1 //===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===//
3 // The LLVM Compiler Infrastructure
5 // This file is distributed under the University of Illinois Open Source
6 // License. See LICENSE.TXT for details.
8 //===----------------------------------------------------------------------===//
10 // This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops
11 // and generates target-independent LLVM-IR.
12 // The vectorizer uses the TargetTransformInfo analysis to estimate the costs
13 // of instructions in order to estimate the profitability of vectorization.
15 // The loop vectorizer combines consecutive loop iterations into a single
16 // 'wide' iteration. After this transformation the index is incremented
17 // by the SIMD vector width, and not by one.
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.
29 //===----------------------------------------------------------------------===//
31 // The reduction-variable vectorization is based on the paper:
32 // D. Nuzman and R. Henderson. Multi-platform Auto-vectorization.
34 // Variable uniformity checks are inspired by:
35 // Karrenberg, R. and Hack, S. Whole Function Vectorization.
37 // Other ideas/concepts are from:
38 // A. Zaks and D. Nuzman. Autovectorization in GCC-two years later.
40 // S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of
41 // Vectorizing Compilers.
43 //===----------------------------------------------------------------------===//
45 #define LV_NAME "loop-vectorize"
46 #define DEBUG_TYPE LV_NAME
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/PatternMatch.h"
82 #include "llvm/Support/raw_ostream.h"
83 #include "llvm/Target/TargetLibraryInfo.h"
84 #include "llvm/Transforms/Scalar.h"
85 #include "llvm/Transforms/Utils/BasicBlockUtils.h"
86 #include "llvm/Transforms/Utils/Local.h"
91 using namespace llvm::PatternMatch;
93 static cl::opt<unsigned>
94 VectorizationFactor("force-vector-width", cl::init(0), cl::Hidden,
95 cl::desc("Sets the SIMD width. Zero is autoselect."));
97 static cl::opt<unsigned>
98 VectorizationUnroll("force-vector-unroll", cl::init(0), cl::Hidden,
99 cl::desc("Sets the vectorization unroll count. "
100 "Zero is autoselect."));
103 EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden,
104 cl::desc("Enable if-conversion during vectorization."));
106 /// We don't vectorize loops with a known constant trip count below this number.
107 static cl::opt<unsigned>
108 TinyTripCountVectorThreshold("vectorizer-min-trip-count", cl::init(16),
110 cl::desc("Don't vectorize loops with a constant "
111 "trip count that is smaller than this "
114 /// We don't unroll loops with a known constant trip count below this number.
115 static const unsigned TinyTripCountUnrollThreshold = 128;
117 /// When performing memory disambiguation checks at runtime do not make more
118 /// than this number of comparisons.
119 static const unsigned RuntimeMemoryCheckThreshold = 8;
121 /// We use a metadata with this name to indicate that a scalar loop was
122 /// vectorized and that we don't need to re-vectorize it if we run into it
125 AlreadyVectorizedMDName = "llvm.vectorizer.already_vectorized";
129 // Forward declarations.
130 class LoopVectorizationLegality;
131 class LoopVectorizationCostModel;
133 /// InnerLoopVectorizer vectorizes loops which contain only one basic
134 /// block to a specified vectorization factor (VF).
135 /// This class performs the widening of scalars into vectors, or multiple
136 /// scalars. This class also implements the following features:
137 /// * It inserts an epilogue loop for handling loops that don't have iteration
138 /// counts that are known to be a multiple of the vectorization factor.
139 /// * It handles the code generation for reduction variables.
140 /// * Scalarization (implementation using scalars) of un-vectorizable
142 /// InnerLoopVectorizer does not perform any vectorization-legality
143 /// checks, and relies on the caller to check for the different legality
144 /// aspects. The InnerLoopVectorizer relies on the
145 /// LoopVectorizationLegality class to provide information about the induction
146 /// and reduction variables that were found to a given vectorization factor.
147 class InnerLoopVectorizer {
149 InnerLoopVectorizer(Loop *OrigLoop, ScalarEvolution *SE, LoopInfo *LI,
150 DominatorTree *DT, DataLayout *DL,
151 const TargetLibraryInfo *TLI, unsigned VecWidth,
152 unsigned UnrollFactor)
153 : OrigLoop(OrigLoop), SE(SE), LI(LI), DT(DT), DL(DL), TLI(TLI),
154 VF(VecWidth), UF(UnrollFactor), Builder(SE->getContext()), Induction(0),
155 OldInduction(0), WidenMap(UnrollFactor) {}
157 // Perform the actual loop widening (vectorization).
158 void vectorize(LoopVectorizationLegality *Legal) {
159 // Create a new empty loop. Unlink the old loop and connect the new one.
160 createEmptyLoop(Legal);
161 // Widen each instruction in the old loop to a new one in the new loop.
162 // Use the Legality module to find the induction and reduction variables.
163 vectorizeLoop(Legal);
164 // Register the new loop and update the analysis passes.
169 /// A small list of PHINodes.
170 typedef SmallVector<PHINode*, 4> PhiVector;
171 /// When we unroll loops we have multiple vector values for each scalar.
172 /// This data structure holds the unrolled and vectorized values that
173 /// originated from one scalar instruction.
174 typedef SmallVector<Value*, 2> VectorParts;
176 /// Add code that checks at runtime if the accessed arrays overlap.
177 /// Returns the comparator value or NULL if no check is needed.
178 Instruction *addRuntimeCheck(LoopVectorizationLegality *Legal,
180 /// Create an empty loop, based on the loop ranges of the old loop.
181 void createEmptyLoop(LoopVectorizationLegality *Legal);
182 /// Copy and widen the instructions from the old loop.
183 void vectorizeLoop(LoopVectorizationLegality *Legal);
185 /// A helper function that computes the predicate of the block BB, assuming
186 /// that the header block of the loop is set to True. It returns the *entry*
187 /// mask for the block BB.
188 VectorParts createBlockInMask(BasicBlock *BB);
189 /// A helper function that computes the predicate of the edge between SRC
191 VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst);
193 /// A helper function to vectorize a single BB within the innermost loop.
194 void vectorizeBlockInLoop(LoopVectorizationLegality *Legal, BasicBlock *BB,
197 /// Insert the new loop to the loop hierarchy and pass manager
198 /// and update the analysis passes.
199 void updateAnalysis();
201 /// This instruction is un-vectorizable. Implement it as a sequence
203 void scalarizeInstruction(Instruction *Instr);
205 /// Vectorize Load and Store instructions,
206 void vectorizeMemoryInstruction(Instruction *Instr,
207 LoopVectorizationLegality *Legal);
209 /// Create a broadcast instruction. This method generates a broadcast
210 /// instruction (shuffle) for loop invariant values and for the induction
211 /// value. If this is the induction variable then we extend it to N, N+1, ...
212 /// this is needed because each iteration in the loop corresponds to a SIMD
214 Value *getBroadcastInstrs(Value *V);
216 /// This function adds 0, 1, 2 ... to each vector element, starting at zero.
217 /// If Negate is set then negative numbers are added e.g. (0, -1, -2, ...).
218 /// The sequence starts at StartIndex.
219 Value *getConsecutiveVector(Value* Val, int StartIdx, bool Negate);
221 /// When we go over instructions in the basic block we rely on previous
222 /// values within the current basic block or on loop invariant values.
223 /// When we widen (vectorize) values we place them in the map. If the values
224 /// are not within the map, they have to be loop invariant, so we simply
225 /// broadcast them into a vector.
226 VectorParts &getVectorValue(Value *V);
228 /// Generate a shuffle sequence that will reverse the vector Vec.
229 Value *reverseVector(Value *Vec);
231 /// This is a helper class that holds the vectorizer state. It maps scalar
232 /// instructions to vector instructions. When the code is 'unrolled' then
233 /// then a single scalar value is mapped to multiple vector parts. The parts
234 /// are stored in the VectorPart type.
236 /// C'tor. UnrollFactor controls the number of vectors ('parts') that
238 ValueMap(unsigned UnrollFactor) : UF(UnrollFactor) {}
240 /// \return True if 'Key' is saved in the Value Map.
241 bool has(Value *Key) const { return MapStorage.count(Key); }
243 /// Initializes a new entry in the map. Sets all of the vector parts to the
244 /// save value in 'Val'.
245 /// \return A reference to a vector with splat values.
246 VectorParts &splat(Value *Key, Value *Val) {
247 VectorParts &Entry = MapStorage[Key];
248 Entry.assign(UF, Val);
252 ///\return A reference to the value that is stored at 'Key'.
253 VectorParts &get(Value *Key) {
254 VectorParts &Entry = MapStorage[Key];
257 assert(Entry.size() == UF);
262 /// The unroll factor. Each entry in the map stores this number of vector
266 /// Map storage. We use std::map and not DenseMap because insertions to a
267 /// dense map invalidates its iterators.
268 std::map<Value *, VectorParts> MapStorage;
271 /// The original loop.
273 /// Scev analysis to use.
281 /// Target Library Info.
282 const TargetLibraryInfo *TLI;
284 /// The vectorization SIMD factor to use. Each vector will have this many
287 /// The vectorization unroll factor to use. Each scalar is vectorized to this
288 /// many different vector instructions.
291 /// The builder that we use
294 // --- Vectorization state ---
296 /// The vector-loop preheader.
297 BasicBlock *LoopVectorPreHeader;
298 /// The scalar-loop preheader.
299 BasicBlock *LoopScalarPreHeader;
300 /// Middle Block between the vector and the scalar.
301 BasicBlock *LoopMiddleBlock;
302 ///The ExitBlock of the scalar loop.
303 BasicBlock *LoopExitBlock;
304 ///The vector loop body.
305 BasicBlock *LoopVectorBody;
306 ///The scalar loop body.
307 BasicBlock *LoopScalarBody;
308 /// A list of all bypass blocks. The first block is the entry of the loop.
309 SmallVector<BasicBlock *, 4> LoopBypassBlocks;
311 /// The new Induction variable which was added to the new block.
313 /// The induction variable of the old basic block.
314 PHINode *OldInduction;
315 /// Maps scalars to widened vectors.
319 /// LoopVectorizationLegality checks if it is legal to vectorize a loop, and
320 /// to what vectorization factor.
321 /// This class does not look at the profitability of vectorization, only the
322 /// legality. This class has two main kinds of checks:
323 /// * Memory checks - The code in canVectorizeMemory checks if vectorization
324 /// will change the order of memory accesses in a way that will change the
325 /// correctness of the program.
326 /// * Scalars checks - The code in canVectorizeInstrs and canVectorizeMemory
327 /// checks for a number of different conditions, such as the availability of a
328 /// single induction variable, that all types are supported and vectorize-able,
329 /// etc. This code reflects the capabilities of InnerLoopVectorizer.
330 /// This class is also used by InnerLoopVectorizer for identifying
331 /// induction variable and the different reduction variables.
332 class LoopVectorizationLegality {
334 LoopVectorizationLegality(Loop *L, ScalarEvolution *SE, DataLayout *DL,
335 DominatorTree *DT, TargetTransformInfo* TTI,
336 AliasAnalysis *AA, TargetLibraryInfo *TLI)
337 : TheLoop(L), SE(SE), DL(DL), DT(DT), TTI(TTI), AA(AA), TLI(TLI),
338 Induction(0), HasFunNoNaNAttr(false) {}
340 /// This enum represents the kinds of reductions that we support.
342 RK_NoReduction, ///< Not a reduction.
343 RK_IntegerAdd, ///< Sum of integers.
344 RK_IntegerMult, ///< Product of integers.
345 RK_IntegerOr, ///< Bitwise or logical OR of numbers.
346 RK_IntegerAnd, ///< Bitwise or logical AND of numbers.
347 RK_IntegerXor, ///< Bitwise or logical XOR of numbers.
348 RK_IntegerMinMax, ///< Min/max implemented in terms of select(cmp()).
349 RK_FloatAdd, ///< Sum of floats.
350 RK_FloatMult, ///< Product of floats.
351 RK_FloatMinMax ///< Min/max implemented in terms of select(cmp()).
354 /// This enum represents the kinds of inductions that we support.
356 IK_NoInduction, ///< Not an induction variable.
357 IK_IntInduction, ///< Integer induction variable. Step = 1.
358 IK_ReverseIntInduction, ///< Reverse int induction variable. Step = -1.
359 IK_PtrInduction, ///< Pointer induction var. Step = sizeof(elem).
360 IK_ReversePtrInduction ///< Reverse ptr indvar. Step = - sizeof(elem).
363 // This enum represents the kind of minmax reduction.
364 enum MinMaxReductionKind {
374 /// This POD struct holds information about reduction variables.
375 struct ReductionDescriptor {
376 ReductionDescriptor() : StartValue(0), LoopExitInstr(0),
377 Kind(RK_NoReduction), MinMaxKind(MRK_Invalid) {}
379 ReductionDescriptor(Value *Start, Instruction *Exit, ReductionKind K,
380 MinMaxReductionKind MK)
381 : StartValue(Start), LoopExitInstr(Exit), Kind(K), MinMaxKind(MK) {}
383 // The starting value of the reduction.
384 // It does not have to be zero!
386 // The instruction who's value is used outside the loop.
387 Instruction *LoopExitInstr;
388 // The kind of the reduction.
390 // If this a min/max reduction the kind of reduction.
391 MinMaxReductionKind MinMaxKind;
394 /// This POD struct holds information about a potential reduction operation.
395 struct ReductionInstDesc {
396 ReductionInstDesc(bool IsRedux, Instruction *I) :
397 IsReduction(IsRedux), PatternLastInst(I), MinMaxKind(MRK_Invalid) {}
399 ReductionInstDesc(Instruction *I, MinMaxReductionKind K) :
400 IsReduction(true), PatternLastInst(I), MinMaxKind(K) {}
402 // Is this instruction a reduction candidate.
404 // The last instruction in a min/max pattern (select of the select(icmp())
405 // pattern), or the current reduction instruction otherwise.
406 Instruction *PatternLastInst;
407 // If this is a min/max pattern the comparison predicate.
408 MinMaxReductionKind MinMaxKind;
411 // This POD struct holds information about the memory runtime legality
412 // check that a group of pointers do not overlap.
413 struct RuntimePointerCheck {
414 RuntimePointerCheck() : Need(false) {}
416 /// Reset the state of the pointer runtime information.
424 /// Insert a pointer and calculate the start and end SCEVs.
425 void insert(ScalarEvolution *SE, Loop *Lp, Value *Ptr, bool WritePtr);
427 /// This flag indicates if we need to add the runtime check.
429 /// Holds the pointers that we need to check.
430 SmallVector<Value*, 2> Pointers;
431 /// Holds the pointer value at the beginning of the loop.
432 SmallVector<const SCEV*, 2> Starts;
433 /// Holds the pointer value at the end of the loop.
434 SmallVector<const SCEV*, 2> Ends;
435 /// Holds the information if this pointer is used for writing to memory.
436 SmallVector<bool, 2> IsWritePtr;
439 /// A POD for saving information about induction variables.
440 struct InductionInfo {
441 InductionInfo(Value *Start, InductionKind K) : StartValue(Start), IK(K) {}
442 InductionInfo() : StartValue(0), IK(IK_NoInduction) {}
449 /// ReductionList contains the reduction descriptors for all
450 /// of the reductions that were found in the loop.
451 typedef DenseMap<PHINode*, ReductionDescriptor> ReductionList;
453 /// InductionList saves induction variables and maps them to the
454 /// induction descriptor.
455 typedef MapVector<PHINode*, InductionInfo> InductionList;
457 /// Alias(Multi)Map stores the values (GEPs or underlying objects and their
458 /// respective Store/Load instruction(s) to calculate aliasing.
459 typedef MapVector<Value*, Instruction* > AliasMap;
460 typedef DenseMap<Value*, std::vector<Instruction*> > AliasMultiMap;
462 /// Returns true if it is legal to vectorize this loop.
463 /// This does not mean that it is profitable to vectorize this
464 /// loop, only that it is legal to do so.
467 /// Returns the Induction variable.
468 PHINode *getInduction() { return Induction; }
470 /// Returns the reduction variables found in the loop.
471 ReductionList *getReductionVars() { return &Reductions; }
473 /// Returns the induction variables found in the loop.
474 InductionList *getInductionVars() { return &Inductions; }
476 /// Returns True if V is an induction variable in this loop.
477 bool isInductionVariable(const Value *V);
479 /// Return true if the block BB needs to be predicated in order for the loop
480 /// to be vectorized.
481 bool blockNeedsPredication(BasicBlock *BB);
483 /// Check if this pointer is consecutive when vectorizing. This happens
484 /// when the last index of the GEP is the induction variable, or that the
485 /// pointer itself is an induction variable.
486 /// This check allows us to vectorize A[idx] into a wide load/store.
488 /// 0 - Stride is unknown or non consecutive.
489 /// 1 - Address is consecutive.
490 /// -1 - Address is consecutive, and decreasing.
491 int isConsecutivePtr(Value *Ptr);
493 /// Returns true if the value V is uniform within the loop.
494 bool isUniform(Value *V);
496 /// Returns true if this instruction will remain scalar after vectorization.
497 bool isUniformAfterVectorization(Instruction* I) { return Uniforms.count(I); }
499 /// Returns the information that we collected about runtime memory check.
500 RuntimePointerCheck *getRuntimePointerCheck() { return &PtrRtCheck; }
502 /// This function returns the identity element (or neutral element) for
504 static Constant *getReductionIdentity(ReductionKind K, Type *Tp);
506 /// Check if a single basic block loop is vectorizable.
507 /// At this point we know that this is a loop with a constant trip count
508 /// and we only need to check individual instructions.
509 bool canVectorizeInstrs();
511 /// When we vectorize loops we may change the order in which
512 /// we read and write from memory. This method checks if it is
513 /// legal to vectorize the code, considering only memory constrains.
514 /// Returns true if the loop is vectorizable
515 bool canVectorizeMemory();
517 /// Return true if we can vectorize this loop using the IF-conversion
519 bool canVectorizeWithIfConvert();
521 /// Collect the variables that need to stay uniform after vectorization.
522 void collectLoopUniforms();
524 /// Return true if all of the instructions in the block can be speculatively
526 bool blockCanBePredicated(BasicBlock *BB);
528 /// Returns True, if 'Phi' is the kind of reduction variable for type
529 /// 'Kind'. If this is a reduction variable, it adds it to ReductionList.
530 bool AddReductionVar(PHINode *Phi, ReductionKind Kind);
531 /// Returns a struct describing if the instruction 'I' can be a reduction
532 /// variable of type 'Kind'. If the reduction is a min/max pattern of
533 /// select(icmp()) this function advances the instruction pointer 'I' from the
534 /// compare instruction to the select instruction and stores this pointer in
535 /// 'PatternLastInst' member of the returned struct.
536 ReductionInstDesc isReductionInstr(Instruction *I, ReductionKind Kind,
537 ReductionInstDesc &Desc);
538 /// Returns true if the instruction is a Select(ICmp(X, Y), X, Y) instruction
539 /// pattern corresponding to a min(X, Y) or max(X, Y).
540 static ReductionInstDesc isMinMaxSelectCmpPattern(Instruction *I,
541 ReductionInstDesc &Prev);
542 /// Returns the induction kind of Phi. This function may return NoInduction
543 /// if the PHI is not an induction variable.
544 InductionKind isInductionVariable(PHINode *Phi);
545 /// Return true if can compute the address bounds of Ptr within the loop.
546 bool hasComputableBounds(Value *Ptr);
547 /// Return true if there is the chance of write reorder.
548 bool hasPossibleGlobalWriteReorder(Value *Object,
550 AliasMultiMap &WriteObjects,
551 unsigned MaxByteWidth);
552 /// Return the AA location for a load or a store.
553 AliasAnalysis::Location getLoadStoreLocation(Instruction *Inst);
556 /// The loop that we evaluate.
560 /// DataLayout analysis.
565 TargetTransformInfo *TTI;
568 /// Target Library Info.
569 TargetLibraryInfo *TLI;
571 // --- vectorization state --- //
573 /// Holds the integer induction variable. This is the counter of the
576 /// Holds the reduction variables.
577 ReductionList Reductions;
578 /// Holds all of the induction variables that we found in the loop.
579 /// Notice that inductions don't need to start at zero and that induction
580 /// variables can be pointers.
581 InductionList Inductions;
583 /// Allowed outside users. This holds the reduction
584 /// vars which can be accessed from outside the loop.
585 SmallPtrSet<Value*, 4> AllowedExit;
586 /// This set holds the variables which are known to be uniform after
588 SmallPtrSet<Instruction*, 4> Uniforms;
589 /// We need to check that all of the pointers in this list are disjoint
591 RuntimePointerCheck PtrRtCheck;
592 /// Can we assume the absence of NaNs.
593 bool HasFunNoNaNAttr;
596 /// LoopVectorizationCostModel - estimates the expected speedups due to
598 /// In many cases vectorization is not profitable. This can happen because of
599 /// a number of reasons. In this class we mainly attempt to predict the
600 /// expected speedup/slowdowns due to the supported instruction set. We use the
601 /// TargetTransformInfo to query the different backends for the cost of
602 /// different operations.
603 class LoopVectorizationCostModel {
605 LoopVectorizationCostModel(Loop *L, ScalarEvolution *SE, LoopInfo *LI,
606 LoopVectorizationLegality *Legal,
607 const TargetTransformInfo &TTI,
608 DataLayout *DL, const TargetLibraryInfo *TLI)
609 : TheLoop(L), SE(SE), LI(LI), Legal(Legal), TTI(TTI), DL(DL), TLI(TLI) {}
611 /// Information about vectorization costs
612 struct VectorizationFactor {
613 unsigned Width; // Vector width with best cost
614 unsigned Cost; // Cost of the loop with that width
616 /// \return The most profitable vectorization factor and the cost of that VF.
617 /// This method checks every power of two up to VF. If UserVF is not ZERO
618 /// then this vectorization factor will be selected if vectorization is
620 VectorizationFactor selectVectorizationFactor(bool OptForSize,
623 /// \return The size (in bits) of the widest type in the code that
624 /// needs to be vectorized. We ignore values that remain scalar such as
625 /// 64 bit loop indices.
626 unsigned getWidestType();
628 /// \return The most profitable unroll factor.
629 /// If UserUF is non-zero then this method finds the best unroll-factor
630 /// based on register pressure and other parameters.
631 /// VF and LoopCost are the selected vectorization factor and the cost of the
633 unsigned selectUnrollFactor(bool OptForSize, unsigned UserUF, unsigned VF,
636 /// \brief A struct that represents some properties of the register usage
638 struct RegisterUsage {
639 /// Holds the number of loop invariant values that are used in the loop.
640 unsigned LoopInvariantRegs;
641 /// Holds the maximum number of concurrent live intervals in the loop.
642 unsigned MaxLocalUsers;
643 /// Holds the number of instructions in the loop.
644 unsigned NumInstructions;
647 /// \return information about the register usage of the loop.
648 RegisterUsage calculateRegisterUsage();
651 /// Returns the expected execution cost. The unit of the cost does
652 /// not matter because we use the 'cost' units to compare different
653 /// vector widths. The cost that is returned is *not* normalized by
654 /// the factor width.
655 unsigned expectedCost(unsigned VF);
657 /// Returns the execution time cost of an instruction for a given vector
658 /// width. Vector width of one means scalar.
659 unsigned getInstructionCost(Instruction *I, unsigned VF);
661 /// A helper function for converting Scalar types to vector types.
662 /// If the incoming type is void, we return void. If the VF is 1, we return
664 static Type* ToVectorTy(Type *Scalar, unsigned VF);
666 /// Returns whether the instruction is a load or store and will be a emitted
667 /// as a vector operation.
668 bool isConsecutiveLoadOrStore(Instruction *I);
670 /// The loop that we evaluate.
674 /// Loop Info analysis.
676 /// Vectorization legality.
677 LoopVectorizationLegality *Legal;
678 /// Vector target information.
679 const TargetTransformInfo &TTI;
680 /// Target data layout information.
682 /// Target Library Info.
683 const TargetLibraryInfo *TLI;
686 /// The LoopVectorize Pass.
687 struct LoopVectorize : public LoopPass {
688 /// Pass identification, replacement for typeid
691 explicit LoopVectorize() : LoopPass(ID) {
692 initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
698 TargetTransformInfo *TTI;
701 TargetLibraryInfo *TLI;
703 virtual bool runOnLoop(Loop *L, LPPassManager &LPM) {
704 // We only vectorize innermost loops.
708 SE = &getAnalysis<ScalarEvolution>();
709 DL = getAnalysisIfAvailable<DataLayout>();
710 LI = &getAnalysis<LoopInfo>();
711 TTI = &getAnalysis<TargetTransformInfo>();
712 DT = &getAnalysis<DominatorTree>();
713 AA = getAnalysisIfAvailable<AliasAnalysis>();
714 TLI = getAnalysisIfAvailable<TargetLibraryInfo>();
717 DEBUG(dbgs() << "LV: Not vectorizing because of missing data layout");
721 DEBUG(dbgs() << "LV: Checking a loop in \"" <<
722 L->getHeader()->getParent()->getName() << "\"\n");
724 // Check if it is legal to vectorize the loop.
725 LoopVectorizationLegality LVL(L, SE, DL, DT, TTI, AA, TLI);
726 if (!LVL.canVectorize()) {
727 DEBUG(dbgs() << "LV: Not vectorizing.\n");
731 // Use the cost model.
732 LoopVectorizationCostModel CM(L, SE, LI, &LVL, *TTI, DL, TLI);
734 // Check the function attributes to find out if this function should be
735 // optimized for size.
736 Function *F = L->getHeader()->getParent();
737 Attribute::AttrKind SzAttr = Attribute::OptimizeForSize;
738 Attribute::AttrKind FlAttr = Attribute::NoImplicitFloat;
739 unsigned FnIndex = AttributeSet::FunctionIndex;
740 bool OptForSize = F->getAttributes().hasAttribute(FnIndex, SzAttr);
741 bool NoFloat = F->getAttributes().hasAttribute(FnIndex, FlAttr);
744 DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat"
745 "attribute is used.\n");
749 // Select the optimal vectorization factor.
750 LoopVectorizationCostModel::VectorizationFactor VF;
751 VF = CM.selectVectorizationFactor(OptForSize, VectorizationFactor);
752 // Select the unroll factor.
753 unsigned UF = CM.selectUnrollFactor(OptForSize, VectorizationUnroll,
757 DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
761 DEBUG(dbgs() << "LV: Found a vectorizable loop ("<< VF.Width << ") in "<<
762 F->getParent()->getModuleIdentifier()<<"\n");
763 DEBUG(dbgs() << "LV: Unroll Factor is " << UF << "\n");
765 // If we decided that it is *legal* to vectorize the loop then do it.
766 InnerLoopVectorizer LB(L, SE, LI, DT, DL, TLI, VF.Width, UF);
769 DEBUG(verifyFunction(*L->getHeader()->getParent()));
773 virtual void getAnalysisUsage(AnalysisUsage &AU) const {
774 LoopPass::getAnalysisUsage(AU);
775 AU.addRequiredID(LoopSimplifyID);
776 AU.addRequiredID(LCSSAID);
777 AU.addRequired<DominatorTree>();
778 AU.addRequired<LoopInfo>();
779 AU.addRequired<ScalarEvolution>();
780 AU.addRequired<TargetTransformInfo>();
781 AU.addPreserved<LoopInfo>();
782 AU.addPreserved<DominatorTree>();
787 } // end anonymous namespace
789 //===----------------------------------------------------------------------===//
790 // Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
791 // LoopVectorizationCostModel.
792 //===----------------------------------------------------------------------===//
795 LoopVectorizationLegality::RuntimePointerCheck::insert(ScalarEvolution *SE,
796 Loop *Lp, Value *Ptr,
798 const SCEV *Sc = SE->getSCEV(Ptr);
799 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Sc);
800 assert(AR && "Invalid addrec expression");
801 const SCEV *Ex = SE->getExitCount(Lp, Lp->getLoopLatch());
802 const SCEV *ScEnd = AR->evaluateAtIteration(Ex, *SE);
803 Pointers.push_back(Ptr);
804 Starts.push_back(AR->getStart());
805 Ends.push_back(ScEnd);
806 IsWritePtr.push_back(WritePtr);
809 Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) {
810 // Save the current insertion location.
811 Instruction *Loc = Builder.GetInsertPoint();
813 // We need to place the broadcast of invariant variables outside the loop.
814 Instruction *Instr = dyn_cast<Instruction>(V);
815 bool NewInstr = (Instr && Instr->getParent() == LoopVectorBody);
816 bool Invariant = OrigLoop->isLoopInvariant(V) && !NewInstr;
818 // Place the code for broadcasting invariant variables in the new preheader.
820 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
822 // Broadcast the scalar into all locations in the vector.
823 Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast");
825 // Restore the builder insertion point.
827 Builder.SetInsertPoint(Loc);
832 Value *InnerLoopVectorizer::getConsecutiveVector(Value* Val, int StartIdx,
834 assert(Val->getType()->isVectorTy() && "Must be a vector");
835 assert(Val->getType()->getScalarType()->isIntegerTy() &&
836 "Elem must be an integer");
838 Type *ITy = Val->getType()->getScalarType();
839 VectorType *Ty = cast<VectorType>(Val->getType());
840 int VLen = Ty->getNumElements();
841 SmallVector<Constant*, 8> Indices;
843 // Create a vector of consecutive numbers from zero to VF.
844 for (int i = 0; i < VLen; ++i) {
845 int64_t Idx = Negate ? (-i) : i;
846 Indices.push_back(ConstantInt::get(ITy, StartIdx + Idx, Negate));
849 // Add the consecutive indices to the vector value.
850 Constant *Cv = ConstantVector::get(Indices);
851 assert(Cv->getType() == Val->getType() && "Invalid consecutive vec");
852 return Builder.CreateAdd(Val, Cv, "induction");
855 int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) {
856 assert(Ptr->getType()->isPointerTy() && "Unexpected non ptr");
857 // Make sure that the pointer does not point to structs.
858 if (cast<PointerType>(Ptr->getType())->getElementType()->isAggregateType())
861 // If this value is a pointer induction variable we know it is consecutive.
862 PHINode *Phi = dyn_cast_or_null<PHINode>(Ptr);
863 if (Phi && Inductions.count(Phi)) {
864 InductionInfo II = Inductions[Phi];
865 if (IK_PtrInduction == II.IK)
867 else if (IK_ReversePtrInduction == II.IK)
871 GetElementPtrInst *Gep = dyn_cast_or_null<GetElementPtrInst>(Ptr);
875 unsigned NumOperands = Gep->getNumOperands();
876 Value *LastIndex = Gep->getOperand(NumOperands - 1);
878 Value *GpPtr = Gep->getPointerOperand();
879 // If this GEP value is a consecutive pointer induction variable and all of
880 // the indices are constant then we know it is consecutive. We can
881 Phi = dyn_cast<PHINode>(GpPtr);
882 if (Phi && Inductions.count(Phi)) {
884 // Make sure that the pointer does not point to structs.
885 PointerType *GepPtrType = cast<PointerType>(GpPtr->getType());
886 if (GepPtrType->getElementType()->isAggregateType())
889 // Make sure that all of the index operands are loop invariant.
890 for (unsigned i = 1; i < NumOperands; ++i)
891 if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
894 InductionInfo II = Inductions[Phi];
895 if (IK_PtrInduction == II.IK)
897 else if (IK_ReversePtrInduction == II.IK)
901 // Check that all of the gep indices are uniform except for the last.
902 for (unsigned i = 0; i < NumOperands - 1; ++i)
903 if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
906 // We can emit wide load/stores only if the last index is the induction
908 const SCEV *Last = SE->getSCEV(LastIndex);
909 if (const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Last)) {
910 const SCEV *Step = AR->getStepRecurrence(*SE);
912 // The memory is consecutive because the last index is consecutive
913 // and all other indices are loop invariant.
916 if (Step->isAllOnesValue())
923 bool LoopVectorizationLegality::isUniform(Value *V) {
924 return (SE->isLoopInvariant(SE->getSCEV(V), TheLoop));
927 InnerLoopVectorizer::VectorParts&
928 InnerLoopVectorizer::getVectorValue(Value *V) {
929 assert(V != Induction && "The new induction variable should not be used.");
930 assert(!V->getType()->isVectorTy() && "Can't widen a vector");
932 // If we have this scalar in the map, return it.
934 return WidenMap.get(V);
936 // If this scalar is unknown, assume that it is a constant or that it is
937 // loop invariant. Broadcast V and save the value for future uses.
938 Value *B = getBroadcastInstrs(V);
939 return WidenMap.splat(V, B);
942 Value *InnerLoopVectorizer::reverseVector(Value *Vec) {
943 assert(Vec->getType()->isVectorTy() && "Invalid type");
944 SmallVector<Constant*, 8> ShuffleMask;
945 for (unsigned i = 0; i < VF; ++i)
946 ShuffleMask.push_back(Builder.getInt32(VF - i - 1));
948 return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()),
949 ConstantVector::get(ShuffleMask),
954 void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr,
955 LoopVectorizationLegality *Legal) {
956 // Attempt to issue a wide load.
957 LoadInst *LI = dyn_cast<LoadInst>(Instr);
958 StoreInst *SI = dyn_cast<StoreInst>(Instr);
960 assert((LI || SI) && "Invalid Load/Store instruction");
962 Type *ScalarDataTy = LI ? LI->getType() : SI->getValueOperand()->getType();
963 Type *DataTy = VectorType::get(ScalarDataTy, VF);
964 Value *Ptr = LI ? LI->getPointerOperand() : SI->getPointerOperand();
965 unsigned Alignment = LI ? LI->getAlignment() : SI->getAlignment();
967 unsigned ScalarAllocatedSize = DL->getTypeAllocSize(ScalarDataTy);
968 unsigned VectorElementSize = DL->getTypeStoreSize(DataTy)/VF;
970 if (ScalarAllocatedSize != VectorElementSize)
971 return scalarizeInstruction(Instr);
973 // If the pointer is loop invariant or if it is non consecutive,
974 // scalarize the load.
975 int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
976 bool Reverse = ConsecutiveStride < 0;
977 bool UniformLoad = LI && Legal->isUniform(Ptr);
978 if (!ConsecutiveStride || UniformLoad)
979 return scalarizeInstruction(Instr);
981 Constant *Zero = Builder.getInt32(0);
982 VectorParts &Entry = WidenMap.get(Instr);
984 // Handle consecutive loads/stores.
985 GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr);
986 if (Gep && Legal->isInductionVariable(Gep->getPointerOperand())) {
987 Value *PtrOperand = Gep->getPointerOperand();
988 Value *FirstBasePtr = getVectorValue(PtrOperand)[0];
989 FirstBasePtr = Builder.CreateExtractElement(FirstBasePtr, Zero);
991 // Create the new GEP with the new induction variable.
992 GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
993 Gep2->setOperand(0, FirstBasePtr);
994 Gep2->setName("gep.indvar.base");
995 Ptr = Builder.Insert(Gep2);
997 assert(SE->isLoopInvariant(SE->getSCEV(Gep->getPointerOperand()),
998 OrigLoop) && "Base ptr must be invariant");
1000 // The last index does not have to be the induction. It can be
1001 // consecutive and be a function of the index. For example A[I+1];
1002 unsigned NumOperands = Gep->getNumOperands();
1004 Value *LastGepOperand = Gep->getOperand(NumOperands - 1);
1005 VectorParts &GEPParts = getVectorValue(LastGepOperand);
1006 Value *LastIndex = GEPParts[0];
1007 LastIndex = Builder.CreateExtractElement(LastIndex, Zero);
1009 // Create the new GEP with the new induction variable.
1010 GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
1011 Gep2->setOperand(NumOperands - 1, LastIndex);
1012 Gep2->setName("gep.indvar.idx");
1013 Ptr = Builder.Insert(Gep2);
1015 // Use the induction element ptr.
1016 assert(isa<PHINode>(Ptr) && "Invalid induction ptr");
1017 VectorParts &PtrVal = getVectorValue(Ptr);
1018 Ptr = Builder.CreateExtractElement(PtrVal[0], Zero);
1023 assert(!Legal->isUniform(SI->getPointerOperand()) &&
1024 "We do not allow storing to uniform addresses");
1026 VectorParts &StoredVal = getVectorValue(SI->getValueOperand());
1027 for (unsigned Part = 0; Part < UF; ++Part) {
1028 // Calculate the pointer for the specific unroll-part.
1029 Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF));
1032 // If we store to reverse consecutive memory locations then we need
1033 // to reverse the order of elements in the stored value.
1034 StoredVal[Part] = reverseVector(StoredVal[Part]);
1035 // If the address is consecutive but reversed, then the
1036 // wide store needs to start at the last vector element.
1037 PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF));
1038 PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF));
1041 Value *VecPtr = Builder.CreateBitCast(PartPtr, DataTy->getPointerTo());
1042 Builder.CreateStore(StoredVal[Part], VecPtr)->setAlignment(Alignment);
1046 for (unsigned Part = 0; Part < UF; ++Part) {
1047 // Calculate the pointer for the specific unroll-part.
1048 Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF));
1051 // If the address is consecutive but reversed, then the
1052 // wide store needs to start at the last vector element.
1053 PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF));
1054 PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF));
1057 Value *VecPtr = Builder.CreateBitCast(PartPtr, DataTy->getPointerTo());
1058 Value *LI = Builder.CreateLoad(VecPtr, "wide.load");
1059 cast<LoadInst>(LI)->setAlignment(Alignment);
1060 Entry[Part] = Reverse ? reverseVector(LI) : LI;
1064 void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr) {
1065 assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
1066 // Holds vector parameters or scalars, in case of uniform vals.
1067 SmallVector<VectorParts, 4> Params;
1069 // Find all of the vectorized parameters.
1070 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
1071 Value *SrcOp = Instr->getOperand(op);
1073 // If we are accessing the old induction variable, use the new one.
1074 if (SrcOp == OldInduction) {
1075 Params.push_back(getVectorValue(SrcOp));
1079 // Try using previously calculated values.
1080 Instruction *SrcInst = dyn_cast<Instruction>(SrcOp);
1082 // If the src is an instruction that appeared earlier in the basic block
1083 // then it should already be vectorized.
1084 if (SrcInst && OrigLoop->contains(SrcInst)) {
1085 assert(WidenMap.has(SrcInst) && "Source operand is unavailable");
1086 // The parameter is a vector value from earlier.
1087 Params.push_back(WidenMap.get(SrcInst));
1089 // The parameter is a scalar from outside the loop. Maybe even a constant.
1090 VectorParts Scalars;
1091 Scalars.append(UF, SrcOp);
1092 Params.push_back(Scalars);
1096 assert(Params.size() == Instr->getNumOperands() &&
1097 "Invalid number of operands");
1099 // Does this instruction return a value ?
1100 bool IsVoidRetTy = Instr->getType()->isVoidTy();
1102 Value *UndefVec = IsVoidRetTy ? 0 :
1103 UndefValue::get(VectorType::get(Instr->getType(), VF));
1104 // Create a new entry in the WidenMap and initialize it to Undef or Null.
1105 VectorParts &VecResults = WidenMap.splat(Instr, UndefVec);
1107 // For each vector unroll 'part':
1108 for (unsigned Part = 0; Part < UF; ++Part) {
1109 // For each scalar that we create:
1110 for (unsigned Width = 0; Width < VF; ++Width) {
1111 Instruction *Cloned = Instr->clone();
1113 Cloned->setName(Instr->getName() + ".cloned");
1114 // Replace the operands of the cloned instrucions with extracted scalars.
1115 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
1116 Value *Op = Params[op][Part];
1117 // Param is a vector. Need to extract the right lane.
1118 if (Op->getType()->isVectorTy())
1119 Op = Builder.CreateExtractElement(Op, Builder.getInt32(Width));
1120 Cloned->setOperand(op, Op);
1123 // Place the cloned scalar in the new loop.
1124 Builder.Insert(Cloned);
1126 // If the original scalar returns a value we need to place it in a vector
1127 // so that future users will be able to use it.
1129 VecResults[Part] = Builder.CreateInsertElement(VecResults[Part], Cloned,
1130 Builder.getInt32(Width));
1136 InnerLoopVectorizer::addRuntimeCheck(LoopVectorizationLegality *Legal,
1138 LoopVectorizationLegality::RuntimePointerCheck *PtrRtCheck =
1139 Legal->getRuntimePointerCheck();
1141 if (!PtrRtCheck->Need)
1144 Instruction *MemoryRuntimeCheck = 0;
1145 unsigned NumPointers = PtrRtCheck->Pointers.size();
1146 SmallVector<Value* , 2> Starts;
1147 SmallVector<Value* , 2> Ends;
1149 SCEVExpander Exp(*SE, "induction");
1151 // Use this type for pointer arithmetic.
1152 Type* PtrArithTy = Type::getInt8PtrTy(Loc->getContext(), 0);
1154 for (unsigned i = 0; i < NumPointers; ++i) {
1155 Value *Ptr = PtrRtCheck->Pointers[i];
1156 const SCEV *Sc = SE->getSCEV(Ptr);
1158 if (SE->isLoopInvariant(Sc, OrigLoop)) {
1159 DEBUG(dbgs() << "LV: Adding RT check for a loop invariant ptr:" <<
1161 Starts.push_back(Ptr);
1162 Ends.push_back(Ptr);
1164 DEBUG(dbgs() << "LV: Adding RT check for range:" << *Ptr <<"\n");
1166 Value *Start = Exp.expandCodeFor(PtrRtCheck->Starts[i], PtrArithTy, Loc);
1167 Value *End = Exp.expandCodeFor(PtrRtCheck->Ends[i], PtrArithTy, Loc);
1168 Starts.push_back(Start);
1169 Ends.push_back(End);
1173 IRBuilder<> ChkBuilder(Loc);
1175 for (unsigned i = 0; i < NumPointers; ++i) {
1176 for (unsigned j = i+1; j < NumPointers; ++j) {
1177 // No need to check if two readonly pointers intersect.
1178 if (!PtrRtCheck->IsWritePtr[i] && !PtrRtCheck->IsWritePtr[j])
1181 Value *Start0 = ChkBuilder.CreateBitCast(Starts[i], PtrArithTy, "bc");
1182 Value *Start1 = ChkBuilder.CreateBitCast(Starts[j], PtrArithTy, "bc");
1183 Value *End0 = ChkBuilder.CreateBitCast(Ends[i], PtrArithTy, "bc");
1184 Value *End1 = ChkBuilder.CreateBitCast(Ends[j], PtrArithTy, "bc");
1186 Value *Cmp0 = ChkBuilder.CreateICmpULE(Start0, End1, "bound0");
1187 Value *Cmp1 = ChkBuilder.CreateICmpULE(Start1, End0, "bound1");
1188 Value *IsConflict = ChkBuilder.CreateAnd(Cmp0, Cmp1, "found.conflict");
1189 if (MemoryRuntimeCheck)
1190 IsConflict = ChkBuilder.CreateOr(MemoryRuntimeCheck, IsConflict,
1193 MemoryRuntimeCheck = cast<Instruction>(IsConflict);
1197 return MemoryRuntimeCheck;
1201 InnerLoopVectorizer::createEmptyLoop(LoopVectorizationLegality *Legal) {
1203 In this function we generate a new loop. The new loop will contain
1204 the vectorized instructions while the old loop will continue to run the
1207 [ ] <-- vector loop bypass (may consist of multiple blocks).
1210 | [ ] <-- vector pre header.
1214 | [ ]_| <-- vector loop.
1217 >[ ] <--- middle-block.
1220 | [ ] <--- new preheader.
1224 | [ ]_| <-- old scalar loop to handle remainder.
1227 >[ ] <-- exit block.
1231 BasicBlock *OldBasicBlock = OrigLoop->getHeader();
1232 BasicBlock *BypassBlock = OrigLoop->getLoopPreheader();
1233 BasicBlock *ExitBlock = OrigLoop->getExitBlock();
1234 assert(ExitBlock && "Must have an exit block");
1236 // Mark the old scalar loop with metadata that tells us not to vectorize this
1237 // loop again if we run into it.
1238 MDNode *MD = MDNode::get(OldBasicBlock->getContext(), None);
1239 OldBasicBlock->getTerminator()->setMetadata(AlreadyVectorizedMDName, MD);
1241 // Some loops have a single integer induction variable, while other loops
1242 // don't. One example is c++ iterators that often have multiple pointer
1243 // induction variables. In the code below we also support a case where we
1244 // don't have a single induction variable.
1245 OldInduction = Legal->getInduction();
1246 Type *IdxTy = OldInduction ? OldInduction->getType() :
1247 DL->getIntPtrType(SE->getContext());
1249 // Find the loop boundaries.
1250 const SCEV *ExitCount = SE->getExitCount(OrigLoop, OrigLoop->getLoopLatch());
1251 assert(ExitCount != SE->getCouldNotCompute() && "Invalid loop count");
1253 // Get the total trip count from the count by adding 1.
1254 ExitCount = SE->getAddExpr(ExitCount,
1255 SE->getConstant(ExitCount->getType(), 1));
1257 // Expand the trip count and place the new instructions in the preheader.
1258 // Notice that the pre-header does not change, only the loop body.
1259 SCEVExpander Exp(*SE, "induction");
1261 // Count holds the overall loop count (N).
1262 Value *Count = Exp.expandCodeFor(ExitCount, ExitCount->getType(),
1263 BypassBlock->getTerminator());
1265 // The loop index does not have to start at Zero. Find the original start
1266 // value from the induction PHI node. If we don't have an induction variable
1267 // then we know that it starts at zero.
1268 Value *StartIdx = OldInduction ?
1269 OldInduction->getIncomingValueForBlock(BypassBlock):
1270 ConstantInt::get(IdxTy, 0);
1272 assert(BypassBlock && "Invalid loop structure");
1273 LoopBypassBlocks.push_back(BypassBlock);
1275 // Split the single block loop into the two loop structure described above.
1276 BasicBlock *VectorPH =
1277 BypassBlock->splitBasicBlock(BypassBlock->getTerminator(), "vector.ph");
1278 BasicBlock *VecBody =
1279 VectorPH->splitBasicBlock(VectorPH->getTerminator(), "vector.body");
1280 BasicBlock *MiddleBlock =
1281 VecBody->splitBasicBlock(VecBody->getTerminator(), "middle.block");
1282 BasicBlock *ScalarPH =
1283 MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(), "scalar.ph");
1285 // Use this IR builder to create the loop instructions (Phi, Br, Cmp)
1287 Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
1289 // Generate the induction variable.
1290 Induction = Builder.CreatePHI(IdxTy, 2, "index");
1291 // The loop step is equal to the vectorization factor (num of SIMD elements)
1292 // times the unroll factor (num of SIMD instructions).
1293 Constant *Step = ConstantInt::get(IdxTy, VF * UF);
1295 // This is the IR builder that we use to add all of the logic for bypassing
1296 // the new vector loop.
1297 IRBuilder<> BypassBuilder(BypassBlock->getTerminator());
1299 // We may need to extend the index in case there is a type mismatch.
1300 // We know that the count starts at zero and does not overflow.
1301 if (Count->getType() != IdxTy) {
1302 // The exit count can be of pointer type. Convert it to the correct
1304 if (ExitCount->getType()->isPointerTy())
1305 Count = BypassBuilder.CreatePointerCast(Count, IdxTy, "ptrcnt.to.int");
1307 Count = BypassBuilder.CreateZExtOrTrunc(Count, IdxTy, "cnt.cast");
1310 // Add the start index to the loop count to get the new end index.
1311 Value *IdxEnd = BypassBuilder.CreateAdd(Count, StartIdx, "end.idx");
1313 // Now we need to generate the expression for N - (N % VF), which is
1314 // the part that the vectorized body will execute.
1315 Value *R = BypassBuilder.CreateURem(Count, Step, "n.mod.vf");
1316 Value *CountRoundDown = BypassBuilder.CreateSub(Count, R, "n.vec");
1317 Value *IdxEndRoundDown = BypassBuilder.CreateAdd(CountRoundDown, StartIdx,
1318 "end.idx.rnd.down");
1320 // Now, compare the new count to zero. If it is zero skip the vector loop and
1321 // jump to the scalar loop.
1322 Value *Cmp = BypassBuilder.CreateICmpEQ(IdxEndRoundDown, StartIdx,
1325 BasicBlock *LastBypassBlock = BypassBlock;
1327 // Generate the code that checks in runtime if arrays overlap. We put the
1328 // checks into a separate block to make the more common case of few elements
1330 Instruction *MemRuntimeCheck = addRuntimeCheck(Legal,
1331 BypassBlock->getTerminator());
1332 if (MemRuntimeCheck) {
1333 // Create a new block containing the memory check.
1334 BasicBlock *CheckBlock = BypassBlock->splitBasicBlock(MemRuntimeCheck,
1336 LoopBypassBlocks.push_back(CheckBlock);
1338 // Replace the branch into the memory check block with a conditional branch
1339 // for the "few elements case".
1340 Instruction *OldTerm = BypassBlock->getTerminator();
1341 BranchInst::Create(MiddleBlock, CheckBlock, Cmp, OldTerm);
1342 OldTerm->eraseFromParent();
1344 Cmp = MemRuntimeCheck;
1345 LastBypassBlock = CheckBlock;
1348 LastBypassBlock->getTerminator()->eraseFromParent();
1349 BranchInst::Create(MiddleBlock, VectorPH, Cmp,
1352 // We are going to resume the execution of the scalar loop.
1353 // Go over all of the induction variables that we found and fix the
1354 // PHIs that are left in the scalar version of the loop.
1355 // The starting values of PHI nodes depend on the counter of the last
1356 // iteration in the vectorized loop.
1357 // If we come from a bypass edge then we need to start from the original
1360 // This variable saves the new starting index for the scalar loop.
1361 PHINode *ResumeIndex = 0;
1362 LoopVectorizationLegality::InductionList::iterator I, E;
1363 LoopVectorizationLegality::InductionList *List = Legal->getInductionVars();
1364 for (I = List->begin(), E = List->end(); I != E; ++I) {
1365 PHINode *OrigPhi = I->first;
1366 LoopVectorizationLegality::InductionInfo II = I->second;
1367 PHINode *ResumeVal = PHINode::Create(OrigPhi->getType(), 2, "resume.val",
1368 MiddleBlock->getTerminator());
1369 Value *EndValue = 0;
1371 case LoopVectorizationLegality::IK_NoInduction:
1372 llvm_unreachable("Unknown induction");
1373 case LoopVectorizationLegality::IK_IntInduction: {
1374 // Handle the integer induction counter:
1375 assert(OrigPhi->getType()->isIntegerTy() && "Invalid type");
1376 assert(OrigPhi == OldInduction && "Unknown integer PHI");
1377 // We know what the end value is.
1378 EndValue = IdxEndRoundDown;
1379 // We also know which PHI node holds it.
1380 ResumeIndex = ResumeVal;
1383 case LoopVectorizationLegality::IK_ReverseIntInduction: {
1384 // Convert the CountRoundDown variable to the PHI size.
1385 unsigned CRDSize = CountRoundDown->getType()->getScalarSizeInBits();
1386 unsigned IISize = II.StartValue->getType()->getScalarSizeInBits();
1387 Value *CRD = CountRoundDown;
1388 if (CRDSize > IISize)
1389 CRD = CastInst::Create(Instruction::Trunc, CountRoundDown,
1390 II.StartValue->getType(), "tr.crd",
1391 LoopBypassBlocks.back()->getTerminator());
1392 else if (CRDSize < IISize)
1393 CRD = CastInst::Create(Instruction::SExt, CountRoundDown,
1394 II.StartValue->getType(),
1396 LoopBypassBlocks.back()->getTerminator());
1397 // Handle reverse integer induction counter:
1399 BinaryOperator::CreateSub(II.StartValue, CRD, "rev.ind.end",
1400 LoopBypassBlocks.back()->getTerminator());
1403 case LoopVectorizationLegality::IK_PtrInduction: {
1404 // For pointer induction variables, calculate the offset using
1407 GetElementPtrInst::Create(II.StartValue, CountRoundDown, "ptr.ind.end",
1408 LoopBypassBlocks.back()->getTerminator());
1411 case LoopVectorizationLegality::IK_ReversePtrInduction: {
1412 // The value at the end of the loop for the reverse pointer is calculated
1413 // by creating a GEP with a negative index starting from the start value.
1414 Value *Zero = ConstantInt::get(CountRoundDown->getType(), 0);
1415 Value *NegIdx = BinaryOperator::CreateSub(Zero, CountRoundDown,
1417 LoopBypassBlocks.back()->getTerminator());
1418 EndValue = GetElementPtrInst::Create(II.StartValue, NegIdx,
1420 LoopBypassBlocks.back()->getTerminator());
1425 // The new PHI merges the original incoming value, in case of a bypass,
1426 // or the value at the end of the vectorized loop.
1427 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
1428 ResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[I]);
1429 ResumeVal->addIncoming(EndValue, VecBody);
1431 // Fix the scalar body counter (PHI node).
1432 unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH);
1433 OrigPhi->setIncomingValue(BlockIdx, ResumeVal);
1436 // If we are generating a new induction variable then we also need to
1437 // generate the code that calculates the exit value. This value is not
1438 // simply the end of the counter because we may skip the vectorized body
1439 // in case of a runtime check.
1441 assert(!ResumeIndex && "Unexpected resume value found");
1442 ResumeIndex = PHINode::Create(IdxTy, 2, "new.indc.resume.val",
1443 MiddleBlock->getTerminator());
1444 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
1445 ResumeIndex->addIncoming(StartIdx, LoopBypassBlocks[I]);
1446 ResumeIndex->addIncoming(IdxEndRoundDown, VecBody);
1449 // Make sure that we found the index where scalar loop needs to continue.
1450 assert(ResumeIndex && ResumeIndex->getType()->isIntegerTy() &&
1451 "Invalid resume Index");
1453 // Add a check in the middle block to see if we have completed
1454 // all of the iterations in the first vector loop.
1455 // If (N - N%VF) == N, then we *don't* need to run the remainder.
1456 Value *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, IdxEnd,
1457 ResumeIndex, "cmp.n",
1458 MiddleBlock->getTerminator());
1460 BranchInst::Create(ExitBlock, ScalarPH, CmpN, MiddleBlock->getTerminator());
1461 // Remove the old terminator.
1462 MiddleBlock->getTerminator()->eraseFromParent();
1464 // Create i+1 and fill the PHINode.
1465 Value *NextIdx = Builder.CreateAdd(Induction, Step, "index.next");
1466 Induction->addIncoming(StartIdx, VectorPH);
1467 Induction->addIncoming(NextIdx, VecBody);
1468 // Create the compare.
1469 Value *ICmp = Builder.CreateICmpEQ(NextIdx, IdxEndRoundDown);
1470 Builder.CreateCondBr(ICmp, MiddleBlock, VecBody);
1472 // Now we have two terminators. Remove the old one from the block.
1473 VecBody->getTerminator()->eraseFromParent();
1475 // Get ready to start creating new instructions into the vectorized body.
1476 Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
1478 // Create and register the new vector loop.
1479 Loop* Lp = new Loop();
1480 Loop *ParentLoop = OrigLoop->getParentLoop();
1482 // Insert the new loop into the loop nest and register the new basic blocks.
1484 ParentLoop->addChildLoop(Lp);
1485 for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
1486 ParentLoop->addBasicBlockToLoop(LoopBypassBlocks[I], LI->getBase());
1487 ParentLoop->addBasicBlockToLoop(ScalarPH, LI->getBase());
1488 ParentLoop->addBasicBlockToLoop(VectorPH, LI->getBase());
1489 ParentLoop->addBasicBlockToLoop(MiddleBlock, LI->getBase());
1491 LI->addTopLevelLoop(Lp);
1494 Lp->addBasicBlockToLoop(VecBody, LI->getBase());
1497 LoopVectorPreHeader = VectorPH;
1498 LoopScalarPreHeader = ScalarPH;
1499 LoopMiddleBlock = MiddleBlock;
1500 LoopExitBlock = ExitBlock;
1501 LoopVectorBody = VecBody;
1502 LoopScalarBody = OldBasicBlock;
1505 /// This function returns the identity element (or neutral element) for
1506 /// the operation K.
1508 LoopVectorizationLegality::getReductionIdentity(ReductionKind K, Type *Tp) {
1513 // Adding, Xoring, Oring zero to a number does not change it.
1514 return ConstantInt::get(Tp, 0);
1515 case RK_IntegerMult:
1516 // Multiplying a number by 1 does not change it.
1517 return ConstantInt::get(Tp, 1);
1519 // AND-ing a number with an all-1 value does not change it.
1520 return ConstantInt::get(Tp, -1, true);
1522 // Multiplying a number by 1 does not change it.
1523 return ConstantFP::get(Tp, 1.0L);
1525 // Adding zero to a number does not change it.
1526 return ConstantFP::get(Tp, 0.0L);
1528 llvm_unreachable("Unknown reduction kind");
1532 static Intrinsic::ID
1533 getIntrinsicIDForCall(CallInst *CI, const TargetLibraryInfo *TLI) {
1534 // If we have an intrinsic call, check if it is trivially vectorizable.
1535 if (IntrinsicInst *II = dyn_cast<IntrinsicInst>(CI)) {
1536 switch (II->getIntrinsicID()) {
1537 case Intrinsic::sqrt:
1538 case Intrinsic::sin:
1539 case Intrinsic::cos:
1540 case Intrinsic::exp:
1541 case Intrinsic::exp2:
1542 case Intrinsic::log:
1543 case Intrinsic::log10:
1544 case Intrinsic::log2:
1545 case Intrinsic::fabs:
1546 case Intrinsic::floor:
1547 case Intrinsic::ceil:
1548 case Intrinsic::trunc:
1549 case Intrinsic::rint:
1550 case Intrinsic::nearbyint:
1551 case Intrinsic::pow:
1552 case Intrinsic::fma:
1553 case Intrinsic::fmuladd:
1554 return II->getIntrinsicID();
1556 return Intrinsic::not_intrinsic;
1561 return Intrinsic::not_intrinsic;
1564 Function *F = CI->getCalledFunction();
1565 // We're going to make assumptions on the semantics of the functions, check
1566 // that the target knows that it's available in this environment.
1567 if (!F || !TLI->getLibFunc(F->getName(), Func))
1568 return Intrinsic::not_intrinsic;
1570 // Otherwise check if we have a call to a function that can be turned into a
1571 // vector intrinsic.
1578 return Intrinsic::sin;
1582 return Intrinsic::cos;
1586 return Intrinsic::exp;
1588 case LibFunc::exp2f:
1589 case LibFunc::exp2l:
1590 return Intrinsic::exp2;
1594 return Intrinsic::log;
1595 case LibFunc::log10:
1596 case LibFunc::log10f:
1597 case LibFunc::log10l:
1598 return Intrinsic::log10;
1600 case LibFunc::log2f:
1601 case LibFunc::log2l:
1602 return Intrinsic::log2;
1604 case LibFunc::fabsf:
1605 case LibFunc::fabsl:
1606 return Intrinsic::fabs;
1607 case LibFunc::floor:
1608 case LibFunc::floorf:
1609 case LibFunc::floorl:
1610 return Intrinsic::floor;
1612 case LibFunc::ceilf:
1613 case LibFunc::ceill:
1614 return Intrinsic::ceil;
1615 case LibFunc::trunc:
1616 case LibFunc::truncf:
1617 case LibFunc::truncl:
1618 return Intrinsic::trunc;
1620 case LibFunc::rintf:
1621 case LibFunc::rintl:
1622 return Intrinsic::rint;
1623 case LibFunc::nearbyint:
1624 case LibFunc::nearbyintf:
1625 case LibFunc::nearbyintl:
1626 return Intrinsic::nearbyint;
1630 return Intrinsic::pow;
1633 return Intrinsic::not_intrinsic;
1636 /// This function translates the reduction kind to an LLVM binary operator.
1638 getReductionBinOp(LoopVectorizationLegality::ReductionKind Kind) {
1640 case LoopVectorizationLegality::RK_IntegerAdd:
1641 return Instruction::Add;
1642 case LoopVectorizationLegality::RK_IntegerMult:
1643 return Instruction::Mul;
1644 case LoopVectorizationLegality::RK_IntegerOr:
1645 return Instruction::Or;
1646 case LoopVectorizationLegality::RK_IntegerAnd:
1647 return Instruction::And;
1648 case LoopVectorizationLegality::RK_IntegerXor:
1649 return Instruction::Xor;
1650 case LoopVectorizationLegality::RK_FloatMult:
1651 return Instruction::FMul;
1652 case LoopVectorizationLegality::RK_FloatAdd:
1653 return Instruction::FAdd;
1654 case LoopVectorizationLegality::RK_IntegerMinMax:
1655 return Instruction::ICmp;
1656 case LoopVectorizationLegality::RK_FloatMinMax:
1657 return Instruction::FCmp;
1659 llvm_unreachable("Unknown reduction operation");
1663 Value *createMinMaxOp(IRBuilder<> &Builder,
1664 LoopVectorizationLegality::MinMaxReductionKind RK,
1667 CmpInst::Predicate P = CmpInst::ICMP_NE;
1670 llvm_unreachable("Unknown min/max reduction kind");
1671 case LoopVectorizationLegality::MRK_UIntMin:
1672 P = CmpInst::ICMP_ULT;
1674 case LoopVectorizationLegality::MRK_UIntMax:
1675 P = CmpInst::ICMP_UGT;
1677 case LoopVectorizationLegality::MRK_SIntMin:
1678 P = CmpInst::ICMP_SLT;
1680 case LoopVectorizationLegality::MRK_SIntMax:
1681 P = CmpInst::ICMP_SGT;
1683 case LoopVectorizationLegality::MRK_FloatMin:
1684 P = CmpInst::FCMP_OLT;
1686 case LoopVectorizationLegality::MRK_FloatMax:
1687 P = CmpInst::FCMP_OGT;
1692 if (RK == LoopVectorizationLegality::MRK_FloatMin || RK == LoopVectorizationLegality::MRK_FloatMax)
1693 Cmp = Builder.CreateFCmp(P, Left, Right, "rdx.minmax.cmp");
1695 Cmp = Builder.CreateICmp(P, Left, Right, "rdx.minmax.cmp");
1697 Value *Select = Builder.CreateSelect(Cmp, Left, Right, "rdx.minmax.select");
1702 InnerLoopVectorizer::vectorizeLoop(LoopVectorizationLegality *Legal) {
1703 //===------------------------------------------------===//
1705 // Notice: any optimization or new instruction that go
1706 // into the code below should be also be implemented in
1709 //===------------------------------------------------===//
1710 Constant *Zero = Builder.getInt32(0);
1712 // In order to support reduction variables we need to be able to vectorize
1713 // Phi nodes. Phi nodes have cycles, so we need to vectorize them in two
1714 // stages. First, we create a new vector PHI node with no incoming edges.
1715 // We use this value when we vectorize all of the instructions that use the
1716 // PHI. Next, after all of the instructions in the block are complete we
1717 // add the new incoming edges to the PHI. At this point all of the
1718 // instructions in the basic block are vectorized, so we can use them to
1719 // construct the PHI.
1720 PhiVector RdxPHIsToFix;
1722 // Scan the loop in a topological order to ensure that defs are vectorized
1724 LoopBlocksDFS DFS(OrigLoop);
1727 // Vectorize all of the blocks in the original loop.
1728 for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(),
1729 be = DFS.endRPO(); bb != be; ++bb)
1730 vectorizeBlockInLoop(Legal, *bb, &RdxPHIsToFix);
1732 // At this point every instruction in the original loop is widened to
1733 // a vector form. We are almost done. Now, we need to fix the PHI nodes
1734 // that we vectorized. The PHI nodes are currently empty because we did
1735 // not want to introduce cycles. Notice that the remaining PHI nodes
1736 // that we need to fix are reduction variables.
1738 // Create the 'reduced' values for each of the induction vars.
1739 // The reduced values are the vector values that we scalarize and combine
1740 // after the loop is finished.
1741 for (PhiVector::iterator it = RdxPHIsToFix.begin(), e = RdxPHIsToFix.end();
1743 PHINode *RdxPhi = *it;
1744 assert(RdxPhi && "Unable to recover vectorized PHI");
1746 // Find the reduction variable descriptor.
1747 assert(Legal->getReductionVars()->count(RdxPhi) &&
1748 "Unable to find the reduction variable");
1749 LoopVectorizationLegality::ReductionDescriptor RdxDesc =
1750 (*Legal->getReductionVars())[RdxPhi];
1752 // We need to generate a reduction vector from the incoming scalar.
1753 // To do so, we need to generate the 'identity' vector and overide
1754 // one of the elements with the incoming scalar reduction. We need
1755 // to do it in the vector-loop preheader.
1756 Builder.SetInsertPoint(LoopBypassBlocks.front()->getTerminator());
1758 // This is the vector-clone of the value that leaves the loop.
1759 VectorParts &VectorExit = getVectorValue(RdxDesc.LoopExitInstr);
1760 Type *VecTy = VectorExit[0]->getType();
1762 // Find the reduction identity variable. Zero for addition, or, xor,
1763 // one for multiplication, -1 for And.
1766 if (RdxDesc.Kind == LoopVectorizationLegality::RK_IntegerMinMax ||
1767 RdxDesc.Kind == LoopVectorizationLegality::RK_FloatMinMax) {
1768 // MinMax reduction have the start value as their identify.
1769 VectorStart = Identity = Builder.CreateVectorSplat(VF, RdxDesc.StartValue,
1773 LoopVectorizationLegality::getReductionIdentity(RdxDesc.Kind,
1774 VecTy->getScalarType());
1775 Identity = ConstantVector::getSplat(VF, Iden);
1777 // This vector is the Identity vector where the first element is the
1778 // incoming scalar reduction.
1779 VectorStart = Builder.CreateInsertElement(Identity,
1780 RdxDesc.StartValue, Zero);
1783 // Fix the vector-loop phi.
1784 // We created the induction variable so we know that the
1785 // preheader is the first entry.
1786 BasicBlock *VecPreheader = Induction->getIncomingBlock(0);
1788 // Reductions do not have to start at zero. They can start with
1789 // any loop invariant values.
1790 VectorParts &VecRdxPhi = WidenMap.get(RdxPhi);
1791 BasicBlock *Latch = OrigLoop->getLoopLatch();
1792 Value *LoopVal = RdxPhi->getIncomingValueForBlock(Latch);
1793 VectorParts &Val = getVectorValue(LoopVal);
1794 for (unsigned part = 0; part < UF; ++part) {
1795 // Make sure to add the reduction stat value only to the
1796 // first unroll part.
1797 Value *StartVal = (part == 0) ? VectorStart : Identity;
1798 cast<PHINode>(VecRdxPhi[part])->addIncoming(StartVal, VecPreheader);
1799 cast<PHINode>(VecRdxPhi[part])->addIncoming(Val[part], LoopVectorBody);
1802 // Before each round, move the insertion point right between
1803 // the PHIs and the values we are going to write.
1804 // This allows us to write both PHINodes and the extractelement
1806 Builder.SetInsertPoint(LoopMiddleBlock->getFirstInsertionPt());
1808 VectorParts RdxParts;
1809 for (unsigned part = 0; part < UF; ++part) {
1810 // This PHINode contains the vectorized reduction variable, or
1811 // the initial value vector, if we bypass the vector loop.
1812 VectorParts &RdxExitVal = getVectorValue(RdxDesc.LoopExitInstr);
1813 PHINode *NewPhi = Builder.CreatePHI(VecTy, 2, "rdx.vec.exit.phi");
1814 Value *StartVal = (part == 0) ? VectorStart : Identity;
1815 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
1816 NewPhi->addIncoming(StartVal, LoopBypassBlocks[I]);
1817 NewPhi->addIncoming(RdxExitVal[part], LoopVectorBody);
1818 RdxParts.push_back(NewPhi);
1821 // Reduce all of the unrolled parts into a single vector.
1822 Value *ReducedPartRdx = RdxParts[0];
1823 unsigned Op = getReductionBinOp(RdxDesc.Kind);
1824 for (unsigned part = 1; part < UF; ++part) {
1825 if (Op != Instruction::ICmp && Op != Instruction::FCmp)
1826 ReducedPartRdx = Builder.CreateBinOp((Instruction::BinaryOps)Op,
1827 RdxParts[part], ReducedPartRdx,
1830 ReducedPartRdx = createMinMaxOp(Builder, RdxDesc.MinMaxKind,
1831 ReducedPartRdx, RdxParts[part]);
1834 // VF is a power of 2 so we can emit the reduction using log2(VF) shuffles
1835 // and vector ops, reducing the set of values being computed by half each
1837 assert(isPowerOf2_32(VF) &&
1838 "Reduction emission only supported for pow2 vectors!");
1839 Value *TmpVec = ReducedPartRdx;
1840 SmallVector<Constant*, 32> ShuffleMask(VF, 0);
1841 for (unsigned i = VF; i != 1; i >>= 1) {
1842 // Move the upper half of the vector to the lower half.
1843 for (unsigned j = 0; j != i/2; ++j)
1844 ShuffleMask[j] = Builder.getInt32(i/2 + j);
1846 // Fill the rest of the mask with undef.
1847 std::fill(&ShuffleMask[i/2], ShuffleMask.end(),
1848 UndefValue::get(Builder.getInt32Ty()));
1851 Builder.CreateShuffleVector(TmpVec,
1852 UndefValue::get(TmpVec->getType()),
1853 ConstantVector::get(ShuffleMask),
1856 if (Op != Instruction::ICmp && Op != Instruction::FCmp)
1857 TmpVec = Builder.CreateBinOp((Instruction::BinaryOps)Op, TmpVec, Shuf,
1860 TmpVec = createMinMaxOp(Builder, RdxDesc.MinMaxKind, TmpVec, Shuf);
1863 // The result is in the first element of the vector.
1864 Value *Scalar0 = Builder.CreateExtractElement(TmpVec, Builder.getInt32(0));
1866 // Now, we need to fix the users of the reduction variable
1867 // inside and outside of the scalar remainder loop.
1868 // We know that the loop is in LCSSA form. We need to update the
1869 // PHI nodes in the exit blocks.
1870 for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
1871 LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) {
1872 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
1873 if (!LCSSAPhi) continue;
1875 // All PHINodes need to have a single entry edge, or two if
1876 // we already fixed them.
1877 assert(LCSSAPhi->getNumIncomingValues() < 3 && "Invalid LCSSA PHI");
1879 // We found our reduction value exit-PHI. Update it with the
1880 // incoming bypass edge.
1881 if (LCSSAPhi->getIncomingValue(0) == RdxDesc.LoopExitInstr) {
1882 // Add an edge coming from the bypass.
1883 LCSSAPhi->addIncoming(Scalar0, LoopMiddleBlock);
1886 }// end of the LCSSA phi scan.
1888 // Fix the scalar loop reduction variable with the incoming reduction sum
1889 // from the vector body and from the backedge value.
1890 int IncomingEdgeBlockIdx =
1891 (RdxPhi)->getBasicBlockIndex(OrigLoop->getLoopLatch());
1892 assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
1893 // Pick the other block.
1894 int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
1895 (RdxPhi)->setIncomingValue(SelfEdgeBlockIdx, Scalar0);
1896 (RdxPhi)->setIncomingValue(IncomingEdgeBlockIdx, RdxDesc.LoopExitInstr);
1897 }// end of for each redux variable.
1899 // The Loop exit block may have single value PHI nodes where the incoming
1900 // value is 'undef'. While vectorizing we only handled real values that
1901 // were defined inside the loop. Here we handle the 'undef case'.
1903 for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
1904 LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) {
1905 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
1906 if (!LCSSAPhi) continue;
1907 if (LCSSAPhi->getNumIncomingValues() == 1)
1908 LCSSAPhi->addIncoming(UndefValue::get(LCSSAPhi->getType()),
1913 InnerLoopVectorizer::VectorParts
1914 InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) {
1915 assert(std::find(pred_begin(Dst), pred_end(Dst), Src) != pred_end(Dst) &&
1918 VectorParts SrcMask = createBlockInMask(Src);
1920 // The terminator has to be a branch inst!
1921 BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator());
1922 assert(BI && "Unexpected terminator found");
1924 if (BI->isConditional()) {
1925 VectorParts EdgeMask = getVectorValue(BI->getCondition());
1927 if (BI->getSuccessor(0) != Dst)
1928 for (unsigned part = 0; part < UF; ++part)
1929 EdgeMask[part] = Builder.CreateNot(EdgeMask[part]);
1931 for (unsigned part = 0; part < UF; ++part)
1932 EdgeMask[part] = Builder.CreateAnd(EdgeMask[part], SrcMask[part]);
1939 InnerLoopVectorizer::VectorParts
1940 InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) {
1941 assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
1943 // Loop incoming mask is all-one.
1944 if (OrigLoop->getHeader() == BB) {
1945 Value *C = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 1);
1946 return getVectorValue(C);
1949 // This is the block mask. We OR all incoming edges, and with zero.
1950 Value *Zero = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 0);
1951 VectorParts BlockMask = getVectorValue(Zero);
1954 for (pred_iterator it = pred_begin(BB), e = pred_end(BB); it != e; ++it) {
1955 VectorParts EM = createEdgeMask(*it, BB);
1956 for (unsigned part = 0; part < UF; ++part)
1957 BlockMask[part] = Builder.CreateOr(BlockMask[part], EM[part]);
1964 InnerLoopVectorizer::vectorizeBlockInLoop(LoopVectorizationLegality *Legal,
1965 BasicBlock *BB, PhiVector *PV) {
1966 // For each instruction in the old loop.
1967 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
1968 VectorParts &Entry = WidenMap.get(it);
1969 switch (it->getOpcode()) {
1970 case Instruction::Br:
1971 // Nothing to do for PHIs and BR, since we already took care of the
1972 // loop control flow instructions.
1974 case Instruction::PHI:{
1975 PHINode* P = cast<PHINode>(it);
1976 // Handle reduction variables:
1977 if (Legal->getReductionVars()->count(P)) {
1978 for (unsigned part = 0; part < UF; ++part) {
1979 // This is phase one of vectorizing PHIs.
1980 Type *VecTy = VectorType::get(it->getType(), VF);
1981 Entry[part] = PHINode::Create(VecTy, 2, "vec.phi",
1982 LoopVectorBody-> getFirstInsertionPt());
1988 // Check for PHI nodes that are lowered to vector selects.
1989 if (P->getParent() != OrigLoop->getHeader()) {
1990 // We know that all PHIs in non header blocks are converted into
1991 // selects, so we don't have to worry about the insertion order and we
1992 // can just use the builder.
1993 // At this point we generate the predication tree. There may be
1994 // duplications since this is a simple recursive scan, but future
1995 // optimizations will clean it up.
1997 unsigned NumIncoming = P->getNumIncomingValues();
1998 assert(NumIncoming > 1 && "Invalid PHI");
2000 // Generate a sequence of selects of the form:
2001 // SELECT(Mask3, In3,
2002 // SELECT(Mask2, In2,
2004 for (unsigned In = 0; In < NumIncoming; In++) {
2005 VectorParts Cond = createEdgeMask(P->getIncomingBlock(In),
2007 VectorParts &In0 = getVectorValue(P->getIncomingValue(In));
2009 for (unsigned part = 0; part < UF; ++part) {
2010 // We don't need to 'select' the first PHI operand because it is
2011 // the default value if all of the other masks don't match.
2013 Entry[part] = In0[part];
2015 // Select between the current value and the previous incoming edge
2016 // based on the incoming mask.
2017 Entry[part] = Builder.CreateSelect(Cond[part], In0[part],
2018 Entry[part], "predphi");
2024 // This PHINode must be an induction variable.
2025 // Make sure that we know about it.
2026 assert(Legal->getInductionVars()->count(P) &&
2027 "Not an induction variable");
2029 LoopVectorizationLegality::InductionInfo II =
2030 Legal->getInductionVars()->lookup(P);
2033 case LoopVectorizationLegality::IK_NoInduction:
2034 llvm_unreachable("Unknown induction");
2035 case LoopVectorizationLegality::IK_IntInduction: {
2036 assert(P == OldInduction && "Unexpected PHI");
2037 Value *Broadcasted = getBroadcastInstrs(Induction);
2038 // After broadcasting the induction variable we need to make the
2039 // vector consecutive by adding 0, 1, 2 ...
2040 for (unsigned part = 0; part < UF; ++part)
2041 Entry[part] = getConsecutiveVector(Broadcasted, VF * part, false);
2044 case LoopVectorizationLegality::IK_ReverseIntInduction:
2045 case LoopVectorizationLegality::IK_PtrInduction:
2046 case LoopVectorizationLegality::IK_ReversePtrInduction:
2047 // Handle reverse integer and pointer inductions.
2048 Value *StartIdx = 0;
2049 // If we have a single integer induction variable then use it.
2050 // Otherwise, start counting at zero.
2052 LoopVectorizationLegality::InductionInfo OldII =
2053 Legal->getInductionVars()->lookup(OldInduction);
2054 StartIdx = OldII.StartValue;
2056 StartIdx = ConstantInt::get(Induction->getType(), 0);
2058 // This is the normalized GEP that starts counting at zero.
2059 Value *NormalizedIdx = Builder.CreateSub(Induction, StartIdx,
2062 // Handle the reverse integer induction variable case.
2063 if (LoopVectorizationLegality::IK_ReverseIntInduction == II.IK) {
2064 IntegerType *DstTy = cast<IntegerType>(II.StartValue->getType());
2065 Value *CNI = Builder.CreateSExtOrTrunc(NormalizedIdx, DstTy,
2067 Value *ReverseInd = Builder.CreateSub(II.StartValue, CNI,
2070 // This is a new value so do not hoist it out.
2071 Value *Broadcasted = getBroadcastInstrs(ReverseInd);
2072 // After broadcasting the induction variable we need to make the
2073 // vector consecutive by adding ... -3, -2, -1, 0.
2074 for (unsigned part = 0; part < UF; ++part)
2075 Entry[part] = getConsecutiveVector(Broadcasted, -(int)VF * part,
2080 // Handle the pointer induction variable case.
2081 assert(P->getType()->isPointerTy() && "Unexpected type.");
2083 // Is this a reverse induction ptr or a consecutive induction ptr.
2084 bool Reverse = (LoopVectorizationLegality::IK_ReversePtrInduction ==
2087 // This is the vector of results. Notice that we don't generate
2088 // vector geps because scalar geps result in better code.
2089 for (unsigned part = 0; part < UF; ++part) {
2090 Value *VecVal = UndefValue::get(VectorType::get(P->getType(), VF));
2091 for (unsigned int i = 0; i < VF; ++i) {
2092 int EltIndex = (i + part * VF) * (Reverse ? -1 : 1);
2093 Constant *Idx = ConstantInt::get(Induction->getType(), EltIndex);
2096 GlobalIdx = Builder.CreateAdd(NormalizedIdx, Idx, "gep.idx");
2098 GlobalIdx = Builder.CreateSub(Idx, NormalizedIdx, "gep.ridx");
2100 Value *SclrGep = Builder.CreateGEP(II.StartValue, GlobalIdx,
2102 VecVal = Builder.CreateInsertElement(VecVal, SclrGep,
2103 Builder.getInt32(i),
2106 Entry[part] = VecVal;
2113 case Instruction::Add:
2114 case Instruction::FAdd:
2115 case Instruction::Sub:
2116 case Instruction::FSub:
2117 case Instruction::Mul:
2118 case Instruction::FMul:
2119 case Instruction::UDiv:
2120 case Instruction::SDiv:
2121 case Instruction::FDiv:
2122 case Instruction::URem:
2123 case Instruction::SRem:
2124 case Instruction::FRem:
2125 case Instruction::Shl:
2126 case Instruction::LShr:
2127 case Instruction::AShr:
2128 case Instruction::And:
2129 case Instruction::Or:
2130 case Instruction::Xor: {
2131 // Just widen binops.
2132 BinaryOperator *BinOp = dyn_cast<BinaryOperator>(it);
2133 VectorParts &A = getVectorValue(it->getOperand(0));
2134 VectorParts &B = getVectorValue(it->getOperand(1));
2136 // Use this vector value for all users of the original instruction.
2137 for (unsigned Part = 0; Part < UF; ++Part) {
2138 Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A[Part], B[Part]);
2140 // Update the NSW, NUW and Exact flags. Notice: V can be an Undef.
2141 BinaryOperator *VecOp = dyn_cast<BinaryOperator>(V);
2142 if (VecOp && isa<OverflowingBinaryOperator>(BinOp)) {
2143 VecOp->setHasNoSignedWrap(BinOp->hasNoSignedWrap());
2144 VecOp->setHasNoUnsignedWrap(BinOp->hasNoUnsignedWrap());
2146 if (VecOp && isa<PossiblyExactOperator>(VecOp))
2147 VecOp->setIsExact(BinOp->isExact());
2153 case Instruction::Select: {
2155 // If the selector is loop invariant we can create a select
2156 // instruction with a scalar condition. Otherwise, use vector-select.
2157 bool InvariantCond = SE->isLoopInvariant(SE->getSCEV(it->getOperand(0)),
2160 // The condition can be loop invariant but still defined inside the
2161 // loop. This means that we can't just use the original 'cond' value.
2162 // We have to take the 'vectorized' value and pick the first lane.
2163 // Instcombine will make this a no-op.
2164 VectorParts &Cond = getVectorValue(it->getOperand(0));
2165 VectorParts &Op0 = getVectorValue(it->getOperand(1));
2166 VectorParts &Op1 = getVectorValue(it->getOperand(2));
2167 Value *ScalarCond = Builder.CreateExtractElement(Cond[0],
2168 Builder.getInt32(0));
2169 for (unsigned Part = 0; Part < UF; ++Part) {
2170 Entry[Part] = Builder.CreateSelect(
2171 InvariantCond ? ScalarCond : Cond[Part],
2178 case Instruction::ICmp:
2179 case Instruction::FCmp: {
2180 // Widen compares. Generate vector compares.
2181 bool FCmp = (it->getOpcode() == Instruction::FCmp);
2182 CmpInst *Cmp = dyn_cast<CmpInst>(it);
2183 VectorParts &A = getVectorValue(it->getOperand(0));
2184 VectorParts &B = getVectorValue(it->getOperand(1));
2185 for (unsigned Part = 0; Part < UF; ++Part) {
2188 C = Builder.CreateFCmp(Cmp->getPredicate(), A[Part], B[Part]);
2190 C = Builder.CreateICmp(Cmp->getPredicate(), A[Part], B[Part]);
2196 case Instruction::Store:
2197 case Instruction::Load:
2198 vectorizeMemoryInstruction(it, Legal);
2200 case Instruction::ZExt:
2201 case Instruction::SExt:
2202 case Instruction::FPToUI:
2203 case Instruction::FPToSI:
2204 case Instruction::FPExt:
2205 case Instruction::PtrToInt:
2206 case Instruction::IntToPtr:
2207 case Instruction::SIToFP:
2208 case Instruction::UIToFP:
2209 case Instruction::Trunc:
2210 case Instruction::FPTrunc:
2211 case Instruction::BitCast: {
2212 CastInst *CI = dyn_cast<CastInst>(it);
2213 /// Optimize the special case where the source is the induction
2214 /// variable. Notice that we can only optimize the 'trunc' case
2215 /// because: a. FP conversions lose precision, b. sext/zext may wrap,
2216 /// c. other casts depend on pointer size.
2217 if (CI->getOperand(0) == OldInduction &&
2218 it->getOpcode() == Instruction::Trunc) {
2219 Value *ScalarCast = Builder.CreateCast(CI->getOpcode(), Induction,
2221 Value *Broadcasted = getBroadcastInstrs(ScalarCast);
2222 for (unsigned Part = 0; Part < UF; ++Part)
2223 Entry[Part] = getConsecutiveVector(Broadcasted, VF * Part, false);
2226 /// Vectorize casts.
2227 Type *DestTy = VectorType::get(CI->getType()->getScalarType(), VF);
2229 VectorParts &A = getVectorValue(it->getOperand(0));
2230 for (unsigned Part = 0; Part < UF; ++Part)
2231 Entry[Part] = Builder.CreateCast(CI->getOpcode(), A[Part], DestTy);
2235 case Instruction::Call: {
2236 // Ignore dbg intrinsics.
2237 if (isa<DbgInfoIntrinsic>(it))
2240 Module *M = BB->getParent()->getParent();
2241 CallInst *CI = cast<CallInst>(it);
2242 Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI);
2243 assert(ID && "Not an intrinsic call!");
2244 for (unsigned Part = 0; Part < UF; ++Part) {
2245 SmallVector<Value*, 4> Args;
2246 for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) {
2247 VectorParts &Arg = getVectorValue(CI->getArgOperand(i));
2248 Args.push_back(Arg[Part]);
2250 Type *Tys[] = { VectorType::get(CI->getType()->getScalarType(), VF) };
2251 Function *F = Intrinsic::getDeclaration(M, ID, Tys);
2252 Entry[Part] = Builder.CreateCall(F, Args);
2258 // All other instructions are unsupported. Scalarize them.
2259 scalarizeInstruction(it);
2262 }// end of for_each instr.
2265 void InnerLoopVectorizer::updateAnalysis() {
2266 // Forget the original basic block.
2267 SE->forgetLoop(OrigLoop);
2269 // Update the dominator tree information.
2270 assert(DT->properlyDominates(LoopBypassBlocks.front(), LoopExitBlock) &&
2271 "Entry does not dominate exit.");
2273 for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
2274 DT->addNewBlock(LoopBypassBlocks[I], LoopBypassBlocks[I-1]);
2275 DT->addNewBlock(LoopVectorPreHeader, LoopBypassBlocks.back());
2276 DT->addNewBlock(LoopVectorBody, LoopVectorPreHeader);
2277 DT->addNewBlock(LoopMiddleBlock, LoopBypassBlocks.front());
2278 DT->addNewBlock(LoopScalarPreHeader, LoopMiddleBlock);
2279 DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader);
2280 DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock);
2282 DEBUG(DT->verifyAnalysis());
2285 bool LoopVectorizationLegality::canVectorizeWithIfConvert() {
2286 if (!EnableIfConversion)
2289 assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable");
2290 std::vector<BasicBlock*> &LoopBlocks = TheLoop->getBlocksVector();
2292 // Collect the blocks that need predication.
2293 for (unsigned i = 0, e = LoopBlocks.size(); i < e; ++i) {
2294 BasicBlock *BB = LoopBlocks[i];
2296 // We don't support switch statements inside loops.
2297 if (!isa<BranchInst>(BB->getTerminator()))
2300 // We must be able to predicate all blocks that need to be predicated.
2301 if (blockNeedsPredication(BB) && !blockCanBePredicated(BB))
2305 // We can if-convert this loop.
2309 bool LoopVectorizationLegality::canVectorize() {
2310 assert(TheLoop->getLoopPreheader() && "No preheader!!");
2312 // We can only vectorize innermost loops.
2313 if (TheLoop->getSubLoopsVector().size())
2316 // We must have a single backedge.
2317 if (TheLoop->getNumBackEdges() != 1)
2320 // We must have a single exiting block.
2321 if (!TheLoop->getExitingBlock())
2324 unsigned NumBlocks = TheLoop->getNumBlocks();
2326 // Check if we can if-convert non single-bb loops.
2327 if (NumBlocks != 1 && !canVectorizeWithIfConvert()) {
2328 DEBUG(dbgs() << "LV: Can't if-convert the loop.\n");
2332 // We need to have a loop header.
2333 BasicBlock *Latch = TheLoop->getLoopLatch();
2334 DEBUG(dbgs() << "LV: Found a loop: " <<
2335 TheLoop->getHeader()->getName() << "\n");
2337 // ScalarEvolution needs to be able to find the exit count.
2338 const SCEV *ExitCount = SE->getExitCount(TheLoop, Latch);
2339 if (ExitCount == SE->getCouldNotCompute()) {
2340 DEBUG(dbgs() << "LV: SCEV could not compute the loop exit count.\n");
2344 // Do not loop-vectorize loops with a tiny trip count.
2345 unsigned TC = SE->getSmallConstantTripCount(TheLoop, Latch);
2346 if (TC > 0u && TC < TinyTripCountVectorThreshold) {
2347 DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " <<
2348 "This loop is not worth vectorizing.\n");
2352 // Check if we can vectorize the instructions and CFG in this loop.
2353 if (!canVectorizeInstrs()) {
2354 DEBUG(dbgs() << "LV: Can't vectorize the instructions or CFG\n");
2358 // Go over each instruction and look at memory deps.
2359 if (!canVectorizeMemory()) {
2360 DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n");
2364 // Collect all of the variables that remain uniform after vectorization.
2365 collectLoopUniforms();
2367 DEBUG(dbgs() << "LV: We can vectorize this loop" <<
2368 (PtrRtCheck.Need ? " (with a runtime bound check)" : "")
2371 // Okay! We can vectorize. At this point we don't have any other mem analysis
2372 // which may limit our maximum vectorization factor, so just return true with
2377 bool LoopVectorizationLegality::canVectorizeInstrs() {
2378 BasicBlock *PreHeader = TheLoop->getLoopPreheader();
2379 BasicBlock *Header = TheLoop->getHeader();
2381 // If we marked the scalar loop as "already vectorized" then no need
2382 // to vectorize it again.
2383 if (Header->getTerminator()->getMetadata(AlreadyVectorizedMDName)) {
2384 DEBUG(dbgs() << "LV: This loop was vectorized before\n");
2388 // Look for the attribute signaling the absence of NaNs.
2389 Function &F = *Header->getParent();
2390 if (F.hasFnAttribute("no-nans-fp-math"))
2391 HasFunNoNaNAttr = F.getAttributes().getAttribute(
2392 AttributeSet::FunctionIndex,
2393 "no-nans-fp-math").getValueAsString() == "true";
2395 // For each block in the loop.
2396 for (Loop::block_iterator bb = TheLoop->block_begin(),
2397 be = TheLoop->block_end(); bb != be; ++bb) {
2399 // Scan the instructions in the block and look for hazards.
2400 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
2403 if (PHINode *Phi = dyn_cast<PHINode>(it)) {
2404 // Check that this PHI type is allowed.
2405 if (!Phi->getType()->isIntegerTy() &&
2406 !Phi->getType()->isFloatingPointTy() &&
2407 !Phi->getType()->isPointerTy()) {
2408 DEBUG(dbgs() << "LV: Found an non-int non-pointer PHI.\n");
2412 // If this PHINode is not in the header block, then we know that we
2413 // can convert it to select during if-conversion. No need to check if
2414 // the PHIs in this block are induction or reduction variables.
2418 // We only allow if-converted PHIs with more than two incoming values.
2419 if (Phi->getNumIncomingValues() != 2) {
2420 DEBUG(dbgs() << "LV: Found an invalid PHI.\n");
2424 // This is the value coming from the preheader.
2425 Value *StartValue = Phi->getIncomingValueForBlock(PreHeader);
2426 // Check if this is an induction variable.
2427 InductionKind IK = isInductionVariable(Phi);
2429 if (IK_NoInduction != IK) {
2430 // Int inductions are special because we only allow one IV.
2431 if (IK == IK_IntInduction) {
2433 DEBUG(dbgs() << "LV: Found too many inductions."<< *Phi <<"\n");
2439 DEBUG(dbgs() << "LV: Found an induction variable.\n");
2440 Inductions[Phi] = InductionInfo(StartValue, IK);
2444 if (AddReductionVar(Phi, RK_IntegerAdd)) {
2445 DEBUG(dbgs() << "LV: Found an ADD reduction PHI."<< *Phi <<"\n");
2448 if (AddReductionVar(Phi, RK_IntegerMult)) {
2449 DEBUG(dbgs() << "LV: Found a MUL reduction PHI."<< *Phi <<"\n");
2452 if (AddReductionVar(Phi, RK_IntegerOr)) {
2453 DEBUG(dbgs() << "LV: Found an OR reduction PHI."<< *Phi <<"\n");
2456 if (AddReductionVar(Phi, RK_IntegerAnd)) {
2457 DEBUG(dbgs() << "LV: Found an AND reduction PHI."<< *Phi <<"\n");
2460 if (AddReductionVar(Phi, RK_IntegerXor)) {
2461 DEBUG(dbgs() << "LV: Found a XOR reduction PHI."<< *Phi <<"\n");
2464 if (AddReductionVar(Phi, RK_IntegerMinMax)) {
2465 DEBUG(dbgs() << "LV: Found a MINMAX reduction PHI."<< *Phi <<"\n");
2468 if (AddReductionVar(Phi, RK_FloatMult)) {
2469 DEBUG(dbgs() << "LV: Found an FMult reduction PHI."<< *Phi <<"\n");
2472 if (AddReductionVar(Phi, RK_FloatAdd)) {
2473 DEBUG(dbgs() << "LV: Found an FAdd reduction PHI."<< *Phi <<"\n");
2476 if (AddReductionVar(Phi, RK_FloatMinMax)) {
2477 DEBUG(dbgs() << "LV: Found an float MINMAX reduction PHI."<< *Phi <<"\n");
2481 DEBUG(dbgs() << "LV: Found an unidentified PHI."<< *Phi <<"\n");
2483 }// end of PHI handling
2485 // We still don't handle functions. However, we can ignore dbg intrinsic
2486 // calls and we do handle certain intrinsic and libm functions.
2487 CallInst *CI = dyn_cast<CallInst>(it);
2488 if (CI && !getIntrinsicIDForCall(CI, TLI) && !isa<DbgInfoIntrinsic>(CI)) {
2489 DEBUG(dbgs() << "LV: Found a call site.\n");
2493 // Check that the instruction return type is vectorizable.
2494 if (!VectorType::isValidElementType(it->getType()) &&
2495 !it->getType()->isVoidTy()) {
2496 DEBUG(dbgs() << "LV: Found unvectorizable type." << "\n");
2500 // Check that the stored type is vectorizable.
2501 if (StoreInst *ST = dyn_cast<StoreInst>(it)) {
2502 Type *T = ST->getValueOperand()->getType();
2503 if (!VectorType::isValidElementType(T))
2507 // Reduction instructions are allowed to have exit users.
2508 // All other instructions must not have external users.
2509 if (!AllowedExit.count(it))
2510 //Check that all of the users of the loop are inside the BB.
2511 for (Value::use_iterator I = it->use_begin(), E = it->use_end();
2513 Instruction *U = cast<Instruction>(*I);
2514 // This user may be a reduction exit value.
2515 if (!TheLoop->contains(U)) {
2516 DEBUG(dbgs() << "LV: Found an outside user for : "<< *U << "\n");
2525 DEBUG(dbgs() << "LV: Did not find one integer induction var.\n");
2526 assert(getInductionVars()->size() && "No induction variables");
2532 void LoopVectorizationLegality::collectLoopUniforms() {
2533 // We now know that the loop is vectorizable!
2534 // Collect variables that will remain uniform after vectorization.
2535 std::vector<Value*> Worklist;
2536 BasicBlock *Latch = TheLoop->getLoopLatch();
2538 // Start with the conditional branch and walk up the block.
2539 Worklist.push_back(Latch->getTerminator()->getOperand(0));
2541 while (Worklist.size()) {
2542 Instruction *I = dyn_cast<Instruction>(Worklist.back());
2543 Worklist.pop_back();
2545 // Look at instructions inside this loop.
2546 // Stop when reaching PHI nodes.
2547 // TODO: we need to follow values all over the loop, not only in this block.
2548 if (!I || !TheLoop->contains(I) || isa<PHINode>(I))
2551 // This is a known uniform.
2554 // Insert all operands.
2555 for (int i = 0, Op = I->getNumOperands(); i < Op; ++i) {
2556 Worklist.push_back(I->getOperand(i));
2561 AliasAnalysis::Location
2562 LoopVectorizationLegality::getLoadStoreLocation(Instruction *Inst) {
2563 if (StoreInst *Store = dyn_cast<StoreInst>(Inst))
2564 return AA->getLocation(Store);
2565 else if (LoadInst *Load = dyn_cast<LoadInst>(Inst))
2566 return AA->getLocation(Load);
2568 llvm_unreachable("Should be either load or store instruction");
2572 LoopVectorizationLegality::hasPossibleGlobalWriteReorder(
2575 AliasMultiMap& WriteObjects,
2576 unsigned MaxByteWidth) {
2578 AliasAnalysis::Location ThisLoc = getLoadStoreLocation(Inst);
2580 std::vector<Instruction*>::iterator
2581 it = WriteObjects[Object].begin(),
2582 end = WriteObjects[Object].end();
2584 for (; it != end; ++it) {
2585 Instruction* I = *it;
2589 AliasAnalysis::Location ThatLoc = getLoadStoreLocation(I);
2590 if (AA->alias(ThisLoc.getWithNewSize(MaxByteWidth),
2591 ThatLoc.getWithNewSize(MaxByteWidth)))
2597 bool LoopVectorizationLegality::canVectorizeMemory() {
2599 typedef SmallVector<Value*, 16> ValueVector;
2600 typedef SmallPtrSet<Value*, 16> ValueSet;
2601 // Holds the Load and Store *instructions*.
2604 PtrRtCheck.Pointers.clear();
2605 PtrRtCheck.Need = false;
2607 const bool IsAnnotatedParallel = TheLoop->isAnnotatedParallel();
2610 for (Loop::block_iterator bb = TheLoop->block_begin(),
2611 be = TheLoop->block_end(); bb != be; ++bb) {
2613 // Scan the BB and collect legal loads and stores.
2614 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
2617 // If this is a load, save it. If this instruction can read from memory
2618 // but is not a load, then we quit. Notice that we don't handle function
2619 // calls that read or write.
2620 if (it->mayReadFromMemory()) {
2621 LoadInst *Ld = dyn_cast<LoadInst>(it);
2622 if (!Ld) return false;
2623 if (!Ld->isSimple() && !IsAnnotatedParallel) {
2624 DEBUG(dbgs() << "LV: Found a non-simple load.\n");
2627 Loads.push_back(Ld);
2631 // Save 'store' instructions. Abort if other instructions write to memory.
2632 if (it->mayWriteToMemory()) {
2633 StoreInst *St = dyn_cast<StoreInst>(it);
2634 if (!St) return false;
2635 if (!St->isSimple() && !IsAnnotatedParallel) {
2636 DEBUG(dbgs() << "LV: Found a non-simple store.\n");
2639 Stores.push_back(St);
2644 // Now we have two lists that hold the loads and the stores.
2645 // Next, we find the pointers that they use.
2647 // Check if we see any stores. If there are no stores, then we don't
2648 // care if the pointers are *restrict*.
2649 if (!Stores.size()) {
2650 DEBUG(dbgs() << "LV: Found a read-only loop!\n");
2654 // Holds the read and read-write *pointers* that we find. These maps hold
2655 // unique values for pointers (so no need for multi-map).
2657 AliasMap ReadWrites;
2659 // Holds the analyzed pointers. We don't want to call GetUnderlyingObjects
2660 // multiple times on the same object. If the ptr is accessed twice, once
2661 // for read and once for write, it will only appear once (on the write
2662 // list). This is okay, since we are going to check for conflicts between
2663 // writes and between reads and writes, but not between reads and reads.
2666 ValueVector::iterator I, IE;
2667 for (I = Stores.begin(), IE = Stores.end(); I != IE; ++I) {
2668 StoreInst *ST = cast<StoreInst>(*I);
2669 Value* Ptr = ST->getPointerOperand();
2671 if (isUniform(Ptr)) {
2672 DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n");
2676 // If we did *not* see this pointer before, insert it to
2677 // the read-write list. At this phase it is only a 'write' list.
2678 if (Seen.insert(Ptr))
2679 ReadWrites.insert(std::make_pair(Ptr, ST));
2682 if (IsAnnotatedParallel) {
2684 << "LV: A loop annotated parallel, ignore memory dependency "
2689 for (I = Loads.begin(), IE = Loads.end(); I != IE; ++I) {
2690 LoadInst *LD = cast<LoadInst>(*I);
2691 Value* Ptr = LD->getPointerOperand();
2692 // If we did *not* see this pointer before, insert it to the
2693 // read list. If we *did* see it before, then it is already in
2694 // the read-write list. This allows us to vectorize expressions
2695 // such as A[i] += x; Because the address of A[i] is a read-write
2696 // pointer. This only works if the index of A[i] is consecutive.
2697 // If the address of i is unknown (for example A[B[i]]) then we may
2698 // read a few words, modify, and write a few words, and some of the
2699 // words may be written to the same address.
2700 if (Seen.insert(Ptr) || 0 == isConsecutivePtr(Ptr))
2701 Reads.insert(std::make_pair(Ptr, LD));
2704 // If we write (or read-write) to a single destination and there are no
2705 // other reads in this loop then is it safe to vectorize.
2706 if (ReadWrites.size() == 1 && Reads.size() == 0) {
2707 DEBUG(dbgs() << "LV: Found a write-only loop!\n");
2711 unsigned NumReadPtrs = 0;
2712 unsigned NumWritePtrs = 0;
2714 // Find pointers with computable bounds. We are going to use this information
2715 // to place a runtime bound check.
2716 bool CanDoRT = true;
2717 AliasMap::iterator MI, ME;
2718 for (MI = ReadWrites.begin(), ME = ReadWrites.end(); MI != ME; ++MI) {
2719 Value *V = (*MI).first;
2720 if (hasComputableBounds(V)) {
2721 PtrRtCheck.insert(SE, TheLoop, V, true);
2723 DEBUG(dbgs() << "LV: Found a runtime check ptr:" << *V <<"\n");
2729 for (MI = Reads.begin(), ME = Reads.end(); MI != ME; ++MI) {
2730 Value *V = (*MI).first;
2731 if (hasComputableBounds(V)) {
2732 PtrRtCheck.insert(SE, TheLoop, V, false);
2734 DEBUG(dbgs() << "LV: Found a runtime check ptr:" << *V <<"\n");
2741 // Check that we did not collect too many pointers or found a
2742 // unsizeable pointer.
2743 unsigned NumComparisons = (NumWritePtrs * (NumReadPtrs + NumWritePtrs - 1));
2744 DEBUG(dbgs() << "LV: We need to compare " << NumComparisons << " ptrs.\n");
2745 if (!CanDoRT || NumComparisons > RuntimeMemoryCheckThreshold) {
2751 DEBUG(dbgs() << "LV: We can perform a memory runtime check if needed.\n");
2754 bool NeedRTCheck = false;
2756 // Biggest vectorized access possible, vector width * unroll factor.
2757 // TODO: We're being very pessimistic here, find a way to know the
2758 // real access width before getting here.
2759 unsigned MaxByteWidth = (TTI->getRegisterBitWidth(true) / 8) *
2760 TTI->getMaximumUnrollFactor();
2761 // Now that the pointers are in two lists (Reads and ReadWrites), we
2762 // can check that there are no conflicts between each of the writes and
2763 // between the writes to the reads.
2764 // Note that WriteObjects duplicates the stores (indexed now by underlying
2765 // objects) to avoid pointing to elements inside ReadWrites.
2766 // TODO: Maybe create a new type where they can interact without duplication.
2767 AliasMultiMap WriteObjects;
2768 ValueVector TempObjects;
2770 // Check that the read-writes do not conflict with other read-write
2772 bool AllWritesIdentified = true;
2773 for (MI = ReadWrites.begin(), ME = ReadWrites.end(); MI != ME; ++MI) {
2774 Value *Val = (*MI).first;
2775 Instruction *Inst = (*MI).second;
2777 GetUnderlyingObjects(Val, TempObjects, DL);
2778 for (ValueVector::iterator UI=TempObjects.begin(), UE=TempObjects.end();
2780 if (!isIdentifiedObject(*UI)) {
2781 DEBUG(dbgs() << "LV: Found an unidentified write ptr:"<< **UI <<"\n");
2783 AllWritesIdentified = false;
2786 // Never seen it before, can't alias.
2787 if (WriteObjects[*UI].empty()) {
2788 DEBUG(dbgs() << "LV: Adding Underlying value:" << **UI <<"\n");
2789 WriteObjects[*UI].push_back(Inst);
2792 // Direct alias found.
2793 if (!AA || dyn_cast<GlobalValue>(*UI) == NULL) {
2794 DEBUG(dbgs() << "LV: Found a possible write-write reorder:"
2798 DEBUG(dbgs() << "LV: Found a conflicting global value:"
2800 DEBUG(dbgs() << "LV: While examining store:" << *Inst <<"\n");
2801 DEBUG(dbgs() << "LV: On value:" << *Val <<"\n");
2803 // If global alias, make sure they do alias.
2804 if (hasPossibleGlobalWriteReorder(*UI,
2808 DEBUG(dbgs() << "LV: Found a possible write-write reorder:" << **UI
2813 // Didn't alias, insert into map for further reference.
2814 WriteObjects[*UI].push_back(Inst);
2816 TempObjects.clear();
2819 /// Check that the reads don't conflict with the read-writes.
2820 for (MI = Reads.begin(), ME = Reads.end(); MI != ME; ++MI) {
2821 Value *Val = (*MI).first;
2822 GetUnderlyingObjects(Val, TempObjects, DL);
2823 for (ValueVector::iterator UI=TempObjects.begin(), UE=TempObjects.end();
2825 // If all of the writes are identified then we don't care if the read
2826 // pointer is identified or not.
2827 if (!AllWritesIdentified && !isIdentifiedObject(*UI)) {
2828 DEBUG(dbgs() << "LV: Found an unidentified read ptr:"<< **UI <<"\n");
2832 // Never seen it before, can't alias.
2833 if (WriteObjects[*UI].empty())
2835 // Direct alias found.
2836 if (!AA || dyn_cast<GlobalValue>(*UI) == NULL) {
2837 DEBUG(dbgs() << "LV: Found a possible write-write reorder:"
2841 DEBUG(dbgs() << "LV: Found a global value: "
2843 Instruction *Inst = (*MI).second;
2844 DEBUG(dbgs() << "LV: While examining load:" << *Inst <<"\n");
2845 DEBUG(dbgs() << "LV: On value:" << *Val <<"\n");
2847 // If global alias, make sure they do alias.
2848 if (hasPossibleGlobalWriteReorder(*UI,
2852 DEBUG(dbgs() << "LV: Found a possible read-write reorder:" << **UI
2857 TempObjects.clear();
2860 PtrRtCheck.Need = NeedRTCheck;
2861 if (NeedRTCheck && !CanDoRT) {
2862 DEBUG(dbgs() << "LV: We can't vectorize because we can't find " <<
2863 "the array bounds.\n");
2868 DEBUG(dbgs() << "LV: We "<< (NeedRTCheck ? "" : "don't") <<
2869 " need a runtime memory check.\n");
2873 bool LoopVectorizationLegality::AddReductionVar(PHINode *Phi,
2874 ReductionKind Kind) {
2875 if (Phi->getNumIncomingValues() != 2)
2878 // Reduction variables are only found in the loop header block.
2879 if (Phi->getParent() != TheLoop->getHeader())
2882 // Obtain the reduction start value from the value that comes from the loop
2884 Value *RdxStart = Phi->getIncomingValueForBlock(TheLoop->getLoopPreheader());
2886 // ExitInstruction is the single value which is used outside the loop.
2887 // We only allow for a single reduction value to be used outside the loop.
2888 // This includes users of the reduction, variables (which form a cycle
2889 // which ends in the phi node).
2890 Instruction *ExitInstruction = 0;
2891 // Indicates that we found a binary operation in our scan.
2892 bool FoundBinOp = false;
2894 // Iter is our iterator. We start with the PHI node and scan for all of the
2895 // users of this instruction. All users must be instructions that can be
2896 // used as reduction variables (such as ADD). We may have a single
2897 // out-of-block user. The cycle must end with the original PHI.
2898 Instruction *Iter = Phi;
2900 // To recognize min/max patterns formed by a icmp select sequence, we store
2901 // the number of instruction we saw from the recognized min/max pattern,
2902 // such that we don't stop when we see the phi has two uses (one by the select
2903 // and one by the icmp) and to make sure we only see exactly the two
2905 unsigned NumCmpSelectPatternInst = 0;
2906 ReductionInstDesc ReduxDesc(false, 0);
2908 // Avoid cycles in the chain.
2909 SmallPtrSet<Instruction *, 8> VisitedInsts;
2910 while (VisitedInsts.insert(Iter)) {
2911 // If the instruction has no users then this is a broken
2912 // chain and can't be a reduction variable.
2913 if (Iter->use_empty())
2916 // Did we find a user inside this loop already ?
2917 bool FoundInBlockUser = false;
2918 // Did we reach the initial PHI node already ?
2919 bool FoundStartPHI = false;
2921 // Is this a bin op ?
2922 FoundBinOp |= !isa<PHINode>(Iter);
2924 // For each of the *users* of iter.
2925 for (Value::use_iterator it = Iter->use_begin(), e = Iter->use_end();
2927 Instruction *U = cast<Instruction>(*it);
2928 // We already know that the PHI is a user.
2930 FoundStartPHI = true;
2934 // Check if we found the exit user.
2935 BasicBlock *Parent = U->getParent();
2936 if (!TheLoop->contains(Parent)) {
2937 // Exit if you find multiple outside users.
2938 if (ExitInstruction != 0)
2940 ExitInstruction = Iter;
2943 // We allow in-loop PHINodes which are not the original reduction PHI
2944 // node. If this PHI is the only user of Iter (happens in IF w/ no ELSE
2945 // structure) then don't skip this PHI.
2946 if (isa<PHINode>(Iter) && isa<PHINode>(U) &&
2947 U->getParent() != TheLoop->getHeader() &&
2948 TheLoop->contains(U) &&
2949 Iter->hasNUsesOrMore(2))
2952 // We can't have multiple inside users except for a combination of
2953 // icmp/select both using the phi.
2954 if (FoundInBlockUser && !NumCmpSelectPatternInst)
2956 FoundInBlockUser = true;
2958 // Any reduction instr must be of one of the allowed kinds.
2959 ReduxDesc = isReductionInstr(U, Kind, ReduxDesc);
2960 if (!ReduxDesc.IsReduction)
2963 if (Kind == RK_IntegerMinMax && (isa<ICmpInst>(U) || isa<SelectInst>(U)))
2964 ++NumCmpSelectPatternInst;
2965 if (Kind == RK_FloatMinMax && (isa<FCmpInst>(U) || isa<SelectInst>(U)))
2966 ++NumCmpSelectPatternInst;
2968 // Reductions of instructions such as Div, and Sub is only
2969 // possible if the LHS is the reduction variable.
2970 if (!U->isCommutative() && !isa<PHINode>(U) && !isa<SelectInst>(U) &&
2971 !isa<ICmpInst>(U) && !isa<FCmpInst>(U) && U->getOperand(0) != Iter)
2974 Iter = ReduxDesc.PatternLastInst;
2977 // This means we have seen one but not the other instruction of the
2978 // pattern or more than just a select and cmp.
2979 if ((Kind == RK_IntegerMinMax || Kind == RK_FloatMinMax) &&
2980 NumCmpSelectPatternInst != 2)
2983 // We found a reduction var if we have reached the original
2984 // phi node and we only have a single instruction with out-of-loop
2986 if (FoundStartPHI) {
2987 // This instruction is allowed to have out-of-loop users.
2988 AllowedExit.insert(ExitInstruction);
2990 // Save the description of this reduction variable.
2991 ReductionDescriptor RD(RdxStart, ExitInstruction, Kind,
2992 ReduxDesc.MinMaxKind);
2993 Reductions[Phi] = RD;
2994 // We've ended the cycle. This is a reduction variable if we have an
2995 // outside user and it has a binary op.
2996 return FoundBinOp && ExitInstruction;
3003 /// Returns true if the instruction is a Select(ICmp(X, Y), X, Y) instruction
3004 /// pattern corresponding to a min(X, Y) or max(X, Y).
3005 LoopVectorizationLegality::ReductionInstDesc
3006 LoopVectorizationLegality::isMinMaxSelectCmpPattern(Instruction *I,
3007 ReductionInstDesc &Prev) {
3009 assert((isa<ICmpInst>(I) || isa<FCmpInst>(I) || isa<SelectInst>(I)) &&
3010 "Expect a select instruction");
3011 Instruction *Cmp = 0;
3012 SelectInst *Select = 0;
3014 // We must handle the select(cmp()) as a single instruction. Advance to the
3016 if ((Cmp = dyn_cast<ICmpInst>(I)) || (Cmp = dyn_cast<FCmpInst>(I))) {
3017 if (!Cmp->hasOneUse() || !(Select = dyn_cast<SelectInst>(*I->use_begin())))
3018 return ReductionInstDesc(false, I);
3019 return ReductionInstDesc(Select, Prev.MinMaxKind);
3022 // Only handle single use cases for now.
3023 if (!(Select = dyn_cast<SelectInst>(I)))
3024 return ReductionInstDesc(false, I);
3025 if (!(Cmp = dyn_cast<ICmpInst>(I->getOperand(0))) &&
3026 !(Cmp = dyn_cast<FCmpInst>(I->getOperand(0))))
3027 return ReductionInstDesc(false, I);
3028 if (!Cmp->hasOneUse())
3029 return ReductionInstDesc(false, I);
3034 // Look for a min/max pattern.
3035 if (m_UMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3036 return ReductionInstDesc(Select, MRK_UIntMin);
3037 else if (m_UMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3038 return ReductionInstDesc(Select, MRK_UIntMax);
3039 else if (m_SMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3040 return ReductionInstDesc(Select, MRK_SIntMax);
3041 else if (m_SMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3042 return ReductionInstDesc(Select, MRK_SIntMin);
3043 else if (m_OrdFMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3044 return ReductionInstDesc(Select, MRK_FloatMin);
3045 else if (m_OrdFMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3046 return ReductionInstDesc(Select, MRK_FloatMax);
3047 else if (m_UnordFMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3048 return ReductionInstDesc(Select, MRK_FloatMin);
3049 else if (m_UnordFMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
3050 return ReductionInstDesc(Select, MRK_FloatMax);
3052 return ReductionInstDesc(false, I);
3055 LoopVectorizationLegality::ReductionInstDesc
3056 LoopVectorizationLegality::isReductionInstr(Instruction *I,
3058 ReductionInstDesc &Prev) {
3059 bool FP = I->getType()->isFloatingPointTy();
3060 bool FastMath = (FP && I->isCommutative() && I->isAssociative());
3061 switch (I->getOpcode()) {
3063 return ReductionInstDesc(false, I);
3064 case Instruction::PHI:
3065 if (FP && (Kind != RK_FloatMult && Kind != RK_FloatAdd &&
3066 Kind != RK_FloatMinMax))
3067 return ReductionInstDesc(false, I);
3068 return ReductionInstDesc(I, Prev.MinMaxKind);
3069 case Instruction::Sub:
3070 case Instruction::Add:
3071 return ReductionInstDesc(Kind == RK_IntegerAdd, I);
3072 case Instruction::Mul:
3073 return ReductionInstDesc(Kind == RK_IntegerMult, I);
3074 case Instruction::And:
3075 return ReductionInstDesc(Kind == RK_IntegerAnd, I);
3076 case Instruction::Or:
3077 return ReductionInstDesc(Kind == RK_IntegerOr, I);
3078 case Instruction::Xor:
3079 return ReductionInstDesc(Kind == RK_IntegerXor, I);
3080 case Instruction::FMul:
3081 return ReductionInstDesc(Kind == RK_FloatMult && FastMath, I);
3082 case Instruction::FAdd:
3083 return ReductionInstDesc(Kind == RK_FloatAdd && FastMath, I);
3084 case Instruction::FCmp:
3085 case Instruction::ICmp:
3086 case Instruction::Select:
3087 if (Kind != RK_IntegerMinMax &&
3088 (!HasFunNoNaNAttr || Kind != RK_FloatMinMax))
3089 return ReductionInstDesc(false, I);
3090 return isMinMaxSelectCmpPattern(I, Prev);
3094 LoopVectorizationLegality::InductionKind
3095 LoopVectorizationLegality::isInductionVariable(PHINode *Phi) {
3096 Type *PhiTy = Phi->getType();
3097 // We only handle integer and pointer inductions variables.
3098 if (!PhiTy->isIntegerTy() && !PhiTy->isPointerTy())
3099 return IK_NoInduction;
3101 // Check that the PHI is consecutive.
3102 const SCEV *PhiScev = SE->getSCEV(Phi);
3103 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev);
3105 DEBUG(dbgs() << "LV: PHI is not a poly recurrence.\n");
3106 return IK_NoInduction;
3108 const SCEV *Step = AR->getStepRecurrence(*SE);
3110 // Integer inductions need to have a stride of one.
3111 if (PhiTy->isIntegerTy()) {
3113 return IK_IntInduction;
3114 if (Step->isAllOnesValue())
3115 return IK_ReverseIntInduction;
3116 return IK_NoInduction;
3119 // Calculate the pointer stride and check if it is consecutive.
3120 const SCEVConstant *C = dyn_cast<SCEVConstant>(Step);
3122 return IK_NoInduction;
3124 assert(PhiTy->isPointerTy() && "The PHI must be a pointer");
3125 uint64_t Size = DL->getTypeAllocSize(PhiTy->getPointerElementType());
3126 if (C->getValue()->equalsInt(Size))
3127 return IK_PtrInduction;
3128 else if (C->getValue()->equalsInt(0 - Size))
3129 return IK_ReversePtrInduction;
3131 return IK_NoInduction;
3134 bool LoopVectorizationLegality::isInductionVariable(const Value *V) {
3135 Value *In0 = const_cast<Value*>(V);
3136 PHINode *PN = dyn_cast_or_null<PHINode>(In0);
3140 return Inductions.count(PN);
3143 bool LoopVectorizationLegality::blockNeedsPredication(BasicBlock *BB) {
3144 assert(TheLoop->contains(BB) && "Unknown block used");
3146 // Blocks that do not dominate the latch need predication.
3147 BasicBlock* Latch = TheLoop->getLoopLatch();
3148 return !DT->dominates(BB, Latch);
3151 bool LoopVectorizationLegality::blockCanBePredicated(BasicBlock *BB) {
3152 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
3153 // We don't predicate loads/stores at the moment.
3154 if (it->mayReadFromMemory() || it->mayWriteToMemory() || it->mayThrow())
3157 // The instructions below can trap.
3158 switch (it->getOpcode()) {
3160 case Instruction::UDiv:
3161 case Instruction::SDiv:
3162 case Instruction::URem:
3163 case Instruction::SRem:
3171 bool LoopVectorizationLegality::hasComputableBounds(Value *Ptr) {
3172 const SCEV *PhiScev = SE->getSCEV(Ptr);
3173 const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev);
3177 return AR->isAffine();
3180 LoopVectorizationCostModel::VectorizationFactor
3181 LoopVectorizationCostModel::selectVectorizationFactor(bool OptForSize,
3183 // Width 1 means no vectorize
3184 VectorizationFactor Factor = { 1U, 0U };
3185 if (OptForSize && Legal->getRuntimePointerCheck()->Need) {
3186 DEBUG(dbgs() << "LV: Aborting. Runtime ptr check is required in Os.\n");
3190 // Find the trip count.
3191 unsigned TC = SE->getSmallConstantTripCount(TheLoop, TheLoop->getLoopLatch());
3192 DEBUG(dbgs() << "LV: Found trip count:"<<TC<<"\n");
3194 unsigned WidestType = getWidestType();
3195 unsigned WidestRegister = TTI.getRegisterBitWidth(true);
3196 unsigned MaxVectorSize = WidestRegister / WidestType;
3197 DEBUG(dbgs() << "LV: The Widest type: " << WidestType << " bits.\n");
3198 DEBUG(dbgs() << "LV: The Widest register is:" << WidestRegister << "bits.\n");
3200 if (MaxVectorSize == 0) {
3201 DEBUG(dbgs() << "LV: The target has no vector registers.\n");
3205 assert(MaxVectorSize <= 32 && "Did not expect to pack so many elements"
3206 " into one vector!");
3208 unsigned VF = MaxVectorSize;
3210 // If we optimize the program for size, avoid creating the tail loop.
3212 // If we are unable to calculate the trip count then don't try to vectorize.
3214 DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n");
3218 // Find the maximum SIMD width that can fit within the trip count.
3219 VF = TC % MaxVectorSize;
3224 // If the trip count that we found modulo the vectorization factor is not
3225 // zero then we require a tail.
3227 DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n");
3233 assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two");
3234 DEBUG(dbgs() << "LV: Using user VF "<<UserVF<<".\n");
3236 Factor.Width = UserVF;
3240 float Cost = expectedCost(1);
3242 DEBUG(dbgs() << "LV: Scalar loop costs: "<< (int)Cost << ".\n");
3243 for (unsigned i=2; i <= VF; i*=2) {
3244 // Notice that the vector loop needs to be executed less times, so
3245 // we need to divide the cost of the vector loops by the width of
3246 // the vector elements.
3247 float VectorCost = expectedCost(i) / (float)i;
3248 DEBUG(dbgs() << "LV: Vector loop of width "<< i << " costs: " <<
3249 (int)VectorCost << ".\n");
3250 if (VectorCost < Cost) {
3256 DEBUG(dbgs() << "LV: Selecting VF = : "<< Width << ".\n");
3257 Factor.Width = Width;
3258 Factor.Cost = Width * Cost;
3262 unsigned LoopVectorizationCostModel::getWidestType() {
3263 unsigned MaxWidth = 8;
3266 for (Loop::block_iterator bb = TheLoop->block_begin(),
3267 be = TheLoop->block_end(); bb != be; ++bb) {
3268 BasicBlock *BB = *bb;
3270 // For each instruction in the loop.
3271 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
3272 Type *T = it->getType();
3274 // Only examine Loads, Stores and PHINodes.
3275 if (!isa<LoadInst>(it) && !isa<StoreInst>(it) && !isa<PHINode>(it))
3278 // Examine PHI nodes that are reduction variables.
3279 if (PHINode *PN = dyn_cast<PHINode>(it))
3280 if (!Legal->getReductionVars()->count(PN))
3283 // Examine the stored values.
3284 if (StoreInst *ST = dyn_cast<StoreInst>(it))
3285 T = ST->getValueOperand()->getType();
3287 // Ignore loaded pointer types and stored pointer types that are not
3288 // consecutive. However, we do want to take consecutive stores/loads of
3289 // pointer vectors into account.
3290 if (T->isPointerTy() && !isConsecutiveLoadOrStore(it))
3293 MaxWidth = std::max(MaxWidth,
3294 (unsigned)DL->getTypeSizeInBits(T->getScalarType()));
3302 LoopVectorizationCostModel::selectUnrollFactor(bool OptForSize,
3305 unsigned LoopCost) {
3307 // -- The unroll heuristics --
3308 // We unroll the loop in order to expose ILP and reduce the loop overhead.
3309 // There are many micro-architectural considerations that we can't predict
3310 // at this level. For example frontend pressure (on decode or fetch) due to
3311 // code size, or the number and capabilities of the execution ports.
3313 // We use the following heuristics to select the unroll factor:
3314 // 1. If the code has reductions the we unroll in order to break the cross
3315 // iteration dependency.
3316 // 2. If the loop is really small then we unroll in order to reduce the loop
3318 // 3. We don't unroll if we think that we will spill registers to memory due
3319 // to the increased register pressure.
3321 // Use the user preference, unless 'auto' is selected.
3325 // When we optimize for size we don't unroll.
3329 // Do not unroll loops with a relatively small trip count.
3330 unsigned TC = SE->getSmallConstantTripCount(TheLoop,
3331 TheLoop->getLoopLatch());
3332 if (TC > 1 && TC < TinyTripCountUnrollThreshold)
3335 unsigned TargetVectorRegisters = TTI.getNumberOfRegisters(true);
3336 DEBUG(dbgs() << "LV: The target has " << TargetVectorRegisters <<
3337 " vector registers\n");
3339 LoopVectorizationCostModel::RegisterUsage R = calculateRegisterUsage();
3340 // We divide by these constants so assume that we have at least one
3341 // instruction that uses at least one register.
3342 R.MaxLocalUsers = std::max(R.MaxLocalUsers, 1U);
3343 R.NumInstructions = std::max(R.NumInstructions, 1U);
3345 // We calculate the unroll factor using the following formula.
3346 // Subtract the number of loop invariants from the number of available
3347 // registers. These registers are used by all of the unrolled instances.
3348 // Next, divide the remaining registers by the number of registers that is
3349 // required by the loop, in order to estimate how many parallel instances
3350 // fit without causing spills.
3351 unsigned UF = (TargetVectorRegisters - R.LoopInvariantRegs) / R.MaxLocalUsers;
3353 // Clamp the unroll factor ranges to reasonable factors.
3354 unsigned MaxUnrollSize = TTI.getMaximumUnrollFactor();
3356 // If we did not calculate the cost for VF (because the user selected the VF)
3357 // then we calculate the cost of VF here.
3359 LoopCost = expectedCost(VF);
3361 // Clamp the calculated UF to be between the 1 and the max unroll factor
3362 // that the target allows.
3363 if (UF > MaxUnrollSize)
3368 if (Legal->getReductionVars()->size()) {
3369 DEBUG(dbgs() << "LV: Unrolling because of reductions. \n");
3373 // We want to unroll tiny loops in order to reduce the loop overhead.
3374 // We assume that the cost overhead is 1 and we use the cost model
3375 // to estimate the cost of the loop and unroll until the cost of the
3376 // loop overhead is about 5% of the cost of the loop.
3377 DEBUG(dbgs() << "LV: Loop cost is "<< LoopCost <<" \n");
3378 if (LoopCost < 20) {
3379 DEBUG(dbgs() << "LV: Unrolling to reduce branch cost. \n");
3380 unsigned NewUF = 20/LoopCost + 1;
3381 return std::min(NewUF, UF);
3384 DEBUG(dbgs() << "LV: Not Unrolling. \n");
3388 LoopVectorizationCostModel::RegisterUsage
3389 LoopVectorizationCostModel::calculateRegisterUsage() {
3390 // This function calculates the register usage by measuring the highest number
3391 // of values that are alive at a single location. Obviously, this is a very
3392 // rough estimation. We scan the loop in a topological order in order and
3393 // assign a number to each instruction. We use RPO to ensure that defs are
3394 // met before their users. We assume that each instruction that has in-loop
3395 // users starts an interval. We record every time that an in-loop value is
3396 // used, so we have a list of the first and last occurrences of each
3397 // instruction. Next, we transpose this data structure into a multi map that
3398 // holds the list of intervals that *end* at a specific location. This multi
3399 // map allows us to perform a linear search. We scan the instructions linearly
3400 // and record each time that a new interval starts, by placing it in a set.
3401 // If we find this value in the multi-map then we remove it from the set.
3402 // The max register usage is the maximum size of the set.
3403 // We also search for instructions that are defined outside the loop, but are
3404 // used inside the loop. We need this number separately from the max-interval
3405 // usage number because when we unroll, loop-invariant values do not take
3407 LoopBlocksDFS DFS(TheLoop);
3411 R.NumInstructions = 0;
3413 // Each 'key' in the map opens a new interval. The values
3414 // of the map are the index of the 'last seen' usage of the
3415 // instruction that is the key.
3416 typedef DenseMap<Instruction*, unsigned> IntervalMap;
3417 // Maps instruction to its index.
3418 DenseMap<unsigned, Instruction*> IdxToInstr;
3419 // Marks the end of each interval.
3420 IntervalMap EndPoint;
3421 // Saves the list of instruction indices that are used in the loop.
3422 SmallSet<Instruction*, 8> Ends;
3423 // Saves the list of values that are used in the loop but are
3424 // defined outside the loop, such as arguments and constants.
3425 SmallPtrSet<Value*, 8> LoopInvariants;
3428 for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(),
3429 be = DFS.endRPO(); bb != be; ++bb) {
3430 R.NumInstructions += (*bb)->size();
3431 for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
3433 Instruction *I = it;
3434 IdxToInstr[Index++] = I;
3436 // Save the end location of each USE.
3437 for (unsigned i = 0; i < I->getNumOperands(); ++i) {
3438 Value *U = I->getOperand(i);
3439 Instruction *Instr = dyn_cast<Instruction>(U);
3441 // Ignore non-instruction values such as arguments, constants, etc.
3442 if (!Instr) continue;
3444 // If this instruction is outside the loop then record it and continue.
3445 if (!TheLoop->contains(Instr)) {
3446 LoopInvariants.insert(Instr);
3450 // Overwrite previous end points.
3451 EndPoint[Instr] = Index;
3457 // Saves the list of intervals that end with the index in 'key'.
3458 typedef SmallVector<Instruction*, 2> InstrList;
3459 DenseMap<unsigned, InstrList> TransposeEnds;
3461 // Transpose the EndPoints to a list of values that end at each index.
3462 for (IntervalMap::iterator it = EndPoint.begin(), e = EndPoint.end();
3464 TransposeEnds[it->second].push_back(it->first);
3466 SmallSet<Instruction*, 8> OpenIntervals;
3467 unsigned MaxUsage = 0;
3470 DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
3471 for (unsigned int i = 0; i < Index; ++i) {
3472 Instruction *I = IdxToInstr[i];
3473 // Ignore instructions that are never used within the loop.
3474 if (!Ends.count(I)) continue;
3476 // Remove all of the instructions that end at this location.
3477 InstrList &List = TransposeEnds[i];
3478 for (unsigned int j=0, e = List.size(); j < e; ++j)
3479 OpenIntervals.erase(List[j]);
3481 // Count the number of live interals.
3482 MaxUsage = std::max(MaxUsage, OpenIntervals.size());
3484 DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " <<
3485 OpenIntervals.size() <<"\n");
3487 // Add the current instruction to the list of open intervals.
3488 OpenIntervals.insert(I);
3491 unsigned Invariant = LoopInvariants.size();
3492 DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsage << " \n");
3493 DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << " \n");
3494 DEBUG(dbgs() << "LV(REG): LoopSize: " << R.NumInstructions << " \n");
3496 R.LoopInvariantRegs = Invariant;
3497 R.MaxLocalUsers = MaxUsage;
3501 unsigned LoopVectorizationCostModel::expectedCost(unsigned VF) {
3505 for (Loop::block_iterator bb = TheLoop->block_begin(),
3506 be = TheLoop->block_end(); bb != be; ++bb) {
3507 unsigned BlockCost = 0;
3508 BasicBlock *BB = *bb;
3510 // For each instruction in the old loop.
3511 for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
3512 // Skip dbg intrinsics.
3513 if (isa<DbgInfoIntrinsic>(it))
3516 unsigned C = getInstructionCost(it, VF);
3518 DEBUG(dbgs() << "LV: Found an estimated cost of "<< C <<" for VF " <<
3519 VF << " For instruction: "<< *it << "\n");
3522 // We assume that if-converted blocks have a 50% chance of being executed.
3523 // When the code is scalar then some of the blocks are avoided due to CF.
3524 // When the code is vectorized we execute all code paths.
3525 if (Legal->blockNeedsPredication(*bb) && VF == 1)
3535 LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
3536 // If we know that this instruction will remain uniform, check the cost of
3537 // the scalar version.
3538 if (Legal->isUniformAfterVectorization(I))
3541 Type *RetTy = I->getType();
3542 Type *VectorTy = ToVectorTy(RetTy, VF);
3544 // TODO: We need to estimate the cost of intrinsic calls.
3545 switch (I->getOpcode()) {
3546 case Instruction::GetElementPtr:
3547 // We mark this instruction as zero-cost because the cost of GEPs in
3548 // vectorized code depends on whether the corresponding memory instruction
3549 // is scalarized or not. Therefore, we handle GEPs with the memory
3550 // instruction cost.
3552 case Instruction::Br: {
3553 return TTI.getCFInstrCost(I->getOpcode());
3555 case Instruction::PHI:
3556 //TODO: IF-converted IFs become selects.
3558 case Instruction::Add:
3559 case Instruction::FAdd:
3560 case Instruction::Sub:
3561 case Instruction::FSub:
3562 case Instruction::Mul:
3563 case Instruction::FMul:
3564 case Instruction::UDiv:
3565 case Instruction::SDiv:
3566 case Instruction::FDiv:
3567 case Instruction::URem:
3568 case Instruction::SRem:
3569 case Instruction::FRem:
3570 case Instruction::Shl:
3571 case Instruction::LShr:
3572 case Instruction::AShr:
3573 case Instruction::And:
3574 case Instruction::Or:
3575 case Instruction::Xor: {
3576 // Certain instructions can be cheaper to vectorize if they have a constant
3577 // second vector operand. One example of this are shifts on x86.
3578 TargetTransformInfo::OperandValueKind Op1VK =
3579 TargetTransformInfo::OK_AnyValue;
3580 TargetTransformInfo::OperandValueKind Op2VK =
3581 TargetTransformInfo::OK_AnyValue;
3583 if (isa<ConstantInt>(I->getOperand(1)))
3584 Op2VK = TargetTransformInfo::OK_UniformConstantValue;
3586 return TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy, Op1VK, Op2VK);
3588 case Instruction::Select: {
3589 SelectInst *SI = cast<SelectInst>(I);
3590 const SCEV *CondSCEV = SE->getSCEV(SI->getCondition());
3591 bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop));
3592 Type *CondTy = SI->getCondition()->getType();
3594 CondTy = VectorType::get(CondTy, VF);
3596 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy);
3598 case Instruction::ICmp:
3599 case Instruction::FCmp: {
3600 Type *ValTy = I->getOperand(0)->getType();
3601 VectorTy = ToVectorTy(ValTy, VF);
3602 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy);
3604 case Instruction::Store:
3605 case Instruction::Load: {
3606 StoreInst *SI = dyn_cast<StoreInst>(I);
3607 LoadInst *LI = dyn_cast<LoadInst>(I);
3608 Type *ValTy = (SI ? SI->getValueOperand()->getType() :
3610 VectorTy = ToVectorTy(ValTy, VF);
3612 unsigned Alignment = SI ? SI->getAlignment() : LI->getAlignment();
3613 unsigned AS = SI ? SI->getPointerAddressSpace() :
3614 LI->getPointerAddressSpace();
3615 Value *Ptr = SI ? SI->getPointerOperand() : LI->getPointerOperand();
3616 // We add the cost of address computation here instead of with the gep
3617 // instruction because only here we know whether the operation is
3620 return TTI.getAddressComputationCost(VectorTy) +
3621 TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
3623 // Scalarized loads/stores.
3624 int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
3625 bool Reverse = ConsecutiveStride < 0;
3626 unsigned ScalarAllocatedSize = DL->getTypeAllocSize(ValTy);
3627 unsigned VectorElementSize = DL->getTypeStoreSize(VectorTy)/VF;
3628 if (!ConsecutiveStride || ScalarAllocatedSize != VectorElementSize) {
3630 // The cost of extracting from the value vector and pointer vector.
3631 Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
3632 for (unsigned i = 0; i < VF; ++i) {
3633 // The cost of extracting the pointer operand.
3634 Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, PtrTy, i);
3635 // In case of STORE, the cost of ExtractElement from the vector.
3636 // In case of LOAD, the cost of InsertElement into the returned
3638 Cost += TTI.getVectorInstrCost(SI ? Instruction::ExtractElement :
3639 Instruction::InsertElement,
3643 // The cost of the scalar loads/stores.
3644 Cost += VF * TTI.getAddressComputationCost(ValTy->getScalarType());
3645 Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(),
3650 // Wide load/stores.
3651 unsigned Cost = TTI.getAddressComputationCost(VectorTy);
3652 Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
3655 Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse,
3659 case Instruction::ZExt:
3660 case Instruction::SExt:
3661 case Instruction::FPToUI:
3662 case Instruction::FPToSI:
3663 case Instruction::FPExt:
3664 case Instruction::PtrToInt:
3665 case Instruction::IntToPtr:
3666 case Instruction::SIToFP:
3667 case Instruction::UIToFP:
3668 case Instruction::Trunc:
3669 case Instruction::FPTrunc:
3670 case Instruction::BitCast: {
3671 // We optimize the truncation of induction variable.
3672 // The cost of these is the same as the scalar operation.
3673 if (I->getOpcode() == Instruction::Trunc &&
3674 Legal->isInductionVariable(I->getOperand(0)))
3675 return TTI.getCastInstrCost(I->getOpcode(), I->getType(),
3676 I->getOperand(0)->getType());
3678 Type *SrcVecTy = ToVectorTy(I->getOperand(0)->getType(), VF);
3679 return TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy);
3681 case Instruction::Call: {
3682 CallInst *CI = cast<CallInst>(I);
3683 Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI);
3684 assert(ID && "Not an intrinsic call!");
3685 Type *RetTy = ToVectorTy(CI->getType(), VF);
3686 SmallVector<Type*, 4> Tys;
3687 for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i)
3688 Tys.push_back(ToVectorTy(CI->getArgOperand(i)->getType(), VF));
3689 return TTI.getIntrinsicInstrCost(ID, RetTy, Tys);
3692 // We are scalarizing the instruction. Return the cost of the scalar
3693 // instruction, plus the cost of insert and extract into vector
3694 // elements, times the vector width.
3697 if (!RetTy->isVoidTy() && VF != 1) {
3698 unsigned InsCost = TTI.getVectorInstrCost(Instruction::InsertElement,
3700 unsigned ExtCost = TTI.getVectorInstrCost(Instruction::ExtractElement,
3703 // The cost of inserting the results plus extracting each one of the
3705 Cost += VF * (InsCost + ExtCost * I->getNumOperands());
3708 // The cost of executing VF copies of the scalar instruction. This opcode
3709 // is unknown. Assume that it is the same as 'mul'.
3710 Cost += VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy);
3716 Type* LoopVectorizationCostModel::ToVectorTy(Type *Scalar, unsigned VF) {
3717 if (Scalar->isVoidTy() || VF == 1)
3719 return VectorType::get(Scalar, VF);
3722 char LoopVectorize::ID = 0;
3723 static const char lv_name[] = "Loop Vectorization";
3724 INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false)
3725 INITIALIZE_AG_DEPENDENCY(AliasAnalysis)
3726 INITIALIZE_AG_DEPENDENCY(TargetTransformInfo)
3727 INITIALIZE_PASS_DEPENDENCY(ScalarEvolution)
3728 INITIALIZE_PASS_DEPENDENCY(LoopSimplify)
3729 INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
3732 Pass *createLoopVectorizePass() {
3733 return new LoopVectorize();
3737 bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) {
3738 // Check for a store.
3739 if (StoreInst *ST = dyn_cast<StoreInst>(Inst))
3740 return Legal->isConsecutivePtr(ST->getPointerOperand()) != 0;
3742 // Check for a load.
3743 if (LoadInst *LI = dyn_cast<LoadInst>(Inst))
3744 return Legal->isConsecutivePtr(LI->getPointerOperand()) != 0;