It always returns the iterator for the first inserted element, or the passed in
iterator if the inserted range was empty. Flesh out the unit test more and fix
all the cases it uncovered so far.
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SmallDenseMap::swap.
First, make it parse cleanly. Yay for uninstantiated methods.
Second, make the inline-buckets case work correctly. This is way
trickier than it should be due to the uninitialized values in empty and
tombstone buckets.
Finally fix a few typos that caused construction/destruction mismatches
in the counting unittest.
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destruction and fix a bug in SmallDenseMap they caught.
This is kind of a poor-man's version of the testing that just adds the
addresses to a set on construction and removes them on destruction. We
check that double construction and double destruction don't occur.
Amusingly enough, this is enough to catch a lot of SmallDenseMap issues
because we spend a lot of time with fixed stable addresses in the inline
buffer.
The SmallDenseMap bug fix included makes grow() not double-destroy in
some cases. It also fixes a FIXME there, the code was pretty crappy. We
now don't have any wasted initialization, but we do move the entries in
inline bucket array an extra time. It's probably a better tradeoff, and
is much easier to get correct.
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implementation.
This type includes an inline bucket array which is used initially. Once
it is exceeded, an array of 64 buckets is allocated on the heap. The
bucket count grows from there as needed. Some highlights of this
implementation:
- The inline buffer is very carefully aligned, and so supports types
with alignment constraints.
- It works hard to avoid aliasing issues.
- Supports types with non-trivial constructors, destructors, copy
constructions, etc. It works reasonably hard to minimize copies and
unnecessary initialization. The most common initialization is to set
keys to the empty key, and so that should be fast if at all possible.
This class has a performance / space trade-off. It tries to optimize for
relatively small maps, and so packs the inline bucket array densely into
the object. It will be marginally slower than a normal DenseMap in a few
use patterns, so it isn't appropriate everywhere.
The unit tests for DenseMap have been generalized a bit to support
running over different map implementations in addition to different
key/value types. They've then been automatically extended to cover the
new container through the magic of GoogleTest's typed tests.
All of this is still a bit rough though. I'm going to be cleaning up
some aspects of the implementation, documenting things better, and
adding tests which include non-trivial types. As soon as I'm comfortable
with the correctness, I plan to switch existing users of SmallMap over
to this class as it is already more correct w.r.t. construction and
destruction of objects iin the map.
Thanks to Benjamin Kramer for all the reviews of this and the lead-up
patches. That said, more review on this would really be appreciated. As
I've noted a few times, I'm quite surprised how hard it is to get the
semantics for a hashtable-based map container with a small buffer
optimization correct. =]
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of typename. GCC and Clang were fine with this, but MSVC won't accept
it. Fortunately, it also doesn't need it. Yuck.
Thanks to Nakamura for pointing this out in IRC.
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These were already trying to be type parameterized over different
key/value pairs. I've realized this goal using GoogleTest's typed test
functionality. This allows us to easily replicate the tests across
different key/value combinations and soon different mapping templates.
I've fixed a few bugs in the tests and extended them a bit in the
process as many tests were only applying to the int->int mapping.
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Returning a temporary BitVector is very expensive. If you must, create
the temporary explicitly: Use BitVector(A).flip() instead of ~A.
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- FlatArrayMap. Very simple map container that uses flat array inside.
- MultiImplMap. Map container interface, that has two modes, one for small amount of elements and one for big amount.
- SmallMap. SmallMap is DenseMap compatible MultiImplMap. It uses FlatArrayMap for small mode, and DenseMap for big mode.
Also added unittests for new classes and update for ProgrammersManual.
For more details about new classes see ProgrammersManual and comments in sourcecode.
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This nicely handles the most common case of virtual register sets, but
also handles anticipated cases where we will map pointers to IDs.
The goal is not to develop a completely generic SparseSet
template. Instead we want to handle the expected uses within llvm
without any template antics in the client code. I'm adding a bit of
template nastiness here, and some assumption about expected usage in
order to make the client code very clean.
The expected common uses cases I'm designing for:
- integer keys that need to be reindexed, and may map to additional
data
- densely numbered objects where we want pointer keys because no
number->object map exists.
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integral and enumeration types. This is accomplished with a bit of
template type trait magic. Thanks to Richard Smith for the core idea
here to detect viable types by detecting the set of types which can be
default constructed in a template parameter.
This is used (in conjunction with a system for detecting nullptr_t
should it exist) to provide an is_integral_or_enum type trait that
doesn't need a whitelist or direct compiler support.
With this, the hashing is extended to the more general facility. This
will be used in a subsequent commit to hashing more things, but I wanted
to make sure the type trait magic went through the build bots separately
in case other compilers don't like this formulation.
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This currently assumes that both sets have the same SmallSize to keep the implementation simple,
a limitation that can be lifted if someone cares.
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just ensure that the number of bytes in the pair is the sum of the bytes
in each side of the pair. As long as thats true, there are no extra
bytes that might be padding.
Also add a few tests that previously would have slipped through the
checking. The more accurate checking mechanism catches these and ensures
they are handled conservatively correctly.
Thanks to Duncan for prodding me to do this right and more simply.
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hashable data. This matters when we have pair<T*, U*> as a key, which is
quite common in DenseMap, etc. To that end, we need to detect when this
is safe. The requirements on a generic std::pair<T, U> are:
1) Both T and U must satisfy the existing is_hashable_data trait. Note
that this includes the requirement that T and U have no internal
padding bits or other bits not contributing directly to equality.
2) The alignment constraints of std::pair<T, U> do not require padding
between consecutive objects.
3) The alignment constraints of U and the size of T do not conspire to
require padding between the first and second elements.
Grow two somewhat magical traits to detect this by forming a pod
structure and inspecting offset artifacts on it. Hopefully this won't
cause any compilers to panic.
Added and adjusted tests now that pairs, even nested pairs, are treated
as just sequences of data.
Thanks to Jeffrey Yasskin for helping me sort through this and reviewing
the somewhat subtle traits.
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an open question of whether we can do better than this by treating pairs
as boring data containers and directly hashing the two subobjects. This
at least makes the API reasonable.
In order to make this change, I reorganized the header a bit. I lifted
the declarations of the hash_value functions up to the top of the header
with their doxygen comments as these are intended for users to interact
with. They shouldn't have to wade through implementation details. I then
defined them at the very end so that they could be defined in terms of
hash_combine or any other hashing infrastructure.
Added various pair-hashing unittests.
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the hash_code. I'm not sure what I was thinking here, the use cases for
special values are in the *keys*, not in the hashes of those keys.
We can always resurrect this if needed, or clients can accomplish the
same goal themselves. This makes the general case somewhat faster (~5
cycles faster on my machine) and smaller with less branching.
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to keep this around -- updating golden tests is annoying otherwise.
Thanks to Benjamin for pointing this omission out on IRC.
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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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chip in r139383, and the PSP components of the triple are really
annoying to parse. Let's leave this chapter behind. There is no reason
to expect LLVM to see a PSP-related triple these days, and so no
reasonable motivation to support them.
It might be reasonable to prune a few of the older MIPS triple forms in
general, but as those at least cause no burden on parsing (they aren't
both a chip and an OS!), I'm happy to leave them in for now.
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For objects that can be identified by small unsigned keys, SparseSet
provides constant time clear() and fast deterministic iteration. Insert,
erase, and find operations are typically faster than hash tables.
SparseSet is useful for keeping information about physical registers,
virtual registers, or numbered basic blocks.
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construction. Simplify its interface, implementation, and users
accordingly as there is no longer an 'uninitialized' state to check for.
Also, fixes a bug lurking in the interface as there was one method that
didn't correctly check for initialization.
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