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RegionInfo's RegionNode. This mirrors the logic for automating the
extraction from a Loop.
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of the CodeExtractor utility. This allows speculatively computing input
and output sets to measure the likely size impact of the code
extraction.
These sets cannot be reused sadly -- we mutate the function prior to
forming the final sets used by the actual extraction.
The interface has been revamped slightly to make it easier to use
correctly by making the interface const and sinking the computation of
the number of exit blocks into the full extraction function and away
from the rest of this logic which just computed two output parameters.
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and expose it as a utility class rather than as free function wrappers.
The simple free-function interface works well for the bugpoint-specific
pass's uses of code extraction, but in an upcoming patch for more
advanced code extraction, they simply don't expose a rich enough
interface. I need to expose various stages of the process of doing the
code extraction and query information to decide whether or not to
actually complete the extraction or give up.
Rather than build up a new predicate model and pass that into these
functions, just take the class that was actually implementing the
functions and lift it up into a proper interface that can be used to
perform code extraction. The interface is cleaned up and re-documented
to work better in a header. It also is now setup to accept the blocks to
be extracted in the constructor rather than in a method.
In passing this essentially reverts my previous commit here exposing
a block-level query for eligibility of extraction. That is no longer
necessary with the more rich interface as clients can query the
extraction object for eligibility directly. This will reduce the number
of walks of the input basic block sequence by quite a bit which is
useful if this enters the normal optimization pipeline.
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extraction into a public interface. Also clean it up and apply it more
consistently such that we check for landing pads *anywhere* in the
extracted code, not just in single-block extraction.
This will be used to guide decisions in passes that are planning to
eventually perform a round of code extraction.
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Allow the "SplitCriticalEdge" function to split the edge to a landing pad. If
the pass is *sure* that it thinks it knows what it's doing, then it may go ahead
and specify that the landing pad can have its critical edge split. The loop
unswitch pass is one of these passes. It will split the critical edges of all
edges coming from a loop to a landing pad not within the loop. Doing so will
retain important loop analysis information, such as loop simplify.
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of the BBVectorizePass without using command line option. As pointed out
by Hal, we can ask the TargetLoweringInfo for the architecture specific
VectorizeConfig to perform vectorizing with architecture specific
information.
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BasicBlock in other passes, e.g. we can call vectorizeBasicBlock in the
loop unroll pass right after the loop is unrolled.
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interfaces. These methods were used in the old inline cost system where
there was a persistent cache that had to be updated, invalidated, and
cleared. We're now doing more direct computations that don't require
this intricate dance. Even if we resume some level of caching, it would
almost certainly have a simpler and more narrow interface than this.
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on a per-callsite walk of the called function's instructions, in
breadth-first order over the potentially reachable set of basic blocks.
This is a major shift in how inline cost analysis works to improve the
accuracy and rationality of inlining decisions. A brief outline of the
algorithm this moves to:
- Build a simplification mapping based on the callsite arguments to the
function arguments.
- Push the entry block onto a worklist of potentially-live basic blocks.
- Pop the first block off of the *front* of the worklist (for
breadth-first ordering) and walk its instructions using a custom
InstVisitor.
- For each instruction's operands, re-map them based on the
simplification mappings available for the given callsite.
- Compute any simplification possible of the instruction after
re-mapping, and store that back int othe simplification mapping.
- Compute any bonuses, costs, or other impacts of the instruction on the
cost metric.
- When the terminator is reached, replace any conditional value in the
terminator with any simplifications from the mapping we have, and add
any successors which are not proven to be dead from these
simplifications to the worklist.
- Pop the next block off of the front of the worklist, and repeat.
- As soon as the cost of inlining exceeds the threshold for the
callsite, stop analyzing the function in order to bound cost.
The primary goal of this algorithm is to perfectly handle dead code
paths. We do not want any code in trivially dead code paths to impact
inlining decisions. The previous metric was *extremely* flawed here, and
would always subtract the average cost of two successors of
a conditional branch when it was proven to become an unconditional
branch at the callsite. There was no handling of wildly different costs
between the two successors, which would cause inlining when the path
actually taken was too large, and no inlining when the path actually
taken was trivially simple. There was also no handling of the code
*path*, only the immediate successors. These problems vanish completely
now. See the added regression tests for the shiny new features -- we
skip recursive function calls, SROA-killing instructions, and high cost
complex CFG structures when dead at the callsite being analyzed.
Switching to this algorithm required refactoring the inline cost
interface to accept the actual threshold rather than simply returning
a single cost. The resulting interface is pretty bad, and I'm planning
to do lots of interface cleanup after this patch.
Several other refactorings fell out of this, but I've tried to minimize
them for this patch. =/ There is still more cleanup that can be done
here. Please point out anything that you see in review.
I've worked really hard to try to mirror at least the spirit of all of
the previous heuristics in the new model. It's not clear that they are
all correct any more, but I wanted to minimize the change in this single
patch, it's already a bit ridiculous. One heuristic that is *not* yet
mirrored is to allow inlining of functions with a dynamic alloca *if*
the caller has a dynamic alloca. I will add this back, but I think the
most reasonable way requires changes to the inliner itself rather than
just the cost metric, and so I've deferred this for a subsequent patch.
The test case is XFAIL-ed until then.
As mentioned in the review mail, this seems to make Clang run about 1%
to 2% faster in -O0, but makes its binary size grow by just under 4%.
I've looked into the 4% growth, and it can be fixed, but requires
changes to other parts of the inliner.
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blocks in the function cloner. This removes the last case of trivially
dead code that I've been seeing in the wild getting inlined, analyzed,
re-inlined, optimized, only to be deleted. Nukes a FIXME from the
cleanup tests.
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directly query the function information which this set was representing.
This simplifies the interface of the inline cost analysis, and makes the
always-inline pass significantly more efficient.
Previously, always-inline would first make a single set of every
function in the module *except* those marked with the always-inline
attribute. It would then query this set at every call site to see if the
function was a member of the set, and if so, refuse to inline it. This
is quite wasteful. Instead, simply check the function attribute directly
when looking at the callsite.
The normal inliner also had similar redundancy. It added every function
in the module with the noinline attribute to its set to ignore, even
though inside the cost analysis function we *already tested* the
noinline attribute and produced the same result.
The only tricky part of removing this is that we have to be able to
correctly remove only the functions inlined by the always-inline pass
when finalizing, which requires a bit of a hack. Still, much less of
a hack than the set of all non-always-inline functions was. While I was
touching this function, I switched a heavy-weight set to a vector with
sort+unique. The algorithm already had a two-phase insert and removal
pattern, we were just needlessly paying the uniquing cost on every
insert.
This probably speeds up some compiles by a small amount (-O0 compiles
with lots of always-inline, so potentially heavy libc++ users), but I've
not tried to measure it.
I believe there is no functional change here, but yell if you spot one.
None are intended.
Finally, the direction this is going in is to greatly simplify the
inline cost query interface so that we can replace its implementation
with a much more clever one. Along the way, all the APIs get simplified,
so it seems incrementally good.
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changed since. No one was using it. It is yet another consumer of the
InlineCost interface that I'd like to change.
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are optimization hints, but at -O0 we're not optimizing. This becomes a problem
when the alwaysinline attribute is abused.
rdar://10921594
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Clang patch (flags) will follow shortly.
The run-time library will also follow, but not immediately.
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were no 'unwind' instructions being generated before this, so this is in effect
a no-op.
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PHI nodes which were matched, rather than climbing up the
original PHI node's operands to rediscover PHI nodes for
recording, since the PHI nodes found that are not
necessarily part of the matched set.
This fixes rdar://10589171.
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This is the initial checkin of the basic-block autovectorization pass along with some supporting vectorization infrastructure.
Special thanks to everyone who helped review this code over the last several months (especially Tobias Grosser).
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autorelease push+pop pairs.
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EP_ModuleOptimizerEarly, to allow passes to be added before the
main ModulePass optimizers.
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support
to CloneFunctionInto to allow remapping for this case.
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http://llvm.org/docs/CodingStandards.html#ll_virtual_anch
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This will be used by SimplifyCfg in a later commit.
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false positive. compiler part.
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Patch by Brendon Cahoon!
This extends the existing LoopUnroll and LoopUnrollPass. Brendon
measured no regressions in the llvm test suite with -unroll-runtime
enabled. This implementation works by using the existing loop
unrolling code to unroll the loop by a power-of-two (default 8). It
generates an if-then-else sequence of code prior to the loop to
execute the extra iterations before entering the unrolled loop.
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Add a custom name for fwrite and fputs on x86-32 OSX. Make SimplifyLibCalls honor the custom
names for fwrite and fputs.
Fixes <rdar://problem/9815881>.
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Patch by Kostya Serebryany.
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There is no reason to have simple IR level pass in lib/Target.
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This handles the case in which LSR rewrites an IV user that is a phi and
splits critical edges originating from a switch.
Fixes <rdar://problem/6453893> LSR is not splitting edges "nicely"
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ssa, so it has to be run really early in the pipeline. Any replacement
should probably use the SSAUpdater.
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SplitLandingPadPredecessors is similar to SplitBlockPredecessors in that it
splits the current block and attaches a set of predecessors to the new basic
block. However, it differs from SplitBlockPredecessors in that it's specifically
designed to handle landing pad blocks.
Two new basic blocks are created: one that is has the vector of predecessors as
its predecessors and one that has the remaining predecessors as its
predecessors. Those two new blocks then receive a cloned copy of the landingpad
instruction from the original block. The landingpad instructions are joined in a
PHI, etc. Like SplitBlockPredecessors, it updates the LLVM IR, AliasAnalysis,
DominatorTree, DominanceFrontier, LoopInfo, and LCCSA analyses.
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ends can use without needing to be aware of the plugin (or the plugin be aware of the front end).
Before 3.0, I'd like to add a mechanism for automatically loading a set of plugins from a config file. API suggestions welcome...
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SimplifyIndVar utility since it is required.
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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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