF) && 1418 "Reduction emission only supported for pow2 vectors!"); 1419 Value *TmpVec = ReducedPartRdx; 1420 SmallVector<Constant*, 32> ShuffleMask(VF, 0); 1421 for (unsigned i = VF; i != 1; i >>= 1) { 1422 // Move the upper half of the vector to the lower half. 1423 for (unsigned j = 0; j != i/2; ++j) 1424 ShuffleMask[j] = Builder.getInt32(i/2 + j); 1425 1426 // Fill the rest of the mask with undef. 1427 std::fill(&ShuffleMask[i/2], ShuffleMask.end(), 1428 UndefValue::get(Builder.getInt32Ty())); 1429 1430 Value *Shuf = 1431 Builder.CreateShuffleVector(TmpVec, 1432 UndefValue::get(TmpVec->getType()), 1433 ConstantVector::get(ShuffleMask), 1434 "rdx.shuf"); 1435 1436 // Emit the operation on the shuffled value. 1437 switch (RdxDesc.Kind) { 1438 case LoopVectorizationLegality::RK_IntegerAdd: 1439 TmpVec = Builder.CreateAdd(TmpVec, Shuf, "add.rdx"); 1440 break; 1441 case LoopVectorizationLegality::RK_IntegerMult: 1442 TmpVec = Builder.CreateMul(TmpVec, Shuf, "mul.rdx"); 1443 break; 1444 case LoopVectorizationLegality::RK_IntegerOr: 1445 TmpVec = Builder.CreateOr(TmpVec, Shuf, "or.rdx"); 1446 break; 1447 case LoopVectorizationLegality::RK_IntegerAnd: 1448 TmpVec = Builder.CreateAnd(TmpVec, Shuf, "and.rdx"); 1449 break; 1450 case LoopVectorizationLegality::RK_IntegerXor: 1451 TmpVec = Builder.CreateXor(TmpVec, Shuf, "xor.rdx"); 1452 break; 1453 case LoopVectorizationLegality::RK_FloatMult: 1454 TmpVec = Builder.CreateFMul(TmpVec, Shuf, "fmul.rdx"); 1455 break; 1456 case LoopVectorizationLegality::RK_FloatAdd: 1457 TmpVec = Builder.CreateFAdd(TmpVec, Shuf, "fadd.rdx"); 1458 break; 1459 default: 1460 llvm_unreachable("Unknown reduction operation"); 1461 } 1462 } 1463 1464 // The result is in the first element of the vector. 1465 Value *Scalar0 = Builder.CreateExtractElement(TmpVec, Builder.getInt32(0)); 1466 1467 // Now, we need to fix the users of the reduction variable 1468 // inside and outside of the scalar remainder loop. 1469 // We know that the loop is in LCSSA form. We need to update the 1470 // PHI nodes in the exit blocks. 1471 for (BasicBlock::iterator LEI = LoopExitBlock->begin(), 1472 LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) { 1473 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI); 1474 if (!LCSSAPhi) continue; 1475 1476 // All PHINodes need to have a single entry edge, or two if 1477 // we already fixed them. 1478 assert(LCSSAPhi->getNumIncomingValues() < 3 && "Invalid LCSSA PHI"); 1479 1480 // We found our reduction value exit-PHI. Update it with the 1481 // incoming bypass edge. 1482 if (LCSSAPhi->getIncomingValue(0) == RdxDesc.LoopExitInstr) { 1483 // Add an edge coming from the bypass. 1484 LCSSAPhi->addIncoming(Scalar0, LoopMiddleBlock); 1485 break; 1486 } 1487 }// end of the LCSSA phi scan. 1488 1489 // Fix the scalar loop reduction variable with the incoming reduction sum 1490 // from the vector body and from the backedge value. 1491 int IncomingEdgeBlockIdx = 1492 (RdxPhi)->getBasicBlockIndex(OrigLoop->getLoopLatch()); 1493 assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index"); 1494 // Pick the other block. 1495 int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1); 1496 (RdxPhi)->setIncomingValue(SelfEdgeBlockIdx, Scalar0); 1497 (RdxPhi)->setIncomingValue(IncomingEdgeBlockIdx, RdxDesc.LoopExitInstr); 1498 }// end of for each redux variable. 1499 1500 // The Loop exit block may have single value PHI nodes where the incoming 1501 // value is 'undef'. While vectorizing we only handled real values that 1502 // were defined inside the loop. Here we handle the 'undef case'. 1503 // See PR14725. 1504 for (BasicBlock::iterator LEI = LoopExitBlock->begin(), 1505 LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) { 1506 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI); 1507 if (!LCSSAPhi) continue; 1508 if (LCSSAPhi->getNumIncomingValues() == 1) 1509 LCSSAPhi->addIncoming(UndefValue::get(LCSSAPhi->getType()), 1510 LoopMiddleBlock); 1511 } 1512} 1513 1514InnerLoopVectorizer::VectorParts 1515InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) { 1516 assert(std::find(pred_begin(Dst), pred_end(Dst), Src) != pred_end(Dst) && 1517 "Invalid edge"); 1518 1519 VectorParts SrcMask = createBlockInMask(Src); 1520 1521 // The terminator has to be a branch inst! 1522 BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator()); 1523 assert(BI && "Unexpected terminator found"); 1524 1525 if (BI->isConditional()) { 1526 VectorParts EdgeMask = getVectorValue(BI->getCondition()); 1527 1528 if (BI->getSuccessor(0) != Dst) 1529 for (unsigned part = 0; part < UF; ++part) 1530 EdgeMask[part] = Builder.CreateNot(EdgeMask[part]); 1531 1532 for (unsigned part = 0; part < UF; ++part) 1533 EdgeMask[part] = Builder.CreateAnd(EdgeMask[part], SrcMask[part]); 1534 return EdgeMask; 1535 } 1536 1537 return SrcMask; 1538} 1539 1540InnerLoopVectorizer::VectorParts 1541InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) { 1542 assert(OrigLoop->contains(BB) && "Block is not a part of a loop"); 1543 1544 // Loop incoming mask is all-one. 1545 if (OrigLoop->getHeader() == BB) { 1546 Value *C = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 1); 1547 return getVectorValue(C); 1548 } 1549 1550 // This is the block mask. We OR all incoming edges, and with zero. 1551 Value *Zero = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 0); 1552 VectorParts BlockMask = getVectorValue(Zero); 1553 1554 // For each pred: 1555 for (pred_iterator it = pred_begin(BB), e = pred_end(BB); it != e; ++it) { 1556 VectorParts EM = createEdgeMask(*it, BB); 1557 for (unsigned part = 0; part < UF; ++part) 1558 BlockMask[part] = Builder.CreateOr(BlockMask[part], EM[part]); 1559 } 1560 1561 return BlockMask; 1562} 1563 1564void 1565InnerLoopVectorizer::vectorizeBlockInLoop(LoopVectorizationLegality *Legal, 1566 BasicBlock *BB, PhiVector *PV) { 1567 Constant *Zero = Builder.getInt32(0); 1568 1569 // For each instruction in the old loop. 1570 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 1571 VectorParts &Entry = WidenMap.get(it); 1572 switch (it->getOpcode()) { 1573 case Instruction::Br: 1574 // Nothing to do for PHIs and BR, since we already took care of the 1575 // loop control flow instructions. 1576 continue; 1577 case Instruction::PHI:{ 1578 PHINode* P = cast<PHINode>(it); 1579 // Handle reduction variables: 1580 if (Legal->getReductionVars()->count(P)) { 1581 for (unsigned part = 0; part < UF; ++part) { 1582 // This is phase one of vectorizing PHIs. 1583 Type *VecTy = VectorType::get(it->getType(), VF); 1584 Entry[part] = PHINode::Create(VecTy, 2, "vec.phi", 1585 LoopVectorBody-> getFirstInsertionPt()); 1586 } 1587 PV->push_back(P); 1588 continue; 1589 } 1590 1591 // Check for PHI nodes that are lowered to vector selects. 1592 if (P->getParent() != OrigLoop->getHeader()) { 1593 // We know that all PHIs in non header blocks are converted into 1594 // selects, so we don't have to worry about the insertion order and we 1595 // can just use the builder. 1596 1597 // At this point we generate the predication tree. There may be 1598 // duplications since this is a simple recursive scan, but future 1599 // optimizations will clean it up. 1600 VectorParts Cond = createEdgeMask(P->getIncomingBlock(0), 1601 P->getParent()); 1602 1603 for (unsigned part = 0; part < UF; ++part) { 1604 VectorParts &In0 = getVectorValue(P->getIncomingValue(0)); 1605 VectorParts &In1 = getVectorValue(P->getIncomingValue(1)); 1606 Entry[part] = Builder.CreateSelect(Cond[part], In0[part], In1[part], 1607 "predphi"); 1608 } 1609 continue; 1610 } 1611 1612 // This PHINode must be an induction variable. 1613 // Make sure that we know about it. 1614 assert(Legal->getInductionVars()->count(P) && 1615 "Not an induction variable"); 1616 1617 LoopVectorizationLegality::InductionInfo II = 1618 Legal->getInductionVars()->lookup(P); 1619 1620 switch (II.IK) { 1621 case LoopVectorizationLegality::NoInduction: 1622 llvm_unreachable("Unknown induction"); 1623 case LoopVectorizationLegality::IntInduction: { 1624 assert(P == OldInduction && "Unexpected PHI"); 1625 Value *Broadcasted = getBroadcastInstrs(Induction); 1626 // After broadcasting the induction variable we need to make the 1627 // vector consecutive by adding 0, 1, 2 ... 1628 for (unsigned part = 0; part < UF; ++part) 1629 Entry[part] = getConsecutiveVector(Broadcasted, VF * part, false); 1630 continue; 1631 } 1632 case LoopVectorizationLegality::ReverseIntInduction: 1633 case LoopVectorizationLegality::PtrInduction: 1634 // Handle reverse integer and pointer inductions. 1635 Value *StartIdx = 0; 1636 // If we have a single integer induction variable then use it. 1637 // Otherwise, start counting at zero. 1638 if (OldInduction) { 1639 LoopVectorizationLegality::InductionInfo OldII = 1640 Legal->getInductionVars()->lookup(OldInduction); 1641 StartIdx = OldII.StartValue; 1642 } else { 1643 StartIdx = ConstantInt::get(Induction->getType(), 0); 1644 } 1645 // This is the normalized GEP that starts counting at zero. 1646 Value *NormalizedIdx = Builder.CreateSub(Induction, StartIdx, 1647 "normalized.idx"); 1648 1649 // Handle the reverse integer induction variable case. 1650 if (LoopVectorizationLegality::ReverseIntInduction == II.IK) { 1651 IntegerType *DstTy = cast<IntegerType>(II.StartValue->getType()); 1652 Value *CNI = Builder.CreateSExtOrTrunc(NormalizedIdx, DstTy, 1653 "resize.norm.idx"); 1654 Value *ReverseInd = Builder.CreateSub(II.StartValue, CNI, 1655 "reverse.idx"); 1656 1657 // This is a new value so do not hoist it out. 1658 Value *Broadcasted = getBroadcastInstrs(ReverseInd); 1659 // After broadcasting the induction variable we need to make the 1660 // vector consecutive by adding ... -3, -2, -1, 0. 1661 for (unsigned part = 0; part < UF; ++part) 1662 Entry[part] = getConsecutiveVector(Broadcasted, -VF * part, true); 1663 continue; 1664 } 1665 1666 // Handle the pointer induction variable case. 1667 assert(P->getType()->isPointerTy() && "Unexpected type."); 1668 1669 // This is the vector of results. Notice that we don't generate 1670 // vector geps because scalar geps result in better code. 1671 for (unsigned part = 0; part < UF; ++part) { 1672 Value *VecVal = UndefValue::get(VectorType::get(P->getType(), VF)); 1673 for (unsigned int i = 0; i < VF; ++i) { 1674 Constant *Idx = ConstantInt::get(Induction->getType(), 1675 i + part * VF); 1676 Value *GlobalIdx = Builder.CreateAdd(NormalizedIdx, Idx, 1677 "gep.idx"); 1678 Value *SclrGep = Builder.CreateGEP(II.StartValue, GlobalIdx, 1679 "next.gep"); 1680 VecVal = Builder.CreateInsertElement(VecVal, SclrGep, 1681 Builder.getInt32(i), 1682 "insert.gep"); 1683 } 1684 Entry[part] = VecVal; 1685 } 1686 continue; 1687 } 1688 1689 }// End of PHI. 1690 1691 case Instruction::Add: 1692 case Instruction::FAdd: 1693 case Instruction::Sub: 1694 case Instruction::FSub: 1695 case Instruction::Mul: 1696 case Instruction::FMul: 1697 case Instruction::UDiv: 1698 case Instruction::SDiv: 1699 case Instruction::FDiv: 1700 case Instruction::URem: 1701 case Instruction::SRem: 1702 case Instruction::FRem: 1703 case Instruction::Shl: 1704 case Instruction::LShr: 1705 case Instruction::AShr: 1706 case Instruction::And: 1707 case Instruction::Or: 1708 case Instruction::Xor: { 1709 // Just widen binops. 1710 BinaryOperator *BinOp = dyn_cast<BinaryOperator>(it); 1711 VectorParts &A = getVectorValue(it->getOperand(0)); 1712 VectorParts &B = getVectorValue(it->getOperand(1)); 1713 1714 // Use this vector value for all users of the original instruction. 1715 for (unsigned Part = 0; Part < UF; ++Part) { 1716 Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A[Part], B[Part]); 1717 1718 // Update the NSW, NUW and Exact flags. 1719 BinaryOperator *VecOp = cast<BinaryOperator>(V); 1720 if (isa<OverflowingBinaryOperator>(BinOp)) { 1721 VecOp->setHasNoSignedWrap(BinOp->hasNoSignedWrap()); 1722 VecOp->setHasNoUnsignedWrap(BinOp->hasNoUnsignedWrap()); 1723 } 1724 if (isa<PossiblyExactOperator>(VecOp)) 1725 VecOp->setIsExact(BinOp->isExact()); 1726 1727 Entry[Part] = V; 1728 } 1729 break; 1730 } 1731 case Instruction::Select: { 1732 // Widen selects. 1733 // If the selector is loop invariant we can create a select 1734 // instruction with a scalar condition. Otherwise, use vector-select. 1735 bool InvariantCond = SE->isLoopInvariant(SE->getSCEV(it->getOperand(0)), 1736 OrigLoop); 1737 1738 // The condition can be loop invariant but still defined inside the 1739 // loop. This means that we can't just use the original 'cond' value. 1740 // We have to take the 'vectorized' value and pick the first lane. 1741 // Instcombine will make this a no-op. 1742 VectorParts &Cond = getVectorValue(it->getOperand(0)); 1743 VectorParts &Op0 = getVectorValue(it->getOperand(1)); 1744 VectorParts &Op1 = getVectorValue(it->getOperand(2)); 1745 Value *ScalarCond = Builder.CreateExtractElement(Cond[0], 1746 Builder.getInt32(0)); 1747 for (unsigned Part = 0; Part < UF; ++Part) { 1748 Entry[Part] = Builder.CreateSelect( 1749 InvariantCond ? ScalarCond : Cond[Part], 1750 Op0[Part], 1751 Op1[Part]); 1752 } 1753 break; 1754 } 1755 1756 case Instruction::ICmp: 1757 case Instruction::FCmp: { 1758 // Widen compares. Generate vector compares. 1759 bool FCmp = (it->getOpcode() == Instruction::FCmp); 1760 CmpInst *Cmp = dyn_cast<CmpInst>(it); 1761 VectorParts &A = getVectorValue(it->getOperand(0)); 1762 VectorParts &B = getVectorValue(it->getOperand(1)); 1763 for (unsigned Part = 0; Part < UF; ++Part) { 1764 Value *C = 0; 1765 if (FCmp) 1766 C = Builder.CreateFCmp(Cmp->getPredicate(), A[Part], B[Part]); 1767 else 1768 C = Builder.CreateICmp(Cmp->getPredicate(), A[Part], B[Part]); 1769 Entry[Part] = C; 1770 } 1771 break; 1772 } 1773 1774 case Instruction::Store: { 1775 // Attempt to issue a wide store. 1776 StoreInst *SI = dyn_cast<StoreInst>(it); 1777 Type *StTy = VectorType::get(SI->getValueOperand()->getType(), VF); 1778 Value *Ptr = SI->getPointerOperand(); 1779 unsigned Alignment = SI->getAlignment(); 1780 1781 assert(!Legal->isUniform(Ptr) && 1782 "We do not allow storing to uniform addresses"); 1783 1784 1785 int Stride = Legal->isConsecutivePtr(Ptr); 1786 bool Reverse = Stride < 0; 1787 if (Stride == 0) { 1788 scalarizeInstruction(it); 1789 break; 1790 } 1791 1792 // Handle consecutive stores. 1793 1794 GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr); 1795 if (Gep) { 1796 // The last index does not have to be the induction. It can be 1797 // consecutive and be a function of the index. For example A[I+1]; 1798 unsigned NumOperands = Gep->getNumOperands(); 1799 1800 Value *LastGepOperand = Gep->getOperand(NumOperands - 1); 1801 VectorParts &GEPParts = getVectorValue(LastGepOperand); 1802 Value *LastIndex = GEPParts[0]; 1803 LastIndex = Builder.CreateExtractElement(LastIndex, Zero); 1804 1805 // Create the new GEP with the new induction variable. 1806 GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone()); 1807 Gep2->setOperand(NumOperands - 1, LastIndex); 1808 Ptr = Builder.Insert(Gep2); 1809 } else { 1810 // Use the induction element ptr. 1811 assert(isa<PHINode>(Ptr) && "Invalid induction ptr"); 1812 VectorParts &PtrVal = getVectorValue(Ptr); 1813 Ptr = Builder.CreateExtractElement(PtrVal[0], Zero); 1814 } 1815 1816 VectorParts &StoredVal = getVectorValue(SI->getValueOperand()); 1817 for (unsigned Part = 0; Part < UF; ++Part) { 1818 // Calculate the pointer for the specific unroll-part. 1819 Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF)); 1820 1821 if (Reverse) { 1822 // If we store to reverse consecutive memory locations then we need 1823 // to reverse the order of elements in the stored value. 1824 StoredVal[Part] = reverseVector(StoredVal[Part]); 1825 // If the address is consecutive but reversed, then the 1826 // wide store needs to start at the last vector element. 1827 PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF)); 1828 PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF)); 1829 } 1830 1831 Value *VecPtr = Builder.CreateBitCast(PartPtr, StTy->getPointerTo()); 1832 Builder.CreateStore(StoredVal[Part], VecPtr)->setAlignment(Alignment); 1833 } 1834 break; 1835 } 1836 case Instruction::Load: { 1837 // Attempt to issue a wide load. 1838 LoadInst *LI = dyn_cast<LoadInst>(it); 1839 Type *RetTy = VectorType::get(LI->getType(), VF); 1840 Value *Ptr = LI->getPointerOperand(); 1841 unsigned Alignment = LI->getAlignment(); 1842 1843 // If the pointer is loop invariant or if it is non consecutive, 1844 // scalarize the load. 1845 int Stride = Legal->isConsecutivePtr(Ptr); 1846 bool Reverse = Stride < 0; 1847 if (Legal->isUniform(Ptr) || Stride == 0) { 1848 scalarizeInstruction(it); 1849 break; 1850 } 1851 1852 GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr); 1853 if (Gep) { 1854 // The last index does not have to be the induction. It can be 1855 // consecutive and be a function of the index. For example A[I+1]; 1856 unsigned NumOperands = Gep->getNumOperands(); 1857 1858 Value *LastGepOperand = Gep->getOperand(NumOperands - 1); 1859 VectorParts &GEPParts = getVectorValue(LastGepOperand); 1860 Value *LastIndex = GEPParts[0]; 1861 LastIndex = Builder.CreateExtractElement(LastIndex, Zero); 1862 1863 // Create the new GEP with the new induction variable. 1864 GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone()); 1865 Gep2->setOperand(NumOperands - 1, LastIndex); 1866 Ptr = Builder.Insert(Gep2); 1867 } else { 1868 // Use the induction element ptr. 1869 assert(isa<PHINode>(Ptr) && "Invalid induction ptr"); 1870 VectorParts &PtrVal = getVectorValue(Ptr); 1871 Ptr = Builder.CreateExtractElement(PtrVal[0], Zero); 1872 } 1873 1874 for (unsigned Part = 0; Part < UF; ++Part) { 1875 // Calculate the pointer for the specific unroll-part. 1876 Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF)); 1877 1878 if (Reverse) { 1879 // If the address is consecutive but reversed, then the 1880 // wide store needs to start at the last vector element. 1881 PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF)); 1882 PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF)); 1883 } 1884 1885 Value *VecPtr = Builder.CreateBitCast(PartPtr, RetTy->getPointerTo()); 1886 Value *LI = Builder.CreateLoad(VecPtr, "wide.load"); 1887 cast<LoadInst>(LI)->setAlignment(Alignment); 1888 Entry[Part] = Reverse ? reverseVector(LI) : LI; 1889 } 1890 break; 1891 } 1892 case Instruction::ZExt: 1893 case Instruction::SExt: 1894 case Instruction::FPToUI: 1895 case Instruction::FPToSI: 1896 case Instruction::FPExt: 1897 case Instruction::PtrToInt: 1898 case Instruction::IntToPtr: 1899 case Instruction::SIToFP: 1900 case Instruction::UIToFP: 1901 case Instruction::Trunc: 1902 case Instruction::FPTrunc: 1903 case Instruction::BitCast: { 1904 CastInst *CI = dyn_cast<CastInst>(it); 1905 /// Optimize the special case where the source is the induction 1906 /// variable. Notice that we can only optimize the 'trunc' case 1907 /// because: a. FP conversions lose precision, b. sext/zext may wrap, 1908 /// c. other casts depend on pointer size. 1909 if (CI->getOperand(0) == OldInduction && 1910 it->getOpcode() == Instruction::Trunc) { 1911 Value *ScalarCast = Builder.CreateCast(CI->getOpcode(), Induction, 1912 CI->getType()); 1913 Value *Broadcasted = getBroadcastInstrs(ScalarCast); 1914 for (unsigned Part = 0; Part < UF; ++Part) 1915 Entry[Part] = getConsecutiveVector(Broadcasted, VF * Part, false); 1916 break; 1917 } 1918 /// Vectorize casts. 1919 Type *DestTy = VectorType::get(CI->getType()->getScalarType(), VF); 1920 1921 VectorParts &A = getVectorValue(it->getOperand(0)); 1922 for (unsigned Part = 0; Part < UF; ++Part) 1923 Entry[Part] = Builder.CreateCast(CI->getOpcode(), A[Part], DestTy); 1924 break; 1925 } 1926 1927 case Instruction::Call: { 1928 assert(isTriviallyVectorizableIntrinsic(it)); 1929 Module *M = BB->getParent()->getParent(); 1930 IntrinsicInst *II = cast<IntrinsicInst>(it); 1931 Intrinsic::ID ID = II->getIntrinsicID(); 1932 for (unsigned Part = 0; Part < UF; ++Part) { 1933 SmallVector<Value*, 4> Args; 1934 for (unsigned i = 0, ie = II->getNumArgOperands(); i != ie; ++i) { 1935 VectorParts &Arg = getVectorValue(II->getArgOperand(i)); 1936 Args.push_back(Arg[Part]); 1937 } 1938 Type *Tys[] = { VectorType::get(II->getType()->getScalarType(), VF) }; 1939 Function *F = Intrinsic::getDeclaration(M, ID, Tys); 1940 Entry[Part] = Builder.CreateCall(F, Args); 1941 } 1942 break; 1943 } 1944 1945 default: 1946 // All other instructions are unsupported. Scalarize them. 1947 scalarizeInstruction(it); 1948 break; 1949 }// end of switch. 1950 }// end of for_each instr. 1951} 1952 1953void InnerLoopVectorizer::updateAnalysis() { 1954 // Forget the original basic block. 1955 SE->forgetLoop(OrigLoop); 1956 1957 // Update the dominator tree information. 1958 assert(DT->properlyDominates(LoopBypassBlock, LoopExitBlock) && 1959 "Entry does not dominate exit."); 1960 1961 DT->addNewBlock(LoopVectorPreHeader, LoopBypassBlock); 1962 DT->addNewBlock(LoopVectorBody, LoopVectorPreHeader); 1963 DT->addNewBlock(LoopMiddleBlock, LoopBypassBlock); 1964 DT->addNewBlock(LoopScalarPreHeader, LoopMiddleBlock); 1965 DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader); 1966 DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock); 1967 1968 DEBUG(DT->verifyAnalysis()); 1969} 1970 1971bool LoopVectorizationLegality::canVectorizeWithIfConvert() { 1972 if (!EnableIfConversion) 1973 return false; 1974 1975 assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable"); 1976 std::vector<BasicBlock*> &LoopBlocks = TheLoop->getBlocksVector(); 1977 1978 // Collect the blocks that need predication. 1979 for (unsigned i = 0, e = LoopBlocks.size(); i < e; ++i) { 1980 BasicBlock *BB = LoopBlocks[i]; 1981 1982 // We don't support switch statements inside loops. 1983 if (!isa<BranchInst>(BB->getTerminator())) 1984 return false; 1985 1986 // We must have at most two predecessors because we need to convert 1987 // all PHIs to selects. 1988 unsigned Preds = std::distance(pred_begin(BB), pred_end(BB)); 1989 if (Preds > 2) 1990 return false; 1991 1992 // We must be able to predicate all blocks that need to be predicated. 1993 if (blockNeedsPredication(BB) && !blockCanBePredicated(BB)) 1994 return false; 1995 } 1996 1997 // We can if-convert this loop. 1998 return true; 1999} 2000 2001bool LoopVectorizationLegality::canVectorize() { 2002 assert(TheLoop->getLoopPreheader() && "No preheader!!"); 2003 2004 // We can only vectorize innermost loops. 2005 if (TheLoop->getSubLoopsVector().size()) 2006 return false; 2007 2008 // We must have a single backedge. 2009 if (TheLoop->getNumBackEdges() != 1) 2010 return false; 2011 2012 // We must have a single exiting block. 2013 if (!TheLoop->getExitingBlock()) 2014 return false; 2015 2016 unsigned NumBlocks = TheLoop->getNumBlocks(); 2017 2018 // Check if we can if-convert non single-bb loops. 2019 if (NumBlocks != 1 && !canVectorizeWithIfConvert()) { 2020 DEBUG(dbgs() << "LV: Can't if-convert the loop.\n"); 2021 return false; 2022 } 2023 2024 // We need to have a loop header. 2025 BasicBlock *Latch = TheLoop->getLoopLatch(); 2026 DEBUG(dbgs() << "LV: Found a loop: " << 2027 TheLoop->getHeader()->getName() << "\n"); 2028 2029 // ScalarEvolution needs to be able to find the exit count. 2030 const SCEV *ExitCount = SE->getExitCount(TheLoop, Latch); 2031 if (ExitCount == SE->getCouldNotCompute()) { 2032 DEBUG(dbgs() << "LV: SCEV could not compute the loop exit count.\n"); 2033 return false; 2034 } 2035 2036 // Do not loop-vectorize loops with a tiny trip count. 2037 unsigned TC = SE->getSmallConstantTripCount(TheLoop, Latch); 2038 if (TC > 0u && TC < TinyTripCountVectorThreshold) { 2039 DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " << 2040 "This loop is not worth vectorizing.\n"); 2041 return false; 2042 } 2043 2044 // Check if we can vectorize the instructions and CFG in this loop. 2045 if (!canVectorizeInstrs()) { 2046 DEBUG(dbgs() << "LV: Can't vectorize the instructions or CFG\n"); 2047 return false; 2048 } 2049 2050 // Go over each instruction and look at memory deps. 2051 if (!canVectorizeMemory()) { 2052 DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n"); 2053 return false; 2054 } 2055 2056 // Collect all of the variables that remain uniform after vectorization. 2057 collectLoopUniforms(); 2058 2059 DEBUG(dbgs() << "LV: We can vectorize this loop" << 2060 (PtrRtCheck.Need ? " (with a runtime bound check)" : "") 2061 <<"!\n"); 2062 2063 // Okay! We can vectorize. At this point we don't have any other mem analysis 2064 // which may limit our maximum vectorization factor, so just return true with 2065 // no restrictions. 2066 return true; 2067} 2068 2069bool LoopVectorizationLegality::canVectorizeInstrs() { 2070 BasicBlock *PreHeader = TheLoop->getLoopPreheader(); 2071 BasicBlock *Header = TheLoop->getHeader(); 2072 2073 // For each block in the loop. 2074 for (Loop::block_iterator bb = TheLoop->block_begin(), 2075 be = TheLoop->block_end(); bb != be; ++bb) { 2076 2077 // Scan the instructions in the block and look for hazards. 2078 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e; 2079 ++it) { 2080 2081 if (PHINode *Phi = dyn_cast<PHINode>(it)) { 2082 // This should not happen because the loop should be normalized. 2083 if (Phi->getNumIncomingValues() != 2) { 2084 DEBUG(dbgs() << "LV: Found an invalid PHI.\n"); 2085 return false; 2086 } 2087 2088 // Check that this PHI type is allowed. 2089 if (!Phi->getType()->isIntegerTy() && 2090 !Phi->getType()->isFloatingPointTy() && 2091 !Phi->getType()->isPointerTy()) { 2092 DEBUG(dbgs() << "LV: Found an non-int non-pointer PHI.\n"); 2093 return false; 2094 } 2095 2096 // If this PHINode is not in the header block, then we know that we 2097 // can convert it to select during if-conversion. No need to check if 2098 // the PHIs in this block are induction or reduction variables. 2099 if (*bb != Header) 2100 continue; 2101 2102 // This is the value coming from the preheader. 2103 Value *StartValue = Phi->getIncomingValueForBlock(PreHeader); 2104 // Check if this is an induction variable. 2105 InductionKind IK = isInductionVariable(Phi); 2106 2107 if (NoInduction != IK) { 2108 // Int inductions are special because we only allow one IV. 2109 if (IK == IntInduction) { 2110 if (Induction) { 2111 DEBUG(dbgs() << "LV: Found too many inductions."<< *Phi <<"\n"); 2112 return false; 2113 } 2114 Induction = Phi; 2115 } 2116 2117 DEBUG(dbgs() << "LV: Found an induction variable.\n"); 2118 Inductions[Phi] = InductionInfo(StartValue, IK); 2119 continue; 2120 } 2121 2122 if (AddReductionVar(Phi, RK_IntegerAdd)) { 2123 DEBUG(dbgs() << "LV: Found an ADD reduction PHI."<< *Phi <<"\n"); 2124 continue; 2125 } 2126 if (AddReductionVar(Phi, RK_IntegerMult)) { 2127 DEBUG(dbgs() << "LV: Found a MUL reduction PHI."<< *Phi <<"\n"); 2128 continue; 2129 } 2130 if (AddReductionVar(Phi, RK_IntegerOr)) { 2131 DEBUG(dbgs() << "LV: Found an OR reduction PHI."<< *Phi <<"\n"); 2132 continue; 2133 } 2134 if (AddReductionVar(Phi, RK_IntegerAnd)) { 2135 DEBUG(dbgs() << "LV: Found an AND reduction PHI."<< *Phi <<"\n"); 2136 continue; 2137 } 2138 if (AddReductionVar(Phi, RK_IntegerXor)) { 2139 DEBUG(dbgs() << "LV: Found a XOR reduction PHI."<< *Phi <<"\n"); 2140 continue; 2141 } 2142 if (AddReductionVar(Phi, RK_FloatMult)) { 2143 DEBUG(dbgs() << "LV: Found an FMult reduction PHI."<< *Phi <<"\n"); 2144 continue; 2145 } 2146 if (AddReductionVar(Phi, RK_FloatAdd)) { 2147 DEBUG(dbgs() << "LV: Found an FAdd reduction PHI."<< *Phi <<"\n"); 2148 continue; 2149 } 2150 2151 DEBUG(dbgs() << "LV: Found an unidentified PHI."<< *Phi <<"\n"); 2152 return false; 2153 }// end of PHI handling 2154 2155 // We still don't handle functions. 2156 CallInst *CI = dyn_cast<CallInst>(it); 2157 if (CI && !isTriviallyVectorizableIntrinsic(it)) { 2158 DEBUG(dbgs() << "LV: Found a call site.\n"); 2159 return false; 2160 } 2161 2162 // Check that the instruction return type is vectorizable. 2163 if (!VectorType::isValidElementType(it->getType()) && 2164 !it->getType()->isVoidTy()) { 2165 DEBUG(dbgs() << "LV: Found unvectorizable type." << "\n"); 2166 return false; 2167 } 2168 2169 // Check that the stored type is vectorizable. 2170 if (StoreInst *ST = dyn_cast<StoreInst>(it)) { 2171 Type *T = ST->getValueOperand()->getType(); 2172 if (!VectorType::isValidElementType(T)) 2173 return false; 2174 } 2175 2176 // Reduction instructions are allowed to have exit users. 2177 // All other instructions must not have external users. 2178 if (!AllowedExit.count(it)) 2179 //Check that all of the users of the loop are inside the BB. 2180 for (Value::use_iterator I = it->use_begin(), E = it->use_end(); 2181 I != E; ++I) { 2182 Instruction *U = cast<Instruction>(*I); 2183 // This user may be a reduction exit value. 2184 if (!TheLoop->contains(U)) { 2185 DEBUG(dbgs() << "LV: Found an outside user for : "<< *U << "\n"); 2186 return false; 2187 } 2188 } 2189 } // next instr. 2190 2191 } 2192 2193 if (!Induction) { 2194 DEBUG(dbgs() << "LV: Did not find one integer induction var.\n"); 2195 assert(getInductionVars()->size() && "No induction variables"); 2196 } 2197 2198 return true; 2199} 2200 2201void LoopVectorizationLegality::collectLoopUniforms() { 2202 // We now know that the loop is vectorizable! 2203 // Collect variables that will remain uniform after vectorization. 2204 std::vector<Value*> Worklist; 2205 BasicBlock *Latch = TheLoop->getLoopLatch(); 2206 2207 // Start with the conditional branch and walk up the block. 2208 Worklist.push_back(Latch->getTerminator()->getOperand(0)); 2209 2210 while (Worklist.size()) { 2211 Instruction *I = dyn_cast<Instruction>(Worklist.back()); 2212 Worklist.pop_back(); 2213 2214 // Look at instructions inside this loop. 2215 // Stop when reaching PHI nodes. 2216 // TODO: we need to follow values all over the loop, not only in this block. 2217 if (!I || !TheLoop->contains(I) || isa<PHINode>(I)) 2218 continue; 2219 2220 // This is a known uniform. 2221 Uniforms.insert(I); 2222 2223 // Insert all operands. 2224 for (int i = 0, Op = I->getNumOperands(); i < Op; ++i) { 2225 Worklist.push_back(I->getOperand(i)); 2226 } 2227 } 2228} 2229 2230bool LoopVectorizationLegality::canVectorizeMemory() { 2231 typedef SmallVector<Value*, 16> ValueVector; 2232 typedef SmallPtrSet<Value*, 16> ValueSet; 2233 // Holds the Load and Store *instructions*. 2234 ValueVector Loads; 2235 ValueVector Stores; 2236 PtrRtCheck.Pointers.clear(); 2237 PtrRtCheck.Need = false; 2238 2239 // For each block. 2240 for (Loop::block_iterator bb = TheLoop->block_begin(), 2241 be = TheLoop->block_end(); bb != be; ++bb) { 2242 2243 // Scan the BB and collect legal loads and stores. 2244 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e; 2245 ++it) { 2246 2247 // If this is a load, save it. If this instruction can read from memory 2248 // but is not a load, then we quit. Notice that we don't handle function 2249 // calls that read or write. 2250 if (it->mayReadFromMemory()) { 2251 LoadInst *Ld = dyn_cast<LoadInst>(it); 2252 if (!Ld) return false; 2253 if (!Ld->isSimple()) { 2254 DEBUG(dbgs() << "LV: Found a non-simple load.\n"); 2255 return false; 2256 } 2257 Loads.push_back(Ld); 2258 continue; 2259 } 2260 2261 // Save 'store' instructions. Abort if other instructions write to memory. 2262 if (it->mayWriteToMemory()) { 2263 StoreInst *St = dyn_cast<StoreInst>(it); 2264 if (!St) return false; 2265 if (!St->isSimple()) { 2266 DEBUG(dbgs() << "LV: Found a non-simple store.\n"); 2267 return false; 2268 } 2269 Stores.push_back(St); 2270 } 2271 } // next instr. 2272 } // next block. 2273 2274 // Now we have two lists that hold the loads and the stores. 2275 // Next, we find the pointers that they use. 2276 2277 // Check if we see any stores. If there are no stores, then we don't 2278 // care if the pointers are *restrict*. 2279 if (!Stores.size()) { 2280 DEBUG(dbgs() << "LV: Found a read-only loop!\n"); 2281 return true; 2282 } 2283 2284 // Holds the read and read-write *pointers* that we find. 2285 ValueVector Reads; 2286 ValueVector ReadWrites; 2287 2288 // Holds the analyzed pointers. We don't want to call GetUnderlyingObjects 2289 // multiple times on the same object. If the ptr is accessed twice, once 2290 // for read and once for write, it will only appear once (on the write 2291 // list). This is okay, since we are going to check for conflicts between 2292 // writes and between reads and writes, but not between reads and reads. 2293 ValueSet Seen; 2294 2295 ValueVector::iterator I, IE; 2296 for (I = Stores.begin(), IE = Stores.end(); I != IE; ++I) { 2297 StoreInst *ST = cast<StoreInst>(*I); 2298 Value* Ptr = ST->getPointerOperand(); 2299 2300 if (isUniform(Ptr)) { 2301 DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n"); 2302 return false; 2303 } 2304 2305 // If we did *not* see this pointer before, insert it to 2306 // the read-write list. At this phase it is only a 'write' list. 2307 if (Seen.insert(Ptr)) 2308 ReadWrites.push_back(Ptr); 2309 } 2310 2311 for (I = Loads.begin(), IE = Loads.end(); I != IE; ++I) { 2312 LoadInst *LD = cast<LoadInst>(*I); 2313 Value* Ptr = LD->getPointerOperand(); 2314 // If we did *not* see this pointer before, insert it to the 2315 // read list. If we *did* see it before, then it is already in 2316 // the read-write list. This allows us to vectorize expressions 2317 // such as A[i] += x; Because the address of A[i] is a read-write 2318 // pointer. This only works if the index of A[i] is consecutive. 2319 // If the address of i is unknown (for example A[B[i]]) then we may 2320 // read a few words, modify, and write a few words, and some of the 2321 // words may be written to the same address. 2322 if (Seen.insert(Ptr) || 0 == isConsecutivePtr(Ptr)) 2323 Reads.push_back(Ptr); 2324 } 2325 2326 // If we write (or read-write) to a single destination and there are no 2327 // other reads in this loop then is it safe to vectorize. 2328 if (ReadWrites.size() == 1 && Reads.size() == 0) { 2329 DEBUG(dbgs() << "LV: Found a write-only loop!\n"); 2330 return true; 2331 } 2332 2333 // Find pointers with computable bounds. We are going to use this information 2334 // to place a runtime bound check. 2335 bool CanDoRT = true; 2336 for (I = ReadWrites.begin(), IE = ReadWrites.end(); I != IE; ++I) 2337 if (hasComputableBounds(*I)) { 2338 PtrRtCheck.insert(SE, TheLoop, *I); 2339 DEBUG(dbgs() << "LV: Found a runtime check ptr:" << **I <<"\n"); 2340 } else { 2341 CanDoRT = false; 2342 break; 2343 } 2344 for (I = Reads.begin(), IE = Reads.end(); I != IE; ++I) 2345 if (hasComputableBounds(*I)) { 2346 PtrRtCheck.insert(SE, TheLoop, *I); 2347 DEBUG(dbgs() << "LV: Found a runtime check ptr:" << **I <<"\n"); 2348 } else { 2349 CanDoRT = false; 2350 break; 2351 } 2352 2353 // Check that we did not collect too many pointers or found a 2354 // unsizeable pointer. 2355 if (!CanDoRT || PtrRtCheck.Pointers.size() > RuntimeMemoryCheckThreshold) { 2356 PtrRtCheck.reset(); 2357 CanDoRT = false; 2358 } 2359 2360 if (CanDoRT) { 2361 DEBUG(dbgs() << "LV: We can perform a memory runtime check if needed.\n"); 2362 } 2363 2364 bool NeedRTCheck = false; 2365 2366 // Now that the pointers are in two lists (Reads and ReadWrites), we 2367 // can check that there are no conflicts between each of the writes and 2368 // between the writes to the reads. 2369 ValueSet WriteObjects; 2370 ValueVector TempObjects; 2371 2372 // Check that the read-writes do not conflict with other read-write 2373 // pointers. 2374 bool AllWritesIdentified = true; 2375 for (I = ReadWrites.begin(), IE = ReadWrites.end(); I != IE; ++I) { 2376 GetUnderlyingObjects(*I, TempObjects, DL); 2377 for (ValueVector::iterator it=TempObjects.begin(), e=TempObjects.end(); 2378 it != e; ++it) { 2379 if (!isIdentifiedObject(*it)) { 2380 DEBUG(dbgs() << "LV: Found an unidentified write ptr:"<< **it <<"\n"); 2381 NeedRTCheck = true; 2382 AllWritesIdentified = false; 2383 } 2384 if (!WriteObjects.insert(*it)) { 2385 DEBUG(dbgs() << "LV: Found a possible write-write reorder:" 2386 << **it <<"\n"); 2387 return false; 2388 } 2389 } 2390 TempObjects.clear(); 2391 } 2392 2393 /// Check that the reads don't conflict with the read-writes. 2394 for (I = Reads.begin(), IE = Reads.end(); I != IE; ++I) { 2395 GetUnderlyingObjects(*I, TempObjects, DL); 2396 for (ValueVector::iterator it=TempObjects.begin(), e=TempObjects.end(); 2397 it != e; ++it) { 2398 // If all of the writes are identified then we don't care if the read 2399 // pointer is identified or not. 2400 if (!AllWritesIdentified && !isIdentifiedObject(*it)) { 2401 DEBUG(dbgs() << "LV: Found an unidentified read ptr:"<< **it <<"\n"); 2402 NeedRTCheck = true; 2403 } 2404 if (WriteObjects.count(*it)) { 2405 DEBUG(dbgs() << "LV: Found a possible read/write reorder:" 2406 << **it <<"\n"); 2407 return false; 2408 } 2409 } 2410 TempObjects.clear(); 2411 } 2412 2413 PtrRtCheck.Need = NeedRTCheck; 2414 if (NeedRTCheck && !CanDoRT) { 2415 DEBUG(dbgs() << "LV: We can't vectorize because we can't find " << 2416 "the array bounds.\n"); 2417 PtrRtCheck.reset(); 2418 return false; 2419 } 2420 2421 DEBUG(dbgs() << "LV: We "<< (NeedRTCheck ? "" : "don't") << 2422 " need a runtime memory check.\n"); 2423 return true; 2424} 2425 2426bool LoopVectorizationLegality::AddReductionVar(PHINode *Phi, 2427 ReductionKind Kind) { 2428 if (Phi->getNumIncomingValues() != 2) 2429 return false; 2430 2431 // Reduction variables are only found in the loop header block. 2432 if (Phi->getParent() != TheLoop->getHeader()) 2433 return false; 2434 2435 // Obtain the reduction start value from the value that comes from the loop 2436 // preheader. 2437 Value *RdxStart = Phi->getIncomingValueForBlock(TheLoop->getLoopPreheader()); 2438 2439 // ExitInstruction is the single value which is used outside the loop. 2440 // We only allow for a single reduction value to be used outside the loop. 2441 // This includes users of the reduction, variables (which form a cycle 2442 // which ends in the phi node). 2443 Instruction *ExitInstruction = 0; 2444 // Indicates that we found a binary operation in our scan. 2445 bool FoundBinOp = false; 2446 2447 // Iter is our iterator. We start with the PHI node and scan for all of the 2448 // users of this instruction. All users must be instructions that can be 2449 // used as reduction variables (such as ADD). We may have a single 2450 // out-of-block user. The cycle must end with the original PHI. 2451 Instruction *Iter = Phi; 2452 while (true) { 2453 // If the instruction has no users then this is a broken 2454 // chain and can't be a reduction variable. 2455 if (Iter->use_empty()) 2456 return false; 2457 2458 // Did we find a user inside this loop already ? 2459 bool FoundInBlockUser = false; 2460 // Did we reach the initial PHI node already ? 2461 bool FoundStartPHI = false; 2462 2463 // Is this a bin op ? 2464 FoundBinOp |= !isa<PHINode>(Iter); 2465 2466 // For each of the *users* of iter. 2467 for (Value::use_iterator it = Iter->use_begin(), e = Iter->use_end(); 2468 it != e; ++it) { 2469 Instruction *U = cast<Instruction>(*it); 2470 // We already know that the PHI is a user. 2471 if (U == Phi) { 2472 FoundStartPHI = true; 2473 continue; 2474 } 2475 2476 // Check if we found the exit user. 2477 BasicBlock *Parent = U->getParent(); 2478 if (!TheLoop->contains(Parent)) { 2479 // Exit if you find multiple outside users. 2480 if (ExitInstruction != 0) 2481 return false; 2482 ExitInstruction = Iter; 2483 } 2484 2485 // We allow in-loop PHINodes which are not the original reduction PHI 2486 // node. If this PHI is the only user of Iter (happens in IF w/ no ELSE 2487 // structure) then don't skip this PHI. 2488 if (isa<PHINode>(Iter) && isa<PHINode>(U) && 2489 U->getParent() != TheLoop->getHeader() && 2490 TheLoop->contains(U) && 2491 Iter->getNumUses() > 1) 2492 continue; 2493 2494 // We can't have multiple inside users. 2495 if (FoundInBlockUser) 2496 return false; 2497 FoundInBlockUser = true; 2498 2499 // Any reduction instr must be of one of the allowed kinds. 2500 if (!isReductionInstr(U, Kind)) 2501 return false; 2502 2503 // Reductions of instructions such as Div, and Sub is only 2504 // possible if the LHS is the reduction variable. 2505 if (!U->isCommutative() && !isa<PHINode>(U) && U->getOperand(0) != Iter) 2506 return false; 2507 2508 Iter = U; 2509 } 2510 2511 // We found a reduction var if we have reached the original 2512 // phi node and we only have a single instruction with out-of-loop 2513 // users. 2514 if (FoundStartPHI) { 2515 // This instruction is allowed to have out-of-loop users. 2516 AllowedExit.insert(ExitInstruction); 2517 2518 // Save the description of this reduction variable. 2519 ReductionDescriptor RD(RdxStart, ExitInstruction, Kind); 2520 Reductions[Phi] = RD; 2521 // We've ended the cycle. This is a reduction variable if we have an 2522 // outside user and it has a binary op. 2523 return FoundBinOp && ExitInstruction; 2524 } 2525 } 2526} 2527 2528bool 2529LoopVectorizationLegality::isReductionInstr(Instruction *I, 2530 ReductionKind Kind) { 2531 bool FP = I->getType()->isFloatingPointTy(); 2532 bool FastMath = (FP && I->isCommutative() && I->isAssociative()); 2533 2534 switch (I->getOpcode()) { 2535 default: 2536 return false; 2537 case Instruction::PHI: 2538 if (FP && (Kind != RK_FloatMult && Kind != RK_FloatAdd)) 2539 return false; 2540 // possibly. 2541 return true; 2542 case Instruction::Sub: 2543 case Instruction::Add: 2544 return Kind == RK_IntegerAdd; 2545 case Instruction::SDiv: 2546 case Instruction::UDiv: 2547 case Instruction::Mul: 2548 return Kind == RK_IntegerMult; 2549 case Instruction::And: 2550 return Kind == RK_IntegerAnd; 2551 case Instruction::Or: 2552 return Kind == RK_IntegerOr; 2553 case Instruction::Xor: 2554 return Kind == RK_IntegerXor; 2555 case Instruction::FMul: 2556 return Kind == RK_FloatMult && FastMath; 2557 case Instruction::FAdd: 2558 return Kind == RK_FloatAdd && FastMath; 2559 } 2560} 2561 2562LoopVectorizationLegality::InductionKind 2563LoopVectorizationLegality::isInductionVariable(PHINode *Phi) { 2564 Type *PhiTy = Phi->getType(); 2565 // We only handle integer and pointer inductions variables. 2566 if (!PhiTy->isIntegerTy() && !PhiTy->isPointerTy()) 2567 return NoInduction; 2568 2569 // Check that the PHI is consecutive and starts at zero. 2570 const SCEV *PhiScev = SE->getSCEV(Phi); 2571 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev); 2572 if (!AR) { 2573 DEBUG(dbgs() << "LV: PHI is not a poly recurrence.\n"); 2574 return NoInduction; 2575 } 2576 const SCEV *Step = AR->getStepRecurrence(*SE); 2577 2578 // Integer inductions need to have a stride of one. 2579 if (PhiTy->isIntegerTy()) { 2580 if (Step->isOne()) 2581 return IntInduction; 2582 if (Step->isAllOnesValue()) 2583 return ReverseIntInduction; 2584 return NoInduction; 2585 } 2586 2587 // Calculate the pointer stride and check if it is consecutive. 2588 const SCEVConstant *C = dyn_cast<SCEVConstant>(Step); 2589 if (!C) 2590 return NoInduction; 2591 2592 assert(PhiTy->isPointerTy() && "The PHI must be a pointer"); 2593 uint64_t Size = DL->getTypeAllocSize(PhiTy->getPointerElementType()); 2594 if (C->getValue()->equalsInt(Size)) 2595 return PtrInduction; 2596 2597 return NoInduction; 2598} 2599 2600bool LoopVectorizationLegality::isInductionVariable(const Value *V) { 2601 Value *In0 = const_cast<Value*>(V); 2602 PHINode *PN = dyn_cast_or_null<PHINode>(In0); 2603 if (!PN) 2604 return false; 2605 2606 return Inductions.count(PN); 2607} 2608 2609bool LoopVectorizationLegality::blockNeedsPredication(BasicBlock *BB) { 2610 assert(TheLoop->contains(BB) && "Unknown block used"); 2611 2612 // Blocks that do not dominate the latch need predication. 2613 BasicBlock* Latch = TheLoop->getLoopLatch(); 2614 return !DT->dominates(BB, Latch); 2615} 2616 2617bool LoopVectorizationLegality::blockCanBePredicated(BasicBlock *BB) { 2618 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 2619 // We don't predicate loads/stores at the moment. 2620 if (it->mayReadFromMemory() || it->mayWriteToMemory() || it->mayThrow()) 2621 return false; 2622 2623 // The instructions below can trap. 2624 switch (it->getOpcode()) { 2625 default: continue; 2626 case Instruction::UDiv: 2627 case Instruction::SDiv: 2628 case Instruction::URem: 2629 case Instruction::SRem: 2630 return false; 2631 } 2632 } 2633 2634 return true; 2635} 2636 2637bool LoopVectorizationLegality::hasComputableBounds(Value *Ptr) { 2638 const SCEV *PhiScev = SE->getSCEV(Ptr); 2639 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev); 2640 if (!AR) 2641 return false; 2642 2643 return AR->isAffine(); 2644} 2645 2646unsigned 2647LoopVectorizationCostModel::selectVectorizationFactor(bool OptForSize, 2648 unsigned UserVF) { 2649 if (OptForSize && Legal->getRuntimePointerCheck()->Need) { 2650 DEBUG(dbgs() << "LV: Aborting. Runtime ptr check is required in Os.\n"); 2651 return 1; 2652 } 2653 2654 // Find the trip count. 2655 unsigned TC = SE->getSmallConstantTripCount(TheLoop, TheLoop->getLoopLatch()); 2656 DEBUG(dbgs() << "LV: Found trip count:"<<TC<<"\n"); 2657 2658 unsigned VF = MaxVectorSize; 2659 2660 // If we optimize the program for size, avoid creating the tail loop. 2661 if (OptForSize) { 2662 // If we are unable to calculate the trip count then don't try to vectorize. 2663 if (TC < 2) { 2664 DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n"); 2665 return 1; 2666 } 2667 2668 // Find the maximum SIMD width that can fit within the trip count. 2669 VF = TC % MaxVectorSize; 2670 2671 if (VF == 0) 2672 VF = MaxVectorSize; 2673 2674 // If the trip count that we found modulo the vectorization factor is not 2675 // zero then we require a tail. 2676 if (VF < 2) { 2677 DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n"); 2678 return 1; 2679 } 2680 } 2681 2682 if (UserVF != 0) { 2683 assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two"); 2684 DEBUG(dbgs() << "LV: Using user VF "<<UserVF<<".\n"); 2685 2686 return UserVF; 2687 } 2688 2689 float Cost = expectedCost(1); 2690 unsigned Width = 1; 2691 DEBUG(dbgs() << "LV: Scalar loop costs: "<< (int)Cost << ".\n"); 2692 for (unsigned i=2; i <= VF; i*=2) { 2693 // Notice that the vector loop needs to be executed less times, so 2694 // we need to divide the cost of the vector loops by the width of 2695 // the vector elements. 2696 float VectorCost = expectedCost(i) / (float)i; 2697 DEBUG(dbgs() << "LV: Vector loop of width "<< i << " costs: " << 2698 (int)VectorCost << ".\n"); 2699 if (VectorCost < Cost) { 2700 Cost = VectorCost; 2701 Width = i; 2702 } 2703 } 2704 2705 DEBUG(dbgs() << "LV: Selecting VF = : "<< Width << ".\n"); 2706 return Width; 2707} 2708 2709unsigned 2710LoopVectorizationCostModel::selectUnrollFactor(bool OptForSize, 2711 unsigned UserUF) { 2712 // Use the user preference, unless 'auto' is selected. 2713 if (UserUF != 0) 2714 return UserUF; 2715 2716 // When we optimize for size we don't unroll. 2717 if (OptForSize) 2718 return 1; 2719 2720 // Do not unroll loops with a relatively small trip count. 2721 unsigned TC = SE->getSmallConstantTripCount(TheLoop, 2722 TheLoop->getLoopLatch()); 2723 if (TC > 1 && TC < TinyTripCountUnrollThreshold) 2724 return 1; 2725 2726 unsigned TargetVectorRegisters = TTI.getNumberOfRegisters(true); 2727 DEBUG(dbgs() << "LV: The target has " << TargetVectorRegisters << 2728 " vector registers\n"); 2729 2730 LoopVectorizationCostModel::RegisterUsage R = calculateRegisterUsage(); 2731 // We divide by these constants so assume that we have at least one 2732 // instruction that uses at least one register. 2733 R.MaxLocalUsers = std::max(R.MaxLocalUsers, 1U); 2734 R.NumInstructions = std::max(R.NumInstructions, 1U); 2735 2736 // We calculate the unroll factor using the following formula. 2737 // Subtract the number of loop invariants from the number of available 2738 // registers. These registers are used by all of the unrolled instances. 2739 // Next, divide the remaining registers by the number of registers that is 2740 // required by the loop, in order to estimate how many parallel instances 2741 // fit without causing spills. 2742 unsigned UF = (TargetVectorRegisters - R.LoopInvariantRegs) / R.MaxLocalUsers; 2743 2744 // We don't want to unroll the loops to the point where they do not fit into 2745 // the decoded cache. Assume that we only allow 32 IR instructions. 2746 UF = std::min(UF, (MaxLoopSizeThreshold / R.NumInstructions)); 2747 2748 // Clamp the unroll factor ranges to reasonable factors. 2749 if (UF > MaxUnrollSize) 2750 UF = MaxUnrollSize; 2751 else if (UF < 1) 2752 UF = 1; 2753 2754 return UF; 2755} 2756 2757LoopVectorizationCostModel::RegisterUsage 2758LoopVectorizationCostModel::calculateRegisterUsage() { 2759 // This function calculates the register usage by measuring the highest number 2760 // of values that are alive at a single location. Obviously, this is a very 2761 // rough estimation. We scan the loop in a topological order in order and 2762 // assign a number to each instruction. We use RPO to ensure that defs are 2763 // met before their users. We assume that each instruction that has in-loop 2764 // users starts an interval. We record every time that an in-loop value is 2765 // used, so we have a list of the first and last occurrences of each 2766 // instruction. Next, we transpose this data structure into a multi map that 2767 // holds the list of intervals that *end* at a specific location. This multi 2768 // map allows us to perform a linear search. We scan the instructions linearly 2769 // and record each time that a new interval starts, by placing it in a set. 2770 // If we find this value in the multi-map then we remove it from the set. 2771 // The max register usage is the maximum size of the set. 2772 // We also search for instructions that are defined outside the loop, but are 2773 // used inside the loop. We need this number separately from the max-interval 2774 // usage number because when we unroll, loop-invariant values do not take 2775 // more register. 2776 LoopBlocksDFS DFS(TheLoop); 2777 DFS.perform(LI); 2778 2779 RegisterUsage R; 2780 R.NumInstructions = 0; 2781 2782 // Each 'key' in the map opens a new interval. The values 2783 // of the map are the index of the 'last seen' usage of the 2784 // instruction that is the key. 2785 typedef DenseMap<Instruction*, unsigned> IntervalMap; 2786 // Maps instruction to its index. 2787 DenseMap<unsigned, Instruction*> IdxToInstr; 2788 // Marks the end of each interval. 2789 IntervalMap EndPoint; 2790 // Saves the list of instruction indices that are used in the loop. 2791 SmallSet<Instruction*, 8> Ends; 2792 // Saves the list of values that are used in the loop but are 2793 // defined outside the loop, such as arguments and constants. 2794 SmallPtrSet<Value*, 8> LoopInvariants; 2795 2796 unsigned Index = 0; 2797 for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(), 2798 be = DFS.endRPO(); bb != be; ++bb) { 2799 R.NumInstructions += (*bb)->size(); 2800 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e; 2801 ++it) { 2802 Instruction *I = it; 2803 IdxToInstr[Index++] = I; 2804 2805 // Save the end location of each USE. 2806 for (unsigned i = 0; i < I->getNumOperands(); ++i) { 2807 Value *U = I->getOperand(i); 2808 Instruction *Instr = dyn_cast<Instruction>(U); 2809 2810 // Ignore non-instruction values such as arguments, constants, etc. 2811 if (!Instr) continue; 2812 2813 // If this instruction is outside the loop then record it and continue. 2814 if (!TheLoop->contains(Instr)) { 2815 LoopInvariants.insert(Instr); 2816 continue; 2817 } 2818 2819 // Overwrite previous end points. 2820 EndPoint[Instr] = Index; 2821 Ends.insert(Instr); 2822 } 2823 } 2824 } 2825 2826 // Saves the list of intervals that end with the index in 'key'. 2827 typedef SmallVector<Instruction*, 2> InstrList; 2828 DenseMap<unsigned, InstrList> TransposeEnds; 2829 2830 // Transpose the EndPoints to a list of values that end at each index. 2831 for (IntervalMap::iterator it = EndPoint.begin(), e = EndPoint.end(); 2832 it != e; ++it) 2833 TransposeEnds[it->second].push_back(it->first); 2834 2835 SmallSet<Instruction*, 8> OpenIntervals; 2836 unsigned MaxUsage = 0; 2837 2838 2839 DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n"); 2840 for (unsigned int i = 0; i < Index; ++i) { 2841 Instruction *I = IdxToInstr[i]; 2842 // Ignore instructions that are never used within the loop. 2843 if (!Ends.count(I)) continue; 2844 2845 // Remove all of the instructions that end at this location. 2846 InstrList &List = TransposeEnds[i]; 2847 for (unsigned int j=0, e = List.size(); j < e; ++j) 2848 OpenIntervals.erase(List[j]); 2849 2850 // Count the number of live interals. 2851 MaxUsage = std::max(MaxUsage, OpenIntervals.size()); 2852 2853 DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " << 2854 OpenIntervals.size() <<"\n"); 2855 2856 // Add the current instruction to the list of open intervals. 2857 OpenIntervals.insert(I); 2858 } 2859 2860 unsigned Invariant = LoopInvariants.size(); 2861 DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsage << " \n"); 2862 DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << " \n"); 2863 DEBUG(dbgs() << "LV(REG): LoopSize: " << R.NumInstructions << " \n"); 2864 2865 R.LoopInvariantRegs = Invariant; 2866 R.MaxLocalUsers = MaxUsage; 2867 return R; 2868} 2869 2870unsigned LoopVectorizationCostModel::expectedCost(unsigned VF) { 2871 unsigned Cost = 0; 2872 2873 // For each block. 2874 for (Loop::block_iterator bb = TheLoop->block_begin(), 2875 be = TheLoop->block_end(); bb != be; ++bb) { 2876 unsigned BlockCost = 0; 2877 BasicBlock *BB = *bb; 2878 2879 // For each instruction in the old loop. 2880 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) { 2881 unsigned C = getInstructionCost(it, VF); 2882 Cost += C; 2883 DEBUG(dbgs() << "LV: Found an estimated cost of "<< C <<" for VF " << 2884 VF << " For instruction: "<< *it << "\n"); 2885 } 2886 2887 // We assume that if-converted blocks have a 50% chance of being executed. 2888 // When the code is scalar then some of the blocks are avoided due to CF. 2889 // When the code is vectorized we execute all code paths. 2890 if (Legal->blockNeedsPredication(*bb) && VF == 1) 2891 BlockCost /= 2; 2892 2893 Cost += BlockCost; 2894 } 2895 2896 return Cost; 2897} 2898 2899unsigned 2900LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) { 2901 // If we know that this instruction will remain uniform, check the cost of 2902 // the scalar version. 2903 if (Legal->isUniformAfterVectorization(I)) 2904 VF = 1; 2905 2906 Type *RetTy = I->getType(); 2907 Type *VectorTy = ToVectorTy(RetTy, VF); 2908 2909 // TODO: We need to estimate the cost of intrinsic calls. 2910 switch (I->getOpcode()) { 2911 case Instruction::GetElementPtr: 2912 // We mark this instruction as zero-cost because scalar GEPs are usually 2913 // lowered to the intruction addressing mode. At the moment we don't 2914 // generate vector geps. 2915 return 0; 2916 case Instruction::Br: { 2917 return TTI.getCFInstrCost(I->getOpcode()); 2918 } 2919 case Instruction::PHI: 2920 //TODO: IF-converted IFs become selects. 2921 return 0; 2922 case Instruction::Add: 2923 case Instruction::FAdd: 2924 case Instruction::Sub: 2925 case Instruction::FSub: 2926 case Instruction::Mul: 2927 case Instruction::FMul: 2928 case Instruction::UDiv: 2929 case Instruction::SDiv: 2930 case Instruction::FDiv: 2931 case Instruction::URem: 2932 case Instruction::SRem: 2933 case Instruction::FRem: 2934 case Instruction::Shl: 2935 case Instruction::LShr: 2936 case Instruction::AShr: 2937 case Instruction::And: 2938 case Instruction::Or: 2939 case Instruction::Xor: 2940 return TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy); 2941 case Instruction::Select: { 2942 SelectInst *SI = cast<SelectInst>(I); 2943 const SCEV *CondSCEV = SE->getSCEV(SI->getCondition()); 2944 bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop)); 2945 Type *CondTy = SI->getCondition()->getType(); 2946 if (ScalarCond) 2947 CondTy = VectorType::get(CondTy, VF); 2948 2949 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy); 2950 } 2951 case Instruction::ICmp: 2952 case Instruction::FCmp: { 2953 Type *ValTy = I->getOperand(0)->getType(); 2954 VectorTy = ToVectorTy(ValTy, VF); 2955 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy); 2956 } 2957 case Instruction::Store: { 2958 StoreInst *SI = cast<StoreInst>(I); 2959 Type *ValTy = SI->getValueOperand()->getType(); 2960 VectorTy = ToVectorTy(ValTy, VF); 2961 2962 if (VF == 1) 2963 return TTI.getMemoryOpCost(I->getOpcode(), VectorTy, 2964 SI->getAlignment(), 2965 SI->getPointerAddressSpace()); 2966 2967 // Scalarized stores. 2968 int Stride = Legal->isConsecutivePtr(SI->getPointerOperand()); 2969 bool Reverse = Stride < 0; 2970 if (0 == Stride) { 2971 unsigned Cost = 0; 2972 2973 // The cost of extracting from the value vector and pointer vector. 2974 Type *PtrTy = ToVectorTy(I->getOperand(0)->getType(), VF); 2975 for (unsigned i = 0; i < VF; ++i) { 2976 Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, VectorTy, 2977 i); 2978 Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, PtrTy, i); 2979 } 2980 2981 // The cost of the scalar stores. 2982 Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), 2983 SI->getAlignment(), 2984 SI->getPointerAddressSpace()); 2985 return Cost; 2986 } 2987 2988 // Wide stores. 2989 unsigned Cost = TTI.getMemoryOpCost(I->getOpcode(), VectorTy, 2990 SI->getAlignment(), 2991 SI->getPointerAddressSpace()); 2992 if (Reverse) 2993 Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, 2994 VectorTy, 0); 2995 return Cost; 2996 } 2997 case Instruction::Load: { 2998 LoadInst *LI = cast<LoadInst>(I); 2999 3000 if (VF == 1) 3001 return TTI.getMemoryOpCost(I->getOpcode(), VectorTy, LI->getAlignment(), 3002 LI->getPointerAddressSpace()); 3003 3004 // Scalarized loads. 3005 int Stride = Legal->isConsecutivePtr(LI->getPointerOperand()); 3006 bool Reverse = Stride < 0; 3007 if (0 == Stride) { 3008 unsigned Cost = 0; 3009 Type *PtrTy = ToVectorTy(I->getOperand(0)->getType(), VF); 3010 3011 // The cost of extracting from the pointer vector. 3012 for (unsigned i = 0; i < VF; ++i) 3013 Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, PtrTy, i); 3014 3015 // The cost of inserting data to the result vector. 3016 for (unsigned i = 0; i < VF; ++i) 3017 Cost += TTI.getVectorInstrCost(Instruction::InsertElement, VectorTy, i); 3018 3019 // The cost of the scalar stores. 3020 Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), RetTy->getScalarType(), 3021 LI->getAlignment(), 3022 LI->getPointerAddressSpace()); 3023 return Cost; 3024 } 3025 3026 // Wide loads. 3027 unsigned Cost = TTI.getMemoryOpCost(I->getOpcode(), VectorTy, 3028 LI->getAlignment(), 3029 LI->getPointerAddressSpace()); 3030 if (Reverse) 3031 Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0); 3032 return Cost; 3033 } 3034 case Instruction::ZExt: 3035 case Instruction::SExt: 3036 case Instruction::FPToUI: 3037 case Instruction::FPToSI: 3038 case Instruction::FPExt: 3039 case Instruction::PtrToInt: 3040 case Instruction::IntToPtr: 3041 case Instruction::SIToFP: 3042 case Instruction::UIToFP: 3043 case Instruction::Trunc: 3044 case Instruction::FPTrunc: 3045 case Instruction::BitCast: { 3046 // We optimize the truncation of induction variable. 3047 // The cost of these is the same as the scalar operation. 3048 if (I->getOpcode() == Instruction::Trunc && 3049 Legal->isInductionVariable(I->getOperand(0))) 3050 return TTI.getCastInstrCost(I->getOpcode(), I->getType(), 3051 I->getOperand(0)->getType()); 3052 3053 Type *SrcVecTy = ToVectorTy(I->getOperand(0)->getType(), VF); 3054 return TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy); 3055 } 3056 case Instruction::Call: { 3057 assert(isTriviallyVectorizableIntrinsic(I)); 3058 IntrinsicInst *II = cast<IntrinsicInst>(I); 3059 Type *RetTy = ToVectorTy(II->getType(), VF); 3060 SmallVector<Type*, 4> Tys; 3061 for (unsigned i = 0, ie = II->getNumArgOperands(); i != ie; ++i) 3062 Tys.push_back(ToVectorTy(II->getArgOperand(i)->getType(), VF)); 3063 return TTI.getIntrinsicInstrCost(II->getIntrinsicID(), RetTy, Tys); 3064 } 3065 default: { 3066 // We are scalarizing the instruction. Return the cost of the scalar 3067 // instruction, plus the cost of insert and extract into vector 3068 // elements, times the vector width. 3069 unsigned Cost = 0; 3070 3071 if (!RetTy->isVoidTy() && VF != 1) { 3072 unsigned InsCost = TTI.getVectorInstrCost(Instruction::InsertElement, 3073 VectorTy); 3074 unsigned ExtCost = TTI.getVectorInstrCost(Instruction::ExtractElement, 3075 VectorTy); 3076 3077 // The cost of inserting the results plus extracting each one of the 3078 // operands. 3079 Cost += VF * (InsCost + ExtCost * I->getNumOperands()); 3080 } 3081 3082 // The cost of executing VF copies of the scalar instruction. This opcode 3083 // is unknown. Assume that it is the same as 'mul'. 3084 Cost += VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy); 3085 return Cost; 3086 } 3087 }// end of switch. 3088} 3089 3090Type* LoopVectorizationCostModel::ToVectorTy(Type *Scalar, unsigned VF) { 3091 if (Scalar->isVoidTy() || VF == 1) 3092 return Scalar; 3093 return VectorType::get(Scalar, VF); 3094} 3095 3096char LoopVectorize::ID = 0; 3097static const char lv_name[] = "Loop Vectorization"; 3098INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false) 3099INITIALIZE_AG_DEPENDENCY(AliasAnalysis) 3100INITIALIZE_AG_DEPENDENCY(TargetTransformInfo) 3101INITIALIZE_PASS_DEPENDENCY(ScalarEvolution) 3102INITIALIZE_PASS_DEPENDENCY(LoopSimplify) 3103INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false) 3104 3105namespace llvm { 3106 Pass *createLoopVectorizePass() { 3107 return new LoopVectorize(); 3108 } 3109} 3110 3111 3112