This discussion has limited its context to comparing *in-memory* Dictionary implementations, where open-addressing has an advantage. Using Magma reveals another context where chained Dictionary's can have some advantages: When they're large, persistent, and shared in a multi-user system -- e.g., when grow rehashing is not an option. Not only do chained Dictionary's make that scenario possible, they improve the concurrency -- different users can add/change/remove from the Dictionary as long as they occur at different keys. With regular Dictionary's, concurrency guarantees a commit conflict. - Chris On Fri, Jun 29, 2018 at 2:56 PM, Peter Crowther <peter@ozzard.org> wrote:
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 <bera.clement@gmail.com> 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 tricky1 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 <maxleske@gmail.com> 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 <maxleske@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/
-- Clément Béra https://clementbera.github.io/ https://clementbera.wordpress.com/