Currently we only check if the pointers involved in runtime checks do
not wrap if we need to perform dependency checks. If that's not the
case, we generate runtime checks, even if the pointers may wrap (see
test/Analysis/LoopAccessAnalysis/runtime-checks-may-wrap.ll).
If the pointer wraps, then we swap start and end of the runtime check,
leading to incorrect checks.
An Alive2 proof of what the runtime checks are checking conceptually (on
i4 to have it complete in reasonable time) showing the incorrect result
should be https://alive2.llvm.org/ce/z/KsHzn8
Depends on https://github.com/llvm/llvm-project/pull/127410 to avoid
more regressions.
PR: https://github.com/llvm/llvm-project/pull/127543
Follow-up as discussed when using VPInstruction::ResumePhi for all resume
values (#112147). This patch explicitly adds incoming values for each
predecessor in VPlan. This simplifies codegen and allows transformations
adjusting the predecessors of blocks with
NFC modulo incoming block order in phis.
This was originally done to reduce the diff for the change. Remove it
and update the remaining tests. NFC modulo reordering of incoming
values.
Clean up after https://github.com/llvm/llvm-project/pull/114292.
Most AArch64 cpus outside of Neoverse V1 (256) and A64FX (512) have an
SVE vector length of 128, and in environments like Android (where no
mcpu option is common) we would expect all cpus to match. This patch
changes the default vector length to 128 with -mcpu=generic, to match
the most common case.
If IVUpdateMayOverflow is false, we proved that the induction increment
cannot overflow in the vector loop. This allows setting NUW in some
cases when folding the tail.
PR: https://github.com/llvm/llvm-project/pull/111758
Don't consider the cost of branches marked to be skipped in VPlan cost
pre-computation. Those aren't included in the legacy cost, so they
should not be included in the VPlan cast.
The conditional branch from the loop latch will be replaced by a
single branch controlling the loop, so there is no extra overhead from
scalarization. This improves the cost esimates in some cases.
