This uses a header-only extraction of the Boost unordered hashtable
project to allow a trivial Bazel build and for us to benchmark against
it effectively.
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.
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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>
Chandler Carruth
