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https://github.com/c64scene-ar/llvm-6502.git
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6e046d3810
Review: http://reviews.llvm.org/D8096 git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@232528 91177308-0d34-0410-b5e6-96231b3b80d8
86 lines
2.8 KiB
LLVM
86 lines
2.8 KiB
LLVM
; RUN: opt < %s -loop-vectorize -mtriple=x86_64-apple-macosx10.8.0 -mcpu=corei7-avx -debug-only=loop-vectorize -stats -S 2>&1 | FileCheck %s
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; REQUIRES: asserts
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; CHECK: LV: Loop hints: force=enabled
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; CHECK: LV: Loop hints: force=?
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; No more loops in the module
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; CHECK-NOT: LV: Loop hints: force=
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; CHECK: 2 loop-vectorize - Number of loops analyzed for vectorization
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; CHECK: 1 loop-vectorize - Number of loops vectorized
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target datalayout = "e-p:64:64:64-i1:8:8-i8:8:8-i16:16:16-i32:32:32-i64:64:64-f32:32:32-f64:64:64-v64:64:64-v128:128:128-a0:0:64-s0:64:64-f80:128:128-n8:16:32:64-S128"
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target triple = "x86_64-apple-macosx10.8.0"
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;
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; The source code for the test:
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;
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; #include <math.h>
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; void foo(float* restrict A, float * restrict B)
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; {
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; for (int i = 0; i < 1000; i+=2) A[i] = sinf(B[i]);
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; }
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;
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;
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; This loop will be vectorized, although the scalar cost is lower than any of vector costs, but vectorization is explicitly forced in metadata.
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;
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define void @vectorized(float* noalias nocapture %A, float* noalias nocapture %B) {
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entry:
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br label %for.body
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for.body:
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%indvars.iv = phi i64 [ %indvars.iv.next, %for.body ], [ 0, %entry ]
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%arrayidx = getelementptr inbounds float, float* %B, i64 %indvars.iv
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%0 = load float, float* %arrayidx, align 4, !llvm.mem.parallel_loop_access !1
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%call = tail call float @llvm.sin.f32(float %0)
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%arrayidx2 = getelementptr inbounds float, float* %A, i64 %indvars.iv
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store float %call, float* %arrayidx2, align 4, !llvm.mem.parallel_loop_access !1
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%indvars.iv.next = add nuw nsw i64 %indvars.iv, 2
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%lftr.wideiv = trunc i64 %indvars.iv.next to i32
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%exitcond = icmp eq i32 %lftr.wideiv, 1000
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br i1 %exitcond, label %for.end.loopexit, label %for.body, !llvm.loop !1
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for.end.loopexit:
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br label %for.end
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for.end:
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ret void
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}
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!1 = !{!1, !2}
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!2 = !{!"llvm.loop.vectorize.enable", i1 true}
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;
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; This method will not be vectorized, as scalar cost is lower than any of vector costs.
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;
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define void @not_vectorized(float* noalias nocapture %A, float* noalias nocapture %B) {
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entry:
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br label %for.body
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for.body:
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%indvars.iv = phi i64 [ %indvars.iv.next, %for.body ], [ 0, %entry ]
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%arrayidx = getelementptr inbounds float, float* %B, i64 %indvars.iv
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%0 = load float, float* %arrayidx, align 4, !llvm.mem.parallel_loop_access !3
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%call = tail call float @llvm.sin.f32(float %0)
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%arrayidx2 = getelementptr inbounds float, float* %A, i64 %indvars.iv
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store float %call, float* %arrayidx2, align 4, !llvm.mem.parallel_loop_access !3
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%indvars.iv.next = add nuw nsw i64 %indvars.iv, 2
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%lftr.wideiv = trunc i64 %indvars.iv.next to i32
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%exitcond = icmp eq i32 %lftr.wideiv, 1000
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br i1 %exitcond, label %for.end.loopexit, label %for.body, !llvm.loop !3
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for.end.loopexit:
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br label %for.end
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for.end:
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ret void
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}
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declare float @llvm.sin.f32(float) nounwind readnone
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; Dummy metadata
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!3 = !{!3}
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