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llvm-project/polly/test/ScopInfo/multidim_2d_outer_parametri...

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ScopDetection: Only detect scops that have at least one read and one write Scops that only read seem generally uninteresting and scops that only write are most likely initializations where there is also little to optimize. To not waste compile time we bail early. Differential Revision: http://reviews.llvm.org/D7735 llvm-svn: 229820
2015-02-19 13:31:07 +08:00
; RUN: opt %loadPolly -polly-detect-unprofitable -polly-scops -analyze -polly-delinearize < %s | FileCheck %s
Derive run-time conditions for delinearization As our delinearization works optimistically, we need in some cases run-time checks that verify our optimistic assumptions. A simple example is the following code: void foo(long n, long m, long o, double A[n][m][o]) { for (long i = 0; i < 100; i++) for (long j = 0; j < 150; j++) for (long k = 0; k < 200; k++) A[i][j][k] = 1.0; } After clang linearized the access to A and we delinearized it again to A[i][j][k] we need to ensure that we do not access the delinearized array out of bounds (this information is not available in LLVM-IR). Hence, we need to verify the following constraints at run-time: CHECK: Assumed Context: CHECK: [o, m] -> { : m >= 150 and o >= 200 } llvm-svn: 212198
2014-07-03 01:47:48 +08:00
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"
; Derived from the following code:
;
; void foo(long n, long m, long p, double A[n][m]) {
; for (long i = 0; i < 100; i++)
; for (long j = 0; j < m; j++)
; A[i+p][j] = 1.0;
; }
; CHECK: Assumed Context:
Do allow negative offsets in the outermost array dimension There is no needed for neither 1-dimensional nor higher dimensional arrays to require positive offsets in the outermost array dimension. We originally introduced this assumption with the support for delinearizing multi-dimensional arrays. llvm-svn: 214665
2014-08-04 05:07:30 +08:00
; CHECK: [m, p] -> { : }
Derive run-time conditions for delinearization As our delinearization works optimistically, we need in some cases run-time checks that verify our optimistic assumptions. A simple example is the following code: void foo(long n, long m, long o, double A[n][m][o]) { for (long i = 0; i < 100; i++) for (long j = 0; j < 150; j++) for (long k = 0; k < 200; k++) A[i][j][k] = 1.0; } After clang linearized the access to A and we delinearized it again to A[i][j][k] we need to ensure that we do not access the delinearized array out of bounds (this information is not available in LLVM-IR). Hence, we need to verify the following constraints at run-time: CHECK: Assumed Context: CHECK: [o, m] -> { : m >= 150 and o >= 200 } llvm-svn: 212198
2014-07-03 01:47:48 +08:00
; CHECK: p0: %m
; CHECK: p1: %p
; CHECK: Statements {
; CHECK: Stmt_for_j
; CHECK: Domain :=
Traverse the SCoP to compute non-loop-carried domain conditions In order to compute domain conditions for conditionals we will now traverse the region in the ScopInfo once and build the domains for each block in the region. The SCoP statements can then use these constraints when they build their domain. The reason behind this change is twofold: 1) This removes a big chunk of preprocessing logic from the TempScopInfo, namely the Conditionals we used to build there. Additionally to moving this logic it is also simplified. Instead of walking the dominance tree up for each basic block in the region (as we did before), we now traverse the region only once in order to collect the domain conditions. 2) This is the first step towards the isl based domain creation. The second step will traverse the region similar to this step, however it will propagate back edge conditions. Once both are in place this conditional handling will allow multiple exit loops additional logic. Reviewers: grosser Differential Revision: http://reviews.llvm.org/D12428 llvm-svn: 246398
2015-08-31 05:13:53 +08:00
; CHECK: [m, p] -> { Stmt_for_j[i0, i1] :
; CHECK-DAG: i0 >= 0
; CHECK-DAG: and
; CHECK-DAG: i0 <= 99
; CHECK-DAG: and
; CHECK-DAG: i1 >= 0
; CHECK-DAG: and
; CHECK-DAG: i1 <= -1 + m
; CHECK: }
Rename 'scattering' to 'schedule' In Polly we used both the term 'scattering' and the term 'schedule' to describe the execution order of a statement without actually distinguishing between them. We now uniformly use the term 'schedule' for the execution order. This corresponds to the terminology of isl. History: CLooG introduced the term scattering as the generated code can be used as a sequential execution order (schedule) or as a parallel dimension enumerating different threads of execution (placement). In Polly and/or isl the term placement was never used, but we uniformly refer to an execution order as a schedule and only later introduce parallelism. When doing so we do not talk about about specific placement dimensions. llvm-svn: 235380
2015-04-21 19:37:25 +08:00
; CHECK: Schedule :=
[NFC] Drop the "scattering" tuple name llvm-svn: 227801
2015-02-02 21:45:54 +08:00
; CHECK: [m, p] -> { Stmt_for_j[i0, i1] -> [i0, i1] };
Derive run-time conditions for delinearization As our delinearization works optimistically, we need in some cases run-time checks that verify our optimistic assumptions. A simple example is the following code: void foo(long n, long m, long o, double A[n][m][o]) { for (long i = 0; i < 100; i++) for (long j = 0; j < 150; j++) for (long k = 0; k < 200; k++) A[i][j][k] = 1.0; } After clang linearized the access to A and we delinearized it again to A[i][j][k] we need to ensure that we do not access the delinearized array out of bounds (this information is not available in LLVM-IR). Hence, we need to verify the following constraints at run-time: CHECK: Assumed Context: CHECK: [o, m] -> { : m >= 150 and o >= 200 } llvm-svn: 212198
2014-07-03 01:47:48 +08:00
; CHECK: MustWriteAccess := [Reduction Type: NONE]
; CHECK: [m, p] -> { Stmt_for_j[i0, i1] -> MemRef_A[p + i0, i1] };
; CHECK: }
define void @foo(i64 %n, i64 %m, i64 %p, double* %A) {
entry:
br label %for.i
for.i:
%i = phi i64 [ 0, %entry ], [ %i.inc, %for.i.inc ]
%add = add nsw i64 %i, %p
%tmp = mul nsw i64 %add, %m
br label %for.j
for.j:
%j = phi i64 [ 0, %for.i ], [ %j.inc, %for.j ]
%vlaarrayidx.sum = add i64 %j, %tmp
Update Polly tests to handle explicitly typed gep changes in LLVM llvm-svn: 230784
2015-02-28 03:20:19 +08:00
%arrayidx = getelementptr inbounds double, double* %A, i64 %vlaarrayidx.sum
Derive run-time conditions for delinearization As our delinearization works optimistically, we need in some cases run-time checks that verify our optimistic assumptions. A simple example is the following code: void foo(long n, long m, long o, double A[n][m][o]) { for (long i = 0; i < 100; i++) for (long j = 0; j < 150; j++) for (long k = 0; k < 200; k++) A[i][j][k] = 1.0; } After clang linearized the access to A and we delinearized it again to A[i][j][k] we need to ensure that we do not access the delinearized array out of bounds (this information is not available in LLVM-IR). Hence, we need to verify the following constraints at run-time: CHECK: Assumed Context: CHECK: [o, m] -> { : m >= 150 and o >= 200 } llvm-svn: 212198
2014-07-03 01:47:48 +08:00
store double 1.0, double* %arrayidx
%j.inc = add nsw i64 %j, 1
%j.exitcond = icmp eq i64 %j.inc, %m
br i1 %j.exitcond, label %for.i.inc, label %for.j
for.i.inc:
%i.inc = add nsw i64 %i, 1
%i.exitcond = icmp eq i64 %i.inc, 100
br i1 %i.exitcond, label %end, label %for.i
end:
ret void
}