Michael Kruse 22c77f2354
[Polly] Use separate DT/LI/SE for outlined subfn. NFC. (#102460)
DominatorTree, LoopInfo, and ScalarEvolution are function-level analyses
that expect to be called only on instructions and basic blocks of the
function they were original created for. When Polly outlined a parallel
loop body into a separate function, it reused the same analyses seemed
to work until new checks to be added in #101198.

This patch creates new analyses for the subfunctions. GenDT, GenLI, and
GenSE now refer to the analyses of the current region of code. Outside
of an outlined function, they refer to the same analysis as used for the
SCoP, but are substituted within an outlined function.

Additionally to the cross-function queries of DT/LI/SE, we must not
create SCEVs that refer to a mix of expressions for old and generated
values. Currently, SCEVs themselves do not "remember" which
ScalarEvolution analysis they were created for, but mixing them is just
as unexpected as using DT/LI across function boundaries. Hence
`SCEVLoopAddRecRewriter` was combined into `ScopExpander`.
`SCEVLoopAddRecRewriter` only replaced induction variables but left
SCEVUnknowns to reference the old function. `SCEVParameterRewriter`
would have done so but its job was effectively superseded by
`ScopExpander`, and now also `SCEVLoopAddRecRewriter`. Some issues
persist put marked with a FIXME in the code. Changing them would
possibly cause this patch to be not NFC anymore.
2024-08-10 14:25:15 +02:00
..

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.