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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some architectures. These are useful for interacting with multiarch or
bi-arch GCC (or GCC-based) toolchains.
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now that this handles the release / retain calls.
Adds a regression test for that bug (which is a compile-time
regression) and for the last two changes to the IntrusiveRefCntPtr,
especially tests for the memory leak due to copy construction of the
ref-counted object and ensuring that the traits are used for release /
retain calls.
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BitVector uses the native word size for its internal representation.
That doesn't work well for literal bit masks in source code.
This patch adds BitVector operations to efficiently apply literal bit
masks specified as arrays of uint32_t. Since each array entry always
holds exactly 32 bits, these portable bit masks can be source code
literals, probably produced by TableGen.
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make VariadicFunction actually be trivial. Do so, and also make it look
more like your standard trivial functor by making it a struct with no
access specifiers. The unit test is updated to initialize its functors
properly.
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variadic-like functions in C++98. See the comments in the header file
for a more detailed description of how these work. We plan to use these
extensively in the AST matching library. This code and idea were
originally authored by Zhanyong Wan. I've condensed it using macros
to reduce repeatition and adjusted it to fit better with LLVM's ADT.
Thanks to both David Blaikie and Doug Gregor for the review!
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was returning incorrect values in rare cases, and incorrectly marking
exact conversions as inexact in some more common cases. Fixes PR11406, and a
missed optimization in test/CodeGen/X86/fp-stack-O0.ll.
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Based on Horspool's simplified version of Boyer-Moore. We use a constant-sized table of
uint8_ts to keep cache thrashing low, needles bigger than 255 bytes are uncommon anyways.
The worst case is still O(n*m) but we do a lot better on the average case now.
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The APFloat "Zero" test was actually calling the
APFloat(const fltSemantics &, integerPart) constructor, and EXPECT_EQ was
treating 0 and -0 as equal.
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whatever the size of unsigned is), though this can't actually
occur for any integer value of NUM_NODES.
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more graphs, like all graphs with 5 nodes or less. With a 32 bit
unsigned type, the maximum is graphs with 6 nodes or less, but that
would take a while to test - 5 nodes or less already requires a few
seconds.
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This computes every graph with 4 or fewer nodes, and checks that the SCC
class indeed returns exactly the simply connected components reachable
from the initial node.
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errors like the one corrected by r135261. Migrate all LLVM callers of the old
constructor to the new one.
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vec.insert(vec.begin(), vec[3]);
The issue was that vec[3] returns a reference into the vector, which is invalidated when insert() memmove's the elements down to make space. The method needs to specifically detect and handle this case to correctly match std::vector's semantics.
Thanks to Howard Hinnant for clarifying the correct behavior, and explaining how std::vector solves this problem.
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Some platforms may treat denormals as zero, on other platforms multiplication
with a subnormal is slower than dividing by a normal.
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The idea is, that if an ieee 754 float is divided by a power of two, we can
turn the division into a cheaper multiplication. This function sees if we can
get an exact multiplicative inverse for a divisor and returns it if possible.
This is the hard part of PR9587.
I tested many inputs against llvm-gcc's frotend implementation of this
optimization and didn't find any difference. However, floating point is the
land of weird edge cases, so any review would be appreciated.
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