operand_values. The first provides a range view over operand Use
objects, and the second provides a range view over the Value*s being
used by those operands.
The naming is "STL-style" rather than "LLVM-style" because we have
historically named iterator methods STL-style, and range methods seem to
have far more in common with their iterator counterparts than with
"normal" APIs. Feel free to bikeshed on this one if you want, I'm happy
to change these around if people feel strongly.
I've switched code in SROA and LCG to exercise these mostly to ensure
they work correctly -- we don't really have an easy way to unittest this
and they're trivial.
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proposed std::iterator_pair which was in committee suggested to move
toward std::iterator_range. There isn't a formal paper yet, but there
seems little disagreement within the committee at this point so it seems
fine to provide our own version in the llvm namespace so we can easily
build range adaptors for the numerous iterators in LLVM's interfaces.
Note that I'm not really comfortable advocating a crazed range-based
migration just yet. The range stuff is still in a great deal of flux in
C++ and the committee hasn't entirely made up its mind (afaict) about
how it will work. So I'm mostly trying to provide the minimal
functionality needed to make writing easy and convenient range adaptors
for range based for loops easy and convenient. ;]
Subsequent patches will use this across the fundamental IR types, where
there are iterator views.
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The interaction between defaulted operators and move elision isn't
totally obvious, add a unit test so it doesn't break unintentionally.
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to the build being C++11.
There is clearly still plenty of simplification than can be done here by
using standard type traits instead of rolling our own in many places.
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on the fact that we now build in C++11 mode with modern compilers. This
should flush out any issues. If the build bots are happy with this, I'll
GC all the code for coping without R-value references.
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The previous PBQP solver was very robust but consumed a lot of memory,
performed a lot of redundant computation, and contained some unnecessarily tight
coupling that prevented experimentation with novel solution techniques. This new
solver is an attempt to address these shortcomings.
Important/interesting changes:
1) The domain-independent PBQP solver class, HeuristicSolverImpl, is gone.
It is replaced by a register allocation specific solver, PBQP::RegAlloc::Solver
(see RegAllocSolver.h).
The optimal reduction rules and the backpropagation algorithm have been extracted
into stand-alone functions (see ReductionRules.h), which can be used to build
domain specific PBQP solvers. This provides many more opportunities for
domain-specific knowledge to inform the PBQP solvers' decisions. In theory this
should allow us to generate better solutions. In practice, we can at least test
out ideas now.
As a side benefit, I believe the new solver is more readable than the old one.
2) The solver type is now a template parameter of the PBQP graph.
This allows the graph to notify the solver of any modifications made (e.g. by
domain independent rules) without the overhead of a virtual call. It also allows
the solver to supply policy information to the graph (see below).
3) Significantly reduced memory overhead.
Memory management policy is now an explicit property of the PBQP graph (via
the CostAllocator typedef on the graph's solver template argument). Because PBQP
graphs for register allocation tend to contain many redundant instances of
single values (E.g. the value representing an interference constraint between
GPRs), the new RASolver class uses a uniquing scheme. This massively reduces
memory consumption for large register allocation problems. For example, looking
at the largest interference graph in each of the SPEC2006 benchmarks (the
largest graph will always set the memory consumption high-water mark for PBQP),
the average memory reduction for the PBQP costs was 400x. That's times, not
percent. The highest was 1400x. Yikes. So - this is fixed.
"PBQP: No longer feasting upon every last byte of your RAM".
Minor details:
- Fully C++11'd. Never copy-construct another vector/matrix!
- Cute tricks with cost metadata: Metadata that is derived solely from cost
matrices/vectors is attached directly to the cost instances themselves. That way
if you unique the costs you never have to recompute the metadata. 400x less
memory means 400x less cost metadata (re)computation.
Special thanks to Arnaud de Grandmaison, who has been the source of much
encouragement, and of many very useful test cases.
This new solver forms the basis for future work, of which there's plenty to do.
I will be adding TODO notes shortly.
- Lang.
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during the finalization for CGDebugInfo in clang we would RAUW
a type and it would result in a corrupted MDNode for an
imported declaration.
Testcase pending as reducing has been difficult.
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We were only using it so find the shared library extension and nm. There are
simpler ways to do those things :-)
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A 'remark' is information that is not an error or a warning, but rather some
additional information provided to the user. In contrast to a 'note' a 'remark'
is an independent diagnostic, whereas a 'note' always depends on another
diagnostic.
A typical use case for remark nodes is information provided to the user, e.g.
information provided by the vectorizer about loops that have been vectorized.
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Unfortunately, it is currently impossible to use a PatFrag as part of an output
pattern (the part of the pattern that has instructions in it) in TableGen.
Looking at the current implementation, this was clearly intended to work (there
is already code in place to expand patterns in the output DAG), but is
currently broken by the baked-in type-checking assumption and the order in which
the pattern fragments are processed (output pattern fragments need to be
processed after the instruction definitions are processed).
Fixing this is fairly simple, but requires some way of differentiating output
patterns from the existing input patterns. The simplest way to handle this
seems to be to create a subclass of PatFrag, and so that's what I've done here.
As a simple example, this allows us to write:
def crnot : OutPatFrag<(ops node:$in),
(CRNOR $in, $in)>;
def : Pat<(not i1:$in),
(crnot $in)>;
which captures the core use case: handling of repeated subexpressions inside
of complicated output patterns.
This will be used by an upcoming commit to the PowerPC backend.
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This is the data structure listed on Microsoft PE/COFF Spec Revision 8.3, p. 80.
The name of the struct is not mentioned in the Microsoft PE/COFF spec, so I made
it up.
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This is a temporary workaround for native arm linux builds:
PR18996: Changing regalloc order breaks "lencod" on native arm linux builds.
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Some MC components like Target Streamers or Assembly Parsers
may need to access the relocation model in order to expand
some directives and/or assembly macros.
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and update everything accordingly. This can be used to conditionalize
the amount of output in the backend based on the amount of debug
requested/metadata emission scheme by a front end (e.g. clang).
Paired with a commit to clang.
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This fixes spurious warnings in llvm-link about the datalayout not matching.
Thanks to Zalman Stern for reporting the bug!
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We don't have any test with more than 6 address spaces, so a DenseMap is
probably not the correct answer.
An unsorted array would also be OK, but we have to sort it for printing anyway.
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