Index: llvm/lib/Transforms/Vectorize/LoopVectorize.cpp =================================================================== --- llvm/lib/Transforms/Vectorize/LoopVectorize.cpp +++ llvm/lib/Transforms/Vectorize/LoopVectorize.cpp @@ -5393,17 +5393,25 @@ unsigned MaxTripCount = PSE.getSE()->getSmallConstantMaxTripCount(TheLoop); - if (!A.Width.isScalable() && !B.Width.isScalable() && foldTailByMasking() && - MaxTripCount) { - // If we are folding the tail and the trip count is a known (possibly small) - // constant, the trip count will be rounded up to an integer number of - // iterations. The total cost will be PerIterationCost*ceil(TripCount/VF), - // which we compare directly. When not folding the tail, the total cost will - // be PerIterationCost*floor(TC/VF) + Scalar remainder cost, and so is - // approximated with the per-lane cost below instead of using the tripcount - // as here. - auto RTCostA = CostA * divideCeil(MaxTripCount, A.Width.getFixedValue()); - auto RTCostB = CostB * divideCeil(MaxTripCount, B.Width.getFixedValue()); + if (!A.Width.isScalable() && !B.Width.isScalable() && MaxTripCount) { + // If the trip count is a known (possibly small) constant, the trip count + // will be rounded up to an integer number of iterations under + // FoldTailByMasking. The total cost in that case will be + // VecCost*ceil(TripCount/VF). When not folding the tail, the total + // cost will be VecCost*floor(TC/VF) + ScalarCost*(TC%VF). There will be + // some extra overheads, but for the purpose of comparing the costs of + // different VFs we can use this to compare the total loop-body cost + // expected after vectorization. + auto GetCostForTC = [MaxTripCount, this](unsigned VF, + InstructionCost VectorCost, + InstructionCost ScalarCost) { + return foldTailByMasking() ? VectorCost * divideCeil(MaxTripCount, VF) + : VectorCost * (MaxTripCount / VF) + + ScalarCost * (MaxTripCount % VF); + }; + auto RTCostA = GetCostForTC(A.Width.getFixedValue(), CostA, A.ScalarCost); + auto RTCostB = GetCostForTC(B.Width.getFixedValue(), CostB, B.ScalarCost); + return RTCostA < RTCostB; } Index: llvm/test/Transforms/LoopVectorize/AArch64/smallest-and-widest-types.ll =================================================================== --- llvm/test/Transforms/LoopVectorize/AArch64/smallest-and-widest-types.ll +++ llvm/test/Transforms/LoopVectorize/AArch64/smallest-and-widest-types.ll @@ -95,7 +95,7 @@ %conv = sitofp i8 %i.08 to float %add = fadd float %s.09, %conv %inc = add nuw nsw i8 %i.08, 1 - %exitcond.not = icmp eq i8 %inc, 12345 + %exitcond.not = icmp eq i8 %inc, 241 br i1 %exitcond.not, label %for.end, label %for.body for.end: Index: llvm/test/Transforms/LoopVectorize/X86/vect.omp.force.small-tc.ll =================================================================== --- llvm/test/Transforms/LoopVectorize/X86/vect.omp.force.small-tc.ll +++ llvm/test/Transforms/LoopVectorize/X86/vect.omp.force.small-tc.ll @@ -14,7 +14,9 @@ ; ; -; This loop will be vectorized, although the trip count is below the threshold, but vectorization is explicitly forced in metadata. +; This loop will be vectorized, although the trip count is below the threshold, but +; vectorization is explicitly forced in metadata. The trip count of 4 is chosen as +; it more nicely divides the loop count of 20, produce a lower total cost. ; define void @vectorized(ptr noalias nocapture %A, ptr noalias nocapture readonly %B) { ; CHECK-LABEL: @vectorized( @@ -27,20 +29,20 @@ ; CHECK-NEXT: [[TMP0:%.*]] = add i64 [[INDEX]], 0 ; CHECK-NEXT: [[TMP1:%.*]] = getelementptr inbounds float, ptr [[B:%.*]], i64 [[TMP0]] ; CHECK-NEXT: [[TMP2:%.*]] = getelementptr inbounds float, ptr [[TMP1]], i32 0 -; CHECK-NEXT: [[WIDE_LOAD:%.*]] = load <8 x float>, ptr [[TMP2]], align 4, !llvm.access.group [[ACC_GRP0:![0-9]+]] +; CHECK-NEXT: [[WIDE_LOAD:%.*]] = load <4 x float>, ptr [[TMP2]], align 4, !llvm.access.group [[ACC_GRP0:![0-9]+]] ; CHECK-NEXT: [[TMP3:%.*]] = getelementptr inbounds float, ptr [[A:%.*]], i64 [[TMP0]] ; CHECK-NEXT: [[TMP4:%.*]] = getelementptr inbounds float, ptr [[TMP3]], i32 0 -; CHECK-NEXT: [[WIDE_LOAD1:%.*]] = load <8 x float>, ptr [[TMP4]], align 4, !llvm.access.group [[ACC_GRP0]] -; CHECK-NEXT: [[TMP5:%.*]] = fadd fast <8 x float> [[WIDE_LOAD]], [[WIDE_LOAD1]] -; CHECK-NEXT: store <8 x float> [[TMP5]], ptr [[TMP4]], align 4, !llvm.access.group [[ACC_GRP0]] -; CHECK-NEXT: [[INDEX_NEXT]] = add nuw i64 [[INDEX]], 8 -; CHECK-NEXT: [[TMP6:%.*]] = icmp eq i64 [[INDEX_NEXT]], 16 +; CHECK-NEXT: [[WIDE_LOAD1:%.*]] = load <4 x float>, ptr [[TMP4]], align 4, !llvm.access.group [[ACC_GRP0]] +; CHECK-NEXT: [[TMP5:%.*]] = fadd fast <4 x float> [[WIDE_LOAD]], [[WIDE_LOAD1]] +; CHECK-NEXT: store <4 x float> [[TMP5]], ptr [[TMP4]], align 4, !llvm.access.group [[ACC_GRP0]] +; CHECK-NEXT: [[INDEX_NEXT]] = add nuw i64 [[INDEX]], 4 +; CHECK-NEXT: [[TMP6:%.*]] = icmp eq i64 [[INDEX_NEXT]], 20 ; CHECK-NEXT: br i1 [[TMP6]], label [[MIDDLE_BLOCK:%.*]], label [[VECTOR_BODY]], !llvm.loop [[LOOP1:![0-9]+]] ; CHECK: middle.block: -; CHECK-NEXT: [[CMP_N:%.*]] = icmp eq i64 20, 16 +; CHECK-NEXT: [[CMP_N:%.*]] = icmp eq i64 20, 20 ; CHECK-NEXT: br i1 [[CMP_N]], label [[FOR_END:%.*]], label [[SCALAR_PH]] ; CHECK: scalar.ph: -; CHECK-NEXT: [[BC_RESUME_VAL:%.*]] = phi i64 [ 16, [[MIDDLE_BLOCK]] ], [ 0, [[ENTRY:%.*]] ] +; CHECK-NEXT: [[BC_RESUME_VAL:%.*]] = phi i64 [ 20, [[MIDDLE_BLOCK]] ], [ 0, [[ENTRY:%.*]] ] ; CHECK-NEXT: br label [[FOR_BODY:%.*]] ; CHECK: for.body: ; CHECK-NEXT: [[INDVARS_IV:%.*]] = phi i64 [ [[BC_RESUME_VAL]], [[SCALAR_PH]] ], [ [[INDVARS_IV_NEXT:%.*]], [[FOR_BODY]] ] @@ -52,7 +54,7 @@ ; CHECK-NEXT: store float [[ADD]], ptr [[ARRAYIDX2]], align 4, !llvm.access.group [[ACC_GRP0]] ; CHECK-NEXT: [[INDVARS_IV_NEXT]] = add nuw nsw i64 [[INDVARS_IV]], 1 ; CHECK-NEXT: [[EXITCOND:%.*]] = icmp eq i64 [[INDVARS_IV_NEXT]], 20 -; CHECK-NEXT: br i1 [[EXITCOND]], label [[FOR_END]], label [[FOR_BODY]], !llvm.loop [[LOOP4:![0-9]+]] +; CHECK-NEXT: br i1 [[EXITCOND]], label [[FOR_END]], label [[FOR_BODY]], !llvm.loop [[LOOP5:![0-9]+]] ; CHECK: for.end: ; CHECK-NEXT: ret void ;