Why do we have SmallDictionary?

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)

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 <[hidden email]> 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/

On Fri, 29 Jun 2018, Clément Bera wrote:

Do you guys agree with this?

Mostly. I don't think open addressing's performance is proporional to loadFactor / (1 - loadFactor). That formula would be correct if you had a an array filled up to loadFactor randomly, but linear probing creates longer chains due to clustering, so it's worse than that in practice.
Here's a small table based on my measurements:

lf      formula measurement
0.5     1.0     ~1.5
0.55    1.222   ~1.96
0.6     1.5     ~2.62
0.65    1.857   ~3.56
0.7     2.333   ~5.06
0.75    3.0     ~7.5
0.8     4.0     ~12.0

In Squeak load factor normally varies between 0.5 and 0.75, but it can be anything between 0.0 and 0.8 (only compaction can push load factor above 0.75), so performance can differ quite a bit. Based on these numbers 0.7 sounds like a reasonable upper limit - something we might want to consider.
You might think that linear probing's clustering effect costs too much, but because of cache locality this is probably still faster than linked lists or double hashing.

It is also irrelevant if you can store more elements than the size of the hash table, because that breaks the precondition of the use of hash tables, therefore the contract too. So, the promised O(1) runtime of operations is also gone in that case.

Finally, removals do not clog the hash tables using open addressing unless you use lazy deletion[1], which is something you should avoid.

Their open addressing implementation is slightly different from the Smalltalk one (they have deleted entries upon removal while we fix collisions,
at least in Pharo 6).

Proactively removing the garbage - aka #fixCollisionsFrom: - takes O(chain length) time, which is amortized O(1) if your hash function is good enough.
Lazy deletion degrades lookup performance over time, because lookups have to treat deleted entries as existing entries. It makes deletion somewhat quicker, because you can stop the lookup procedure as soon as you find your element, but what you save is amortized O(1).

Their chaining implementation does not use balanced binary tree when a bucket becomes large like Java's implementation.

Because hash tables in general don't do such thing. During my time at the
university, it was one of the tricky questions why hash tables use linked lists instead of balanced binary trees for collision resolution.
The answer is that you don't need them, because the contract of hash
tables is that you'll (practically) not have long chains if your hash
function is good enough.
Using binary trees for collision resolution mitigate situations when your hash function is not good enough or when you don't have full control over your hash values. But those are fairly rare in practice, and you still lose the O(1) operation runtime cost.
Binary trees also require your elements to have a total order defined on them.

I don't really understand why an open addressing implementation is trickier to implement, in high level languages such as Smalltalk I may agree
that it might be easier to implement chaining, but in low level language such as in the VM, IMO open addressing is easier since you've got only
one large chunk of memory to manage. Maybe it's because of hash collisions, having good hash and good hash collection size (See HashTableSizes) is
not that easy.

It's easy to make indexing mistakes, especially when wrap-around is involved. Also, deletion is tricky and is often left as an exercise for the reader. Even the wikipedia article[2] has quite complex pseudocode for it. The version you find in Squeak has a few hidden tricks too, but those are there for performance and the code would still work without them.

Levente

[1] https://en.wikipedia.org/wiki/Lazy_deletion
[2] https://en.wikipedia.org/wiki/Open_addressing

I'm curious because I do more and more bare metal programming and I end up having to implement this kind of things myself, I've always implemented
naive open addressing up until now without really understanding details.

[1] http://www.algolist.net/Data_structures/Hash_table/Open_addressing

On Sat, Jun 16, 2018 at 8:49 PM, Max Leske <[hidden email]> wrote:


      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 <[hidden email]> 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/







--
Clément Béra
https://clementbera.github.io/https://clementbera.wordpress.com/

In an environment with garbage collection, it's worth considering the overhead of one's representation on the memory allocator and the garbage collector.  Open addressing adds objects - the exact number of these varies - and hence adds GC overhead (= paying extra cycles for the life of the dictionary even if it never changes).  Each object also has some bytes of overhead for headers, so chaining becomes less efficient as collisions occur due to extra object allocations.

Also, modern processors and memory systems are very good at reading forward in memory, as there's often a wide memory bus (and sometimes other optimisations).  This means it's often quick to read the next entry as it's already been pulled in due to adjacency.  By contrast, chaining puts collisions all over memory, reducing adjacency and increasing the bandwidth required to memory.

I'm unconvinced that chaining dictionaries are sensible in modern languages with garbage collection on modern architectures where bandwidth to memory, and cache space, are both scarce resources.  The days of memory being truly random-access are long gone, and too few programmers realise the impact that can have.

Cheers,

- Peter

On 29 June 2018 at 08:27, Clément Bera <[hidden email]> wrote:

Hum, about dictionary implementations, I've just found this [1]:

Open addressing vs. chaining

	Chaining	Open addressing
Collision resolution	Using external data structure	Using hash table itself
Memory waste	Pointer size overhead per entry (storing list heads in the table)	No overhead 1
Performance dependence on table's load factor	Directly proportional	Proportional to (loadFactor) / (1 - loadFactor)
Allow to store more items, than hash table size	Yes	No. Moreover, it's recommended to keep table's load factor below 0.7
Hash function requirements	Uniform distribution	Uniform distribution, should avoid clustering
Handle removals	Removals are ok	Removals clog the hash table with "DELETED" entries
Implementation	Simple	Correct implementation of open addressing based hash table is quite tricky

¹ Hash tables with chaining can work efficiently even with load factor more than 1. At the same time, tables based on open addressing scheme require load factor not to exceed 0.7 to be efficient. Hence, 30% of slots remain empty, which leads to obvious memory waste.

Do you guys agree with this?

Their open addressing implementation is slightly different from the Smalltalk one (they have deleted entries upon removal while we fix collisions, at least in Pharo 6).

Their chaining implementation does not use balanced binary tree when a bucket becomes large like Java's implementation.

I don't really understand why an open addressing implementation is trickier to implement, in high level languages such as Smalltalk I may agree that it might be easier to implement chaining, but in low level language such as in the VM, IMO open addressing is easier since you've got only one large chunk of memory to manage. Maybe it's because of hash collisions, having good hash and good hash collection size (See HashTableSizes) is not that easy.

I'm curious because I do more and more bare metal programming and I end up having to implement this kind of things myself, I've always implemented naive open addressing up until now without really understanding details.

[1] http://www.algolist.net/Data_structures/Hash_table/Open_addressing

On Sat, Jun 16, 2018 at 8:49 PM, Max Leske <[hidden email]> wrote:

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 <[hidden email]> 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/

--

Clément Béra
https://clementbera.github.io/

https://clementbera.wordpress.com/