llvm-project/polly/test/ScopInfo/assume_gep_bounds_2.ll

Ignoring revisions in .git-blame-ignore-revs. Click here to bypass and see the normal blame view.

96 lines
3.5 KiB
LLVM
Raw Normal View History

; RUN: opt %loadPolly -basicaa -polly-scops -analyze < %s \
; RUN: -polly-precise-inbounds | FileCheck %s
Assume GetElementPtr offsets to be inbounds In case a GEP instruction references into a fixed size array e.g., an access A[i][j] into an array A[100x100], LLVM-IR does not guarantee that the subscripts always compute values that are within array bounds. We now derive the set of parameter values for which all accesses are within bounds and add the assumption that the scop is only every executed with this set of parameter values. Example: void foo(float A[][20], long n, long m { for (long i = 0; i < n; i++) for (long j = 0; j < m; j++) A[i][j] = ... This loop yields out-of-bound accesses if m is at least 20 and at the same time at least one iteration of the outer loop is executed. Hence, we assume: n <= 0 or m <= 20. Doing so simplifies the dependence analysis problem, allows us to perform more optimizations and generate better code. TODO: The location where the GEP instruction is executed is not necessarily the location where the memory is actually accessed. As a result scanning for GEP[s] is imprecise. Even though this is not a correctness problem, this imprecision may result in missed optimizations or non-optimal run-time checks. In polybench where this mismatch between parametric loop bounds and fixed size arrays is common, we see with this patch significant reductions in compile time (up to 50%) and execution time (up to 70%). We see two significant compile time regressions (fdtd-2d, jacobi-2d-imper), and one execution time regression (trmm). Both regressions arise due to additional optimizations that have been enabled by this patch. They can be addressed in subsequent commits. http://reviews.llvm.org/D6369 llvm-svn: 222754
2014-11-25 18:51:12 +08:00
;
; void foo(float A[restrict][20], float B[restrict][20], long n, long m,
; long p) {
; for (long i = 0; i < n; i++)
; for (long j = 0; j < m; j++)
; A[i][j] = i + j;
; for (long i = 0; i < m; i++)
; for (long j = 0; j < p; j++)
; B[i][j] = i + j;
; }
; This code is within bounds either if m and p are smaller than the array sizes,
; but also if only p is smaller than the size of the second B dimension and n
; is such that the first loop is never executed and consequently A is never
; accessed. In this case the value of m does not matter.
; CHECK: Assumed Context:
2016-01-15 23:54:45 +08:00
; CHECK-NEXT: [n, m, p] -> { : p <= 20 and (n <= 0 or (n > 0 and m <= 20)) }
Assume GetElementPtr offsets to be inbounds In case a GEP instruction references into a fixed size array e.g., an access A[i][j] into an array A[100x100], LLVM-IR does not guarantee that the subscripts always compute values that are within array bounds. We now derive the set of parameter values for which all accesses are within bounds and add the assumption that the scop is only every executed with this set of parameter values. Example: void foo(float A[][20], long n, long m { for (long i = 0; i < n; i++) for (long j = 0; j < m; j++) A[i][j] = ... This loop yields out-of-bound accesses if m is at least 20 and at the same time at least one iteration of the outer loop is executed. Hence, we assume: n <= 0 or m <= 20. Doing so simplifies the dependence analysis problem, allows us to perform more optimizations and generate better code. TODO: The location where the GEP instruction is executed is not necessarily the location where the memory is actually accessed. As a result scanning for GEP[s] is imprecise. Even though this is not a correctness problem, this imprecision may result in missed optimizations or non-optimal run-time checks. In polybench where this mismatch between parametric loop bounds and fixed size arrays is common, we see with this patch significant reductions in compile time (up to 50%) and execution time (up to 70%). We see two significant compile time regressions (fdtd-2d, jacobi-2d-imper), and one execution time regression (trmm). Both regressions arise due to additional optimizations that have been enabled by this patch. They can be addressed in subsequent commits. http://reviews.llvm.org/D6369 llvm-svn: 222754
2014-11-25 18:51:12 +08:00
target datalayout = "e-m:e-i64:64-f80:128-n8:16:32:64-S128"
define void @foo([20 x float]* noalias %A, [20 x float]* noalias %B, i64 %n, i64 %m, i64 %p) {
entry:
br label %for.cond
for.cond: ; preds = %for.inc5, %entry
%i.0 = phi i64 [ 0, %entry ], [ %inc6, %for.inc5 ]
%cmp = icmp slt i64 %i.0, %n
br i1 %cmp, label %for.body, label %for.end7
for.body: ; preds = %for.cond
br label %for.cond1
for.cond1: ; preds = %for.inc, %for.body
%j.0 = phi i64 [ 0, %for.body ], [ %inc, %for.inc ]
%cmp2 = icmp slt i64 %j.0, %m
br i1 %cmp2, label %for.body3, label %for.end
for.body3: ; preds = %for.cond1
%add = add nsw i64 %i.0, %j.0
%conv = sitofp i64 %add to float
%arrayidx4 = getelementptr inbounds [20 x float], [20 x float]* %A, i64 %i.0, i64 %j.0
Assume GetElementPtr offsets to be inbounds In case a GEP instruction references into a fixed size array e.g., an access A[i][j] into an array A[100x100], LLVM-IR does not guarantee that the subscripts always compute values that are within array bounds. We now derive the set of parameter values for which all accesses are within bounds and add the assumption that the scop is only every executed with this set of parameter values. Example: void foo(float A[][20], long n, long m { for (long i = 0; i < n; i++) for (long j = 0; j < m; j++) A[i][j] = ... This loop yields out-of-bound accesses if m is at least 20 and at the same time at least one iteration of the outer loop is executed. Hence, we assume: n <= 0 or m <= 20. Doing so simplifies the dependence analysis problem, allows us to perform more optimizations and generate better code. TODO: The location where the GEP instruction is executed is not necessarily the location where the memory is actually accessed. As a result scanning for GEP[s] is imprecise. Even though this is not a correctness problem, this imprecision may result in missed optimizations or non-optimal run-time checks. In polybench where this mismatch between parametric loop bounds and fixed size arrays is common, we see with this patch significant reductions in compile time (up to 50%) and execution time (up to 70%). We see two significant compile time regressions (fdtd-2d, jacobi-2d-imper), and one execution time regression (trmm). Both regressions arise due to additional optimizations that have been enabled by this patch. They can be addressed in subsequent commits. http://reviews.llvm.org/D6369 llvm-svn: 222754
2014-11-25 18:51:12 +08:00
store float %conv, float* %arrayidx4, align 4
br label %for.inc
for.inc: ; preds = %for.body3
%inc = add nsw i64 %j.0, 1
br label %for.cond1
for.end: ; preds = %for.cond1
br label %for.inc5
for.inc5: ; preds = %for.end
%inc6 = add nsw i64 %i.0, 1
br label %for.cond
for.end7: ; preds = %for.cond
br label %for.cond9
for.cond9: ; preds = %for.inc25, %for.end7
%i8.0 = phi i64 [ 0, %for.end7 ], [ %inc26, %for.inc25 ]
%cmp10 = icmp slt i64 %i8.0, %m
br i1 %cmp10, label %for.body12, label %for.end27
for.body12: ; preds = %for.cond9
br label %for.cond14
for.cond14: ; preds = %for.inc22, %for.body12
%j13.0 = phi i64 [ 0, %for.body12 ], [ %inc23, %for.inc22 ]
%cmp15 = icmp slt i64 %j13.0, %p
br i1 %cmp15, label %for.body17, label %for.end24
for.body17: ; preds = %for.cond14
%add18 = add nsw i64 %i8.0, %j13.0
%conv19 = sitofp i64 %add18 to float
%arrayidx21 = getelementptr inbounds [20 x float], [20 x float]* %B, i64 %i8.0, i64 %j13.0
Assume GetElementPtr offsets to be inbounds In case a GEP instruction references into a fixed size array e.g., an access A[i][j] into an array A[100x100], LLVM-IR does not guarantee that the subscripts always compute values that are within array bounds. We now derive the set of parameter values for which all accesses are within bounds and add the assumption that the scop is only every executed with this set of parameter values. Example: void foo(float A[][20], long n, long m { for (long i = 0; i < n; i++) for (long j = 0; j < m; j++) A[i][j] = ... This loop yields out-of-bound accesses if m is at least 20 and at the same time at least one iteration of the outer loop is executed. Hence, we assume: n <= 0 or m <= 20. Doing so simplifies the dependence analysis problem, allows us to perform more optimizations and generate better code. TODO: The location where the GEP instruction is executed is not necessarily the location where the memory is actually accessed. As a result scanning for GEP[s] is imprecise. Even though this is not a correctness problem, this imprecision may result in missed optimizations or non-optimal run-time checks. In polybench where this mismatch between parametric loop bounds and fixed size arrays is common, we see with this patch significant reductions in compile time (up to 50%) and execution time (up to 70%). We see two significant compile time regressions (fdtd-2d, jacobi-2d-imper), and one execution time regression (trmm). Both regressions arise due to additional optimizations that have been enabled by this patch. They can be addressed in subsequent commits. http://reviews.llvm.org/D6369 llvm-svn: 222754
2014-11-25 18:51:12 +08:00
store float %conv19, float* %arrayidx21, align 4
br label %for.inc22
for.inc22: ; preds = %for.body17
%inc23 = add nsw i64 %j13.0, 1
br label %for.cond14
for.end24: ; preds = %for.cond14
br label %for.inc25
for.inc25: ; preds = %for.end24
%inc26 = add nsw i64 %i8.0, 1
br label %for.cond9
for.end27: ; preds = %for.cond9
ret void
}