introduce no-return or unreachable heuristics.
The return heuristics from the Ball and Larus paper don't work well in
practice as they pessimize early return paths. The only good hitrate
return heuristics are those for:
- NULL return
- Constant return
- negative integer return
Only the last of these three can possibly require significant code for
the returning block, and even the last is fairly rare and usually also
a constant. As a consequence, even for the cold return paths, there is
little code on that return path, and so little code density to be gained
by sinking it. The places where sinking these blocks is valuable (inner
loops) will already be weighted appropriately as the edge is a loop-exit
branch.
All of this aside, early returns are nearly as common as all three of
these return categories, and should actually be predicted as taken!
Rather than muddy the waters of the static predictions, just remain
silent on returns and let the CFG itself dictate any layout or other
issues.
However, the return heuristic was flagging one very important case:
unreachable. Unfortunately it still gave a 1/4 chance of the
branch-to-unreachable occuring. It also didn't do a rigorous job of
finding those blocks which post-dominate an unreachable block.
This patch builds a more powerful analysis that should flag all branches
to blocks known to then reach unreachable. It also has better worst-case
runtime complexity by not looping through successors for each block. The
previous code would perform an N^2 walk in the event of a single entry
block branching to N successors with a switch where each successor falls
through to the next and they finally fall through to a return.
Test case added for noreturn heuristics. Also doxygen comments improved
along the way.
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two more subtle routines to the bottom and expand on their cautionary
comments a bit. No functionality or actual interface change here.
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a single class. Previously it was split between two classes, one
internal and one external. The concern seemed to center around exposing
the weights used, but those can remain confined to the implementation
file.
Having a single class to maintain the state and analyses in use will
also simplify several of the enhancements I want to make to our static
heuristics.
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to bring it under direct test instead of merely indirectly testing it in
the BlockFrequencyInfo pass.
The next step is to start adding tests for the various heuristics
employed, and to start fixing those heuristics once they're under test.
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layer already had support for printing the results of this analysis, but
the wiring was missing.
Now that printing the analysis works, actually bring some of this
analysis, and the BranchProbabilityInfo analysis that it wraps, under
test! I'm planning on fixing some bugs and doing other work here, so
having a nice place to add regression tests and a way to observe the
results is really useful.
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Just because we're dealing with a GEP doesn't mean we can assert the
SCEV has a pointer type. The fix is simply to ignore the SCEV pointer
type, which we really didn't need.
Fixes PR11138 webkit crash.
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file. Since it should only be used when necessary propagate it through
the backend code generation and tweak testcases accordingly.
This helps with code like in clang's test/CodeGen/debug-info-line.c where
we have multiple #line directives within a single lexical block and want
to generate only a single block that contains each file change.
Part of rdar://10246360
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IVs.
Indvars previously chose randomly between congruent IVs. Now it will
bias the decision toward IVs that SCEVExpander likes to create. This
was not done to fix any problem, it's just a welcome side effect of
factoring code.
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We want heuristics to be based on accurate data, but more importantly
we don't want llvm to behave randomly. A benign trunc inserted by an
upstream pass should not cause a wild swings in optimization
level. See PR11034. It's a general problem with threshold-based
heuristics, but we can make it less bad.
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MDNodes graph structure such that compiler unit keeps track of important MDNodes and update dwarf writer to process mdnodes top-down instead of bottom up.
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SCEV unrolling can unroll loops with arbitrary induction variables. It
is a prerequisite for -disable-iv-rewrite performance. It is also
easily handles loops of arbitrary structure including multiple exits
and is generally more robust.
This is under a temporary option to avoid affecting default
behavior for the next couple of weeks. It is needed so that I can
checkin unit tests for updateUnloop.
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An algorithm for incrementally updating LoopInfo within a
LoopPassManager. The incremental update should be extremely cheap in
most cases and can be used in places where it's not feasible to
regenerate the entire loop forest.
- "Unloop" is a node in the loop tree whose last backedge has been removed.
- Perform reverse dataflow on the block inside Unloop to propagate the
nearest loop from the block's successors.
- For reducible CFG, each block in unloop is visited exactly
once. This is because unloop no longer has a backedge and blocks
within subloops don't change parents.
- Immediate subloops are summarized by the nearest loop reachable from
their exits or exits within nested subloops.
- At completion the unloop blocks each have a new parent loop, and
each immediate subloop has a new parent.
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based on ScalarEvolution without changing the induction variable phis.
This utility is the main tool of IndVarSimplifyPass, but the pass also
restructures induction variables in strange ways that are sensitive to
pass ordering. This provides a way for other loop passes to simplify
new uses of induction variables created during transformation. The
utility may be used by any pass that preserves ScalarEvolution. Soon
LoopUnroll will use it.
The net effect in this checkin is to cleanup the IndVarSimplify pass
by factoring out the SimplifyIndVar algorithm into a standalone utility.
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These are not individual bug fixes. I had to rewrite a good chunk of
the unroller to make it sane. I think it was getting lucky on trivial
completely unrolled loops with no early exits. I included some fairly
simple unit tests for partial unrolling. I didn't do much stress
testing, so it may not be perfect, but should be usable now.
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inlined variable, based on the discussion in PR10542.
This explodes the runtime of several passes down the pipeline due to
a large number of "copies" remaining live across a large function. This
only shows up with both debug and opt, but when it does it creates
a many-minute compile when self-hosting LLVM+Clang. There are several
other cases that show these types of regressions.
All of this is tracked in PR10542, and progress is being made on fixing
the issue. Once its addressed, the re-instated, but until then this
restores the performance for self-hosting and other opt+debug builds.
Devang, let me know if this causes any trouble, or impedes fixing it in
any way, and thanks for working on this!
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LoopPassManager. The incremental update should be extremely cheap in
most cases and can be used in places where it's not feasible to
regenerate the entire loop forest.
- "Unloop" is a node in the loop tree whose last backedge has been removed.
- Perform reverse dataflow on the block inside Unloop to propagate the
nearest loop from the block's successors.
- For reducible CFG, each block in unloop is visited exactly
once. This is because unloop no longer has a backedge and blocks
within subloops don't change parents.
- Immediate subloops are summarized by the nearest loop reachable from
their exits or exits within nested subloops.
- At completion the unloop blocks each have a new parent loop, and
each immediate subloop has a new parent.
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