Since the llvm::sys::fs::map_file_pages() support function it relies on
is not yet implemented on Windows, the unit tests for FileOutputBuffer
are currently conditionalized to run only on unix.
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of the proposed standard hashing interfaces (N3333), and to use
a modified and tuned version of the CityHash algorithm.
Some of the highlights of this change:
-- Significantly higher quality hashing algorithm with very well
distributed results, and extremely few collisions. Should be close to
a checksum for up to 64-bit keys. Very little clustering or clumping of
hash codes, to better distribute load on probed hash tables.
-- Built-in support for reserved values.
-- Simplified API that composes cleanly with other C++ idioms and APIs.
-- Better scaling performance as keys grow. This is the fastest
algorithm I've found and measured for moderately sized keys (such as
show up in some of the uniquing and folding use cases)
-- Support for enabling per-execution seeds to prevent table ordering
or other artifacts of hashing algorithms to impact the output of
LLVM. The seeding would make each run different and highlight these
problems during bootstrap.
This implementation was tested extensively using the SMHasher test
suite, and pased with flying colors, doing better than the original
CityHash algorithm even.
I've included a unittest, although it is somewhat minimal at the moment.
I've also added (or refactored into the proper location) type traits
necessary to implement this, and converted users of GeneralHash over.
My only immediate concerns with this implementation is the performance
of hashing small keys. I've already started working to improve this, and
will continue to do so. Currently, the only algorithms faster produce
lower quality results, but it is likely there is a better compromise
than the current one.
Many thanks to Jeffrey Yasskin who did most of the work on the N3333
paper, pair-programmed some of this code, and reviewed much of it. Many
thanks also go to Geoff Pike Pike and Jyrki Alakuijala, the original
authors of CityHash on which this is heavily based, and Austin Appleby
who created MurmurHash and the SMHasher test suite.
Also thanks to Nadav, Tobias, Howard, Jay, Nick, Ahmed, and Duncan for
all of the review comments! If there are further comments or concerns,
please let me know and I'll jump on 'em.
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This CL delays reading of function bodies from initial parse until
materialization, allowing overlap of compilation with bitcode download.
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It is an endian-aware helper that can read data from a StringRef. It will
come in handy for DWARF parsing. This class is inspired by LLDB's
DataExtractor, but is stripped down to the bare minimum needed for DWARF.
Comes with unit tests!
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The header file was already properly located. The previous need for it
in Support had to do with the version string printing which was fixed in
r135757.
Also update build dependencies where libraries that needed the
functionality of the Target library (in the form of the TargetRegistry)
were picking it up via Support. This is pretty pervasive, essentially
every TargetInfo library (ARMInfo, etc) uses TargetRegistry, making it
depend on Target. All of these were previously just sneaking by.
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Patch by: Jakub Staszak!
Introduces BranchProbability. Changes unsigned to uint32_t all over and
uint64_t only when overflow is expected.
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This implementation already exists as ConnectedVNInfoEqClasses in
LiveInterval.cpp, and it seems to be generally useful to have a light-weight way
of forming equivalence classes of small integers.
IntEqClasses doesn't allow enumeration of the elements in a class.
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This is a sorted interval map data structure for small keys and values with
automatic coalescing and bidirectional iteration over coalesced intervals.
Except for coalescing intervals, it provides similar functionality to std::map.
It is however much more compact for small keys and values, and hopefully faster
too.
The container object itself can hold the first few intervals without any
allocations, then it switches to a cache conscious B+-tree representation. A
recycling allocator can be shared between many containers, even between
containers holding different types.
The IntervalMap is initially intended to be used with SlotIndex intervals for:
- Backing store for LiveIntervalUnion that is smaller and faster than std::set.
- Backing store for LiveInterval with less overhead than std::vector for typical
intervals and O(N log N) merging of large intervals. 99% of virtual registers
need 4 entries or less and would benefit from the small object optimization.
- Backing store for LiveDebugVariable which doesn't exist yet, but will track
debug variables during register allocation.
This is a work in progress. Missing items are:
- Performance metrics.
- erase().
- insert() shrinkage.
- clear().
- More performance metrics.
- Simplification and detemplatization.
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This is a sorted interval map data structure for small keys and values with
automatic coalescing and bidirectional iteration over coalesced intervals.
Except for coalescing intervals, it provides similar functionality to std::map.
It is however much more compact for small keys and values, and hopefully faster
too.
The container object itself can hold the first few intervals without any
allocations, then it switches to a cache conscious B+-tree representation. A
recycling allocator can be shared between many containers, even between
containers holding different types.
The IntervalMap is initially intended to be used with SlotIndex intervals for:
- Backing store for LiveIntervalUnion that is smaller and faster than std::set.
- Backing store for LiveInterval with less overhead than std::vector for typical
intervals and O(N log N) merging of large intervals. 99% of virtual registers
need 4 entries or less and would benefit from the small object optimization.
- Backing store for LiveDebugVariable which doesn't exist yet, but will track
debug variables during register allocation.
This is a work in progress. Missing items are:
- Performance metrics.
- erase().
- insert() shrinkage.
- clear().
- More performance metrics.
- Simplification and detemplatization.
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- This can give substantial speedups in the delta process for inputs we can construct dependency information for.
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- This is a pretty slow / memory intensive implementation, and I will likely
change it to an iterative model, but it works.
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