Issue #12142 has been updated by Yura Sokolov.


> Code with function (i << 2 + i + p + 1) & m is about 7% faster than one with simpler function (p + d) & h (14.5s vs. 15.7s)

Don't you forget that `d` should be incremented every step? Otherwise it is linear probing instead of quadratic probing.

> So if I can use all hash without visible slowdown even if it decreases number of collisions only by 1% on big tables, I'll take that chance.
> 
But my major argument is that using 32-bit index does not speed up work with hash tables. As I wrote I tried it and using 32-bit index did not improve the performance. So why we should create such hard constraint then.

If you stick with open addressing, then storing 32bit hash in 'entries' array and using 32bit index may speedup hash.

If you try to closed addressing, then 32bit hash + 32bit next pointer will allow to not increase element size.

Prototyping with `2^31` elements in memory?
I work in small social network (monthly auditory 30M, daily 5M), that exists for 9 years, and some in-memory tables sharded to 30 servers just get close to `2^30` totally (i.e. sum among all servers).

Please test time for inserting 200_000_000, 300_000_000 elements, does the time grows lineary?
And you didn't measure String keys or/and values.
You said, you computer has memory for 300_000_000 elements, how much it is? How much memory will 1_000_000_000 Int=>Int elements will consume? How much 1_000_000_000 memory String=>String will comsume?

Concerning `prev,next`
> I tried analogous approach what you proposed. 
> hash_shift 0.038

`hash_shift` result shows that your implementation had flaws, so performance numbers are not representable. I do not expect performance change lesser than 0.95.

What if it is not LRU? What if it is general purpose 100_000_000 table?
You said a lot of about 1% improvement on such table.
What will you say about excessive 2 second pause for rebuilt such table?
How much pause will be for 1_000_000_000 table?



----------------------------------------
Feature #12142: Hash tables with open addressing
https://bugs.ruby-lang.org/issues/12142#change-57321

* Author: Vladimir Makarov
* Status: Open
* Priority: Normal
* Assignee: 
----------------------------------------
~~~
 Hello, the following patch contains a new implementation of hash
tables (major files st.c and include/ruby/st.h).

  Modern processors have several levels of cache.  Usually,the CPU
reads one or a few lines of the cache from memory (or another level of
cache).  So CPU is much faster at reading data stored close to each
other.  The current implementation of Ruby hash tables does not fit
well to modern processor cache organization, which requires better
data locality for faster program speed.

The new hash table implementation achieves a better data locality
mainly by

  o switching to open addressing hash tables for access by keys.
    Removing hash collision lists lets us avoid *pointer chasing*, a
    common problem that produces bad data locality.  I see a tendency
    to move from chaining hash tables to open addressing hash tables
    due to their better fit to modern CPU memory organizations.
    CPython recently made such switch
    (https://hg.python.org/cpython/file/ff1938d12240/Objects/dictobject.c).
    PHP did this a bit earlier
    https://nikic.github.io/2014/12/22/PHPs-new-hashtable-implementation.html.
    GCC has widely-used such hash tables
    (https://gcc.gnu.org/svn/gcc/trunk/libiberty/hashtab.c) internally
    for more than 15 years.

  o removing doubly linked lists and putting the elements into an array
    for accessing to elements by their inclusion order.  That also
    removes pointer chaising on the doubly linked lists used for
    traversing elements by their inclusion order.

A more detailed description of the proposed implementation can be
found in the top comment of the file st.c.

The new implementation was benchmarked on 21 MRI hash table benchmarks
for two most widely used targets x86-64 (Intel 4.2GHz i7-4790K) and ARM
(Exynos 5410 - 1.6GHz Cortex-A15):

make benchmark-each ITEM=bm_hash OPTS='-r 3 -v' COMPARE_RUBY='<trunk ruby>'

Here the results for x86-64:

hash_aref_dsym       1.094
hash_aref_dsym_long          1.383
hash_aref_fix        1.048
hash_aref_flo        1.860
hash_aref_miss       1.107
hash_aref_str        1.107
hash_aref_sym        1.191
hash_aref_sym_long           1.113
hash_flatten         1.258
hash_ident_flo       1.627
hash_ident_num       1.045
hash_ident_obj       1.143
hash_ident_str       1.127
hash_ident_sym       1.152
hash_keys            2.714
hash_shift           2.209
hash_shift_u16       1.442
hash_shift_u24       1.413
hash_shift_u32       1.396
hash_to_proc         2.831
hash_values          2.701

The average performance improvement is more 50%.  ARM results are
analogous -- no any benchmark performance degradation and about the
same average improvement.

The patch can be seen as

https://github.com/vnmakarov/ruby/compare/trunk...hash_tables_with_open_addressing.patch

or in a less convenient way as pull request changes

https://github.com/ruby/ruby/pull/1264/files


This is my first patch for MRI and may be my proposal and
implementation have pitfalls.  But I am keen to learn and work on
inclusion of this code into MRI.

~~~



-- 
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