This switches `DCHECK` and `FATAL` as well.
The goal is to reduce the code size impact of these assertions so that
we can keep more of them enabled. Currently, the largest cost I see from
`CHECK` is not the actual check or the cold code itself, but actually
the failure to inline trivial functions due to the presence of the cold
code. This means that our goal isn't to reduce apparent code size in the
final binary but the LLVM IR cost assessed for these routines in the
inliner, which closely correlates with code size but is a bit different.
As discussed in #4283, experimentation shows that a single function call
with a minimal number of arguments is the lowest cost model for these.
This is easily achieved with a format-string API that internally uses
`llvm::formatv`. This PR is essentially the `CHECK` version of #4283.
However, the check macros are substantially harder to make work with
both format strings and streaming because they also take a condition.
Also, unexpectedly, I was very successful at devising a regular
expression based automated rewrite from the streaming to the format
string form with only low 10s of manual fixes. This includes compacting
strings broken up across lines, etc. Given how well that went, I've
prepared this PR which just directly switches to the format string API
and migrate everything to use it.
One nice side-effect is that the format string approach ends up greatly
simplifying the implementation here as well.
This is ... *shockingly* effective. Parsing speeds up by more than 3%
with just this change. And checking speeds up by **8%** with this change
alone:
```
BM_CompileAPIFileDenseDecls<Phase::Parse>/256 86.3µs ± 1% 82.9µs ± 1% -3.94% (p=0.000 n=17+19)
BM_CompileAPIFileDenseDecls<Phase::Parse>/1024 431µs ± 1% 415µs ± 1% -3.76% (p=0.000 n=18+19)
BM_CompileAPIFileDenseDecls<Phase::Parse>/4096 1.77ms ± 1% 1.71ms ± 1% -3.18% (p=0.000 n=18+19)
BM_CompileAPIFileDenseDecls<Phase::Parse>/16384 7.44ms ± 1% 7.17ms ± 2% -3.56% (p=0.000 n=18+20)
BM_CompileAPIFileDenseDecls<Phase::Parse>/65536 30.7ms ± 1% 29.7ms ± 1% -3.15% (p=0.000 n=18+20)
BM_CompileAPIFileDenseDecls<Phase::Parse>/262144 131ms ± 1% 127ms ± 1% -2.81% (p=0.000 n=18+18)
BM_CompileAPIFileDenseDecls<Phase::Check>/256 878µs ± 2% 800µs ± 1% -8.91% (p=0.000 n=19+20)
BM_CompileAPIFileDenseDecls<Phase::Check>/1024 1.88ms ± 2% 1.72ms ± 1% -8.56% (p=0.000 n=19+20)
BM_CompileAPIFileDenseDecls<Phase::Check>/4096 5.78ms ± 2% 5.28ms ± 1% -8.70% (p=0.000 n=20+18)
BM_CompileAPIFileDenseDecls<Phase::Check>/16384 21.9ms ± 1% 20.1ms ± 1% -8.02% (p=0.000 n=18+20)
BM_CompileAPIFileDenseDecls<Phase::Check>/65536 90.4ms ± 2% 83.1ms ± 1% -8.04% (p=0.000 n=19+20)
BM_CompileAPIFileDenseDecls<Phase::Check>/262144 381ms ± 2% 352ms ± 1% -7.79% (p=0.000 n=19+19)
```
---------
Co-authored-by: Richard Smith <richard@metafoo.co.uk>
Co-authored-by: josh11b <15258583+josh11b@users.noreply.github.com>
This turns out to be super useful now that we have the `key_context`
mechanism and can do much more meaningful heterogeneous lookups, where
the stored key can be *very* different from the lookup key.
---------
Co-authored-by: Richard Smith <richard@metafoo.co.uk>
This lets copy and move assignment work. While it's a bit suboptimal to
do assignment with these tables, it still seems like an unreasonable
burden to not allow the basics to work. Even the toolchain ended up
doing this in a few places.
---------
Co-authored-by: Richard Smith <richard@metafoo.co.uk>
This adds two different growth APIs. This is instead of the more
conventional STL `reserve` method. One allows users that aren't trying
to grow in anticipation of an *exact* count of insertions, but generally
trying to size the table to the correct ballpark with a power-of-two
estimate.
The other API allows pre-growing to allow a specific number of
insertions to be performed without further growth. This API takes the
maximum load factor and other implementation details into account.
---------
Co-authored-by: josh11b <15258583+josh11b@users.noreply.github.com>
Previously we just looked at the raw count of probed keys. Now, we
compute the average and max of both the probe _distance_ measured in the
number of _groups_ probed, and the number of probe _compares_ measured
in the compares required _before_ finding the matching entry.
This lets us understand the relative impact of probe-distance vs. tag
collisions on a given set of benchmark keys. Some of this is motivated
by considering additional optimization techniques similar to those used
in Boost's table and the F14 table from Facebook/Meta.
---------
Co-authored-by: Richard Smith <richard@metafoo.co.uk>
The hash table design is heavily based on Abseil's ["Swiss
Tables"][swiss-tables] design. It uses an array of bytes storing
metadata about each entry and an array of entries where each is a pair
of key and value. The metadata byte consists of 7-bits of hash of the
key (distinct from the bits used to index the table), and one bit
indicating the presence of a special entry -- either empty or deleted.
[swiss-tables]: https://abseil.io/about/design/swisstables
There are a large range of optimizations and other nuanced aspects of
this hash table design and implementation, a good point to understand
that context is `raw_hashtable.h` which has an overview of the design
and references to various other files for relevant details.
---------
Co-authored-by: josh11b <15258583+josh11b@users.noreply.github.com>
