[squeak-dev] [Pharo-dev] Why do we have SmallDictionary?

Max Leske maxleske at gmail.com
Sat Jun 16 18:49:27 UTC 2018

On 8 Jun 2018, at 09:48, Stéphane Rollandin wrote:

> FWIW it seems there is no SmallDictionary in Squeak.

Oh... Thanks Stéf, I wasn't aware of that.

On 8 June 2018, at 15:13, John Brant wrote:

>>> Is anyone aware of a reason for hanging on to SmallDictionary? I'm 
>>> also curious to know how SmallDictionary came to be. There must have 
>>> been some advantage over Dictionary at some point in the past.
>> It came from RB… the idea was that there are (in the Refactoring 
>> engine) a lot of very small dictionaries with <10 elements.
>> The idea is that for such dictionaries, the overhead of hashing was 
>> higher than just linear search.
> I created its ancestor in VW some 20+ years ago (as 
> RBSmallDictionary).
> It was used when doing pattern matching. When it performs a pattern
> match against an AST, it puts the potential value of the pattern
> variable in the dictionary. If the value is used later in the pattern,
> then we can get the previous value and make sure that we have an
> equivalent AST. This allows you to write patterns like:
> 	`@a = `@a
> to find where someone has the same expression on both sides of the #=
> message. Since most patterns have very few pattern variables, these
> dictionaries won't hold many entries. Furthermore, we are likely to
> abort the match when we have 0 entries.
> The original RBSmallDictionary had an #empty method that "emptied" the
> dictionary without actually removing any elements -- it just set the
> size to 0. In a general dictionary this would lead to memory leaks 
> since
> the previous values are still held by the dictionary. However, these
> dictionaries were only used during the matching process and went away
> after the process completed.
> Anyway, at the time when we converted our pattern matching code from
> using the VW parser with our pattern matching extensions to use the 
> new
> RB parser with pattern matching, the time to run Smalllint on the 
> image
> was cut in half even though our parser was quite a bit slower than the
> VW parser. I don't remember everything that was done, but I think that
> most of the speedup came from having special pattern AST nodes and the
> small dictionary.
> John Brant

Very interesting! Thanks John!

As Marcus has mentioned before in this thread, it would make a lot of 
sense to run benchmarks again. Actually, I think it would be nice to 
have a benchmark suite for these cases, that would let us monitor the 
performance and ensure that changes to the codebase don't have a 
deteriorative effect. I'm not saying that it would be easy to make this 
happen, writing proper benchmarks is hard (for me especially, as it 
seems, given my utter failure to think of the edge cases before starting 
this thread). Such a suite might also prevent these sorts of questions 
on the mailing list in the future, or at least might make it easier to 
answer them.

On 8 June 2018, at 13:01, Andres Valloud wrote:

> In addition, open addressing with linear probing has superior cache 
> line read behavior (no indirection / random traversal, and if the 
> first probe misses the second one was likely cached by the first one).

Ah, nice catch! Although that would require frequent access to the 
dictionary / repeated access to the same items to have an effect, 
wouldn't it?

On 8 Jun 2018, at 10:01, Clément Bera wrote:

> Hi Max,
> Theoretically, for a small number of elements, usually somewhere 
> between 3
> and 30 depending on implementations, a linear search is faster than a 
> hash
> search, especially in the Pharo dictionary hash search implementation.
> Efficient dictionary implementations are usually bucket-based. The
> dictionary holds a certain number of buckets, and based on the key 
> hash,
> the bucket where the key value is present is determined. Small buckets 
> are
> linear (arrays or linked list). Large buckets are typically balanced 
> binary
> trees (red-black trees typically). Under a certain number of elements 
> there
> is a single bucket, which means a linear search is performed, as for 
> the
> SmallDictionary. When it grows the dictionary search becomes a 
> combination
> between a hash search and a linear or tree search.
> Pharo dictionary search is first hash-based, then all the buckets are
> represented next to each other in the same arrays and a linear search 
> is
> performed there, leading to many collisions and slower search time
> (especially when the element is not found), sometimes the code 
> searches
> over multiple buckets because the dictionary is too full or there are 
> too
> many near-collisions. The memory consumption is competitive with the
> advanced implementations though (precise measurements would need to be
> made).
> Method dictionaries are represented differently to optimize the 
> look-up
> logic.
> If you want to improve things and have one dictionary implementation
> instead of two, implement or look for a bucket based dictionary and 
> put it
> in the base image instead of Dictionary. This is quite some work since
> there are many APIs to port. You can look at the Pinnochio 
> implementation,
> it's quite good but they've not implemented large buckets.

Thanks for the detailed explanations Clément and Levente. I'll probably 
not add a new dictionary implementation ;)

> On Fri, Jun 8, 2018 at 8:46 AM, Max Leske <maxleske at gmail.com> wrote:
>> Hi,
>> I was messing around with SmallDictionary when I suddenly realised 
>> that I
>> can't find a single reason to use it over a normal Dictionary. While 
>> its
>> name and class comment imply that it is somehow an optimised 
>> Dictionary, I
>> don't see any measurement where that actually holds up. The following 
>> was
>> run in a Pharo 7 image on a recent VM (see below):
>> | d |
>> d := SmallDictionary new.
>> d sizeInMemory. "24"
>> [100000 timesRepeat: [
>>         1 to: 100 do: [ :i | d at:i put: i] ] ] timeToRun. 
>> "0:00:00:05.226"
>> [100000 timesRepeat: [
>>         d at: 48 ifAbsent: [] ] ] timeToRun. "0:00:00:00.041"
>> | d |
>> d := Dictionary new.
>> d sizeInMemory. "16"
>> [100000 timesRepeat: [
>>         1 to: 100 do: [ :i | d at:i put: i] ] ] timeToRun. 
>> "0:00:00:00.385"
>> [100000 timesRepeat: [
>>         d at: 48 ifAbsent: [] ] ] timeToRun.  "0:00:00:00.006"
>> As you can see, SmallDictionary is 8 bytes larger per instance and
>> significantly faster while reading and writing (I know that this 
>> isn't a
>> good benchmark but it suffices to make my point).
>> Is anyone aware of a reason for hanging on to SmallDictionary? I'm 
>> also
>> curious to know how SmallDictionary came to be. There must have been 
>> some
>> advantage over Dictionary at some point in the past.
>> Cheers,
>> Max
>> Image version: Pharo 7.0
>> Build information: Pharo-7.0+alpha.build.961.sha.
>> a69e72a97136bc3f93831584b6efa2b1703deb84 (32 Bit)
>> VM version: CoInterpreter VMMaker.oscog- nice.2281 uuid:
>> 4beeaee7-567e-1a4b-b0fb-bd95ce302516 Nov 27 2017
>> StackToRegisterMappingCogit VMMaker.oscog-nice.2283 uuid:
>> 2d20324d-a2ab-48d6-b0f6-9fc3d66899da Nov 27 2017
>> VM: 201711262336 
>> https://github.com/OpenSmalltalk/opensmalltalk-vm.git $
>> Date: Mon Nov 27 00:36:29 2017 +0100 $ Plugins: 201711262336
>> https://github.com/OpenSmalltalk/opensmalltalk-vm.git $
>> OS: macOS 10.13.5
>> Machine: MacBook Pro (13-inch, 2016, Four Thunderbolt 3 Ports)
> -- 
> Clément Béra
> https://clementbera.github.io/
> https://clementbera.wordpress.com/

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