llvm-project/polly
Michael Kruse beffdb9daa [ScopDetect] Reject loop with multiple exit blocks.
The current statement domain derivation algorithm does not (always)
consider that different exit blocks of a loop can have different
conditions to be reached.

From the code

      for (int i = n; ; i-=2) {
        if (i <= 0) goto even;
        if (i <= 1) goto odd;
        A[i] = i;
      }
    even:
      A[0] = 42;
      return;
    odd:
      A[1] = 21;
      return;

Polly currently derives the following domains:

        Stmt_even_critedge
            Domain :=
                [n] -> { Stmt_even_critedge[] };
        Stmt_odd
            Domain :=
                [n] -> { Stmt_odd[] : (1 + n) mod 2 = 0 and n > 0 };

while the domain for the odd case is correct, Stmt_even is assumed to be
executed unconditionally, which is obviously wrong. While projecting out
the loop dimension in `adjustDomainDimensions`, it does not consider
that there are other exit condition that have matched before.

I don't know a how to fix this without changing a lot of code. Therefore
This patch rejects loops with multiple exist blocks to fix the
miscompile of test-suite's uuencode.

The odd condition is transformed by LLVM to

    %cmp1 = icmp eq i64 %indvars.iv, 1

such that the project_out in adjustDomainDimensions() indeed only
matches for odd n (using this condition only, we'd have an infinite loop
otherwise).

The even condition manifests as

    %cmp = icmp slt i64 %indvars.iv, 3

Because buildDomainsWithBranchConstraints() does not consider other exit
conditions, it has to assume that the induction variable will eventually
be lower than 3 and taking this exit.

IMHO we need to reuse the algorithm that determines the number of
iterations (addLoopBoundsToHeaderDomain) to determine which exit
condition applies first. It has to happen in
buildDomainsWithBranchConstraints() because the result will need to
propagate to successor BBs. Currently addLoopBoundsToHeaderDomain() just
look for union of all backedge conditions (which means leaving not the
loop here). The patch in llvm.org/PR35465 changes it to look for exit
conditions instead. This is required because there might be other exit
conditions that do not alternatively go back to the loop header.

Differential Revision: https://reviews.llvm.org/D45649

llvm-svn: 330858
2018-04-25 18:53:33 +00:00
..
cmake [CMake] Use only keyword-version of target_link_library. NFC. 2018-01-12 16:09:18 +00:00
docs [doc] Overhaul doc on preparing IR for processing by Polly. 2018-04-06 19:24:18 +00:00
include/polly [ScopDetect] Reject loop with multiple exit blocks. 2018-04-25 18:53:33 +00:00
lib [ScopDetect] Reject loop with multiple exit blocks. 2018-04-25 18:53:33 +00:00
test [ScopDetect] Reject loop with multiple exit blocks. 2018-04-25 18:53:33 +00:00
tools [GPUJIT] Improved temporary file handling. 2017-09-19 10:41:29 +00:00
unittests Add isl operator overloads for isl::pw_aff (Try II) 2018-04-12 06:15:17 +00:00
utils Revise polly-{update|check}-format targets 2015-09-14 16:59:50 +00:00
www [Polly] Information about generalized matrix multiplication 2017-09-24 19:00:25 +00:00
.arcconfig [polly] Set up .arcconfig to point to new Diffusion PLO repository 2017-11-27 17:34:03 +00:00
.arclint [External] Move lib/JSON to lib/External/JSON. NFC. 2017-02-05 15:26:56 +00:00
.gitattributes
.gitignore Do not track the isl PDF manual in SVN 2017-01-16 11:48:03 +00:00
CMakeLists.txt [CMake] Use only keyword-version of target_link_library. NFC. 2018-01-12 16:09:18 +00:00
CREDITS.txt
LICENSE.txt [External] Move lib/JSON to lib/External/JSON. NFC. 2017-02-05 15:26:56 +00:00
README Test commit 2017-06-28 12:58:44 +00:00

README

Polly - Polyhedral optimizations for LLVM
-----------------------------------------
http://polly.llvm.org/

Polly uses a mathematical representation, the polyhedral model, to represent and
transform loops and other control flow structures. Using an abstract
representation it is possible to reason about transformations in a more general
way and to use highly optimized linear programming libraries to figure out the
optimal loop structure. These transformations can be used to do constant
propagation through arrays, remove dead loop iterations, optimize loops for
cache locality, optimize arrays, apply advanced automatic parallelization, drive
vectorization, or they can be used to do software pipelining.