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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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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