Clojure

Unrolled small vectors

Details

  • Type: Enhancement Enhancement
  • Status: Open Open
  • Priority: Critical Critical
  • Resolution: Unresolved
  • Affects Version/s: Release 1.7
  • Fix Version/s: Backlog
  • Component/s: None
  • Patch:
    Code
  • Approval:
    Incomplete

Description

As discussed on the mailing list [1], this patch has two unrolled variants of vectors and maps, with special inner classes for each cardinality. Currently both grow to six elements before spilling over into the general versions of the data structures, which is based on rough testing but can be easily changed. At Rich's request, I haven't included any integration into the rest of the code, and there are top-level static create() methods for each.

The sole reason for this patch is performance, both in terms of creating data structures and performing operations on them. This can be seen as a more verbose version of the trick currently played with PersistentArrayMap spilling over into PersistentHashMap. Based on the benchmarks, which can be run by cloning cambrian-collections [2] and running 'lein test :benchmark', this should supplant PersistentArrayMap. Performance is at least on par with PAM, and often much faster. Especially noteworthy is the creation time, which is 5x faster for maps of all sizes (lein test :only cambrian-collections.map-test/benchmark-construction), and on par for 3-vectors, but 20x faster for 5-vectors. There are similar benefits for hash and equality calculations, as well as calls to reduce().

This is a big patch (over 5k lines), and will be kind of a pain to review. My assumption of correctness is based on the use of collection-check, and the fact that the underlying approach is very simple. I'm happy to provide a high-level description of the approach taken, though, if that will help the review process.

I'm hoping to get this into 1.7, so please let me know if there's anything I can do to help accomplish that.

[1] https://groups.google.com/forum/#!topic/clojure-dev/pDhYoELjrcs
[2] https://github.com/ztellman/cambrian-collections

Patch: unrolled-vector-2.patch

Screener Notes: The approach is clear and understandable. Given the volume of generated code, I believe that best way to improve confidence in this code is to get people using it asap, and add collection-test [3] to the Clojure test suite. I would also like to get the generator [4] included in the Clojure repo. We don't need to necessarily automate running it, but would be nice to have it nearby if we want to tweak something later.

[3] https://github.com/ztellman/collection-check/blob/master/src/collection_check.clj
[4] https://github.com/ztellman/cambrian-collections/blob/master/generate/cambrian_collections/vector.clj

  1. unrolled-collections.diff
    02/Sep/14 12:13 PM
    117 kB
    Zach Tellman
  2. unrolled-collections-2.diff
    03/Sep/14 4:31 PM
    117 kB
    Zach Tellman
  3. unrolled-vector.patch
    07/Dec/14 10:34 PM
    42 kB
    Zach Tellman
  4. unrolled-vector-2.patch
    09/Dec/14 5:43 PM
    42 kB
    Zach Tellman

Activity

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Zach Tellman added a comment -

Oh, I forgot to mention that I didn't make a PersistentUnrolledSet, since the existing wrappers can use the unrolled map implementation. However, it would be moderately faster and more memory efficient to have one, so let me know if it seems worthwhile.

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Zach Tellman added a comment - Oh, I forgot to mention that I didn't make a PersistentUnrolledSet, since the existing wrappers can use the unrolled map implementation. However, it would be moderately faster and more memory efficient to have one, so let me know if it seems worthwhile.
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Nicola Mometto added a comment -

Zach, the patch you added isn't in the correct format, they need to be created using `git format-patch`

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Nicola Mometto added a comment - Zach, the patch you added isn't in the correct format, they need to be created using `git format-patch`
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Nicola Mometto added a comment - - edited

Also, I'm not sure if this is on-scope with the ticket but those patches break with *print-dup*, as it expects a static create(x) method for each inner class.

I'd suggest adding a create(Map x) static method for the inner PersistentUnrolledMap classes and a create(ISeq x) one for the inner PersistentUnrolledVector classes

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Nicola Mometto added a comment - - edited Also, I'm not sure if this is on-scope with the ticket but those patches break with *print-dup*, as it expects a static create(x) method for each inner class. I'd suggest adding a create(Map x) static method for the inner PersistentUnrolledMap classes and a create(ISeq x) one for the inner PersistentUnrolledVector classes
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Alex Miller added a comment -
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Alex Miller added a comment - Re making patches, see: http://dev.clojure.org/display/community/Developing+Patches
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Jozef Wagner added a comment -

I wonder what is the overhead of having meta and 2 hash fields in the class. Have you considered a version where the hash is computed on the fly and where you have two sets of collections, one with meta field and one without, using former when the actual metadata is attached to the collection?

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Jozef Wagner added a comment - I wonder what is the overhead of having meta and 2 hash fields in the class. Have you considered a version where the hash is computed on the fly and where you have two sets of collections, one with meta field and one without, using former when the actual metadata is attached to the collection?
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Zach Tellman added a comment - - edited

I've attached a patch using the proper method. Somehow I missed the detailed explanation for how to do this, sorry. I know the guidelines say not to delete previous patches, but since the first one isn't useful I've deleted it to minimize confusion.

I did the print-dup friendly create methods, and then realized that once these are properly integrated, 'pr' will just emit these as vectors. I'm fairly sure the create methods aren't necessary, so I've commented them out, but I'm happy to add them back in if they're useful for some reason I can't see.

I haven't given a lot of thought to memory efficiency, but I think caching the hashes are worthwhile. I can see an argument for creating a "with-meta" version of each collection, but since that would double the size of an already enormous patch, I think that should probably wait.

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Zach Tellman added a comment - - edited I've attached a patch using the proper method. Somehow I missed the detailed explanation for how to do this, sorry. I know the guidelines say not to delete previous patches, but since the first one isn't useful I've deleted it to minimize confusion. I did the print-dup friendly create methods, and then realized that once these are properly integrated, 'pr' will just emit these as vectors. I'm fairly sure the create methods aren't necessary, so I've commented them out, but I'm happy to add them back in if they're useful for some reason I can't see. I haven't given a lot of thought to memory efficiency, but I think caching the hashes are worthwhile. I can see an argument for creating a "with-meta" version of each collection, but since that would double the size of an already enormous patch, I think that should probably wait.
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Zach Tellman added a comment -

I found a bug! Like PersistentArrayMap, I have a special code path for comparing keywords, but my generators for collection-check were previously using only integer keys. There was an off-by-one error in the transient map implementation [1], which was not present for non-keyword lookups.

I've taken a close look for other gaps in my test coverage, and can't find any. I don't think this substantively changes the risk of this patch (an updated version of which has been uploaded as 'unrolled-collections-2.diff'), but obviously where there's one bug, there may be others.

[1] https://github.com/ztellman/cambrian-collections/commit/eb7dfe6d12e6774512dbab22a148202052442c6d#diff-4bf78dbf5b453f84ed59795a3bffe5fcR559

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Zach Tellman added a comment - I found a bug! Like PersistentArrayMap, I have a special code path for comparing keywords, but my generators for collection-check were previously using only integer keys. There was an off-by-one error in the transient map implementation [1], which was not present for non-keyword lookups. I've taken a close look for other gaps in my test coverage, and can't find any. I don't think this substantively changes the risk of this patch (an updated version of which has been uploaded as 'unrolled-collections-2.diff'), but obviously where there's one bug, there may be others. [1] https://github.com/ztellman/cambrian-collections/commit/eb7dfe6d12e6774512dbab22a148202052442c6d#diff-4bf78dbf5b453f84ed59795a3bffe5fcR559
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Zach Tellman added a comment -

As an additional data point, I swapped out the data structures in the Cheshire JSON library. On the "no keyword-fn decode" benchmark, the current implementation takes 6us, with the unrolled data structures takes 4us, and with no data structures (just lexing the JSON via Jackson) takes 2us. Other benchmarks had similar results. So at least in this scenario, it halves the overhead.

Benchmarks can be run by cloning https://github.com/dakrone/cheshire, unrolled collections can be tested by using the 'unrolled-collections' branch. The pure lexing benchmark can be reproduced by messing around with the cheshire.parse namespace a bit.

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Zach Tellman added a comment - As an additional data point, I swapped out the data structures in the Cheshire JSON library. On the "no keyword-fn decode" benchmark, the current implementation takes 6us, with the unrolled data structures takes 4us, and with no data structures (just lexing the JSON via Jackson) takes 2us. Other benchmarks had similar results. So at least in this scenario, it halves the overhead. Benchmarks can be run by cloning https://github.com/dakrone/cheshire, unrolled collections can be tested by using the 'unrolled-collections' branch. The pure lexing benchmark can be reproduced by messing around with the cheshire.parse namespace a bit.
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Zach Tellman added a comment -

Is there no way to get this into 1.7? It's an awfully big win to push off for another year.

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Zach Tellman added a comment - Is there no way to get this into 1.7? It's an awfully big win to push off for another year.
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Alex Miller added a comment -

Hey Zach, it's definitely considered important but we have decided to drop almost everything not fully done for 1.7. Timeframe for following release is unknown, but certainly expected to be significantly less than a year.

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Alex Miller added a comment - Hey Zach, it's definitely considered important but we have decided to drop almost everything not fully done for 1.7. Timeframe for following release is unknown, but certainly expected to be significantly less than a year.
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John Szakmeister added a comment -

You are all free to determine the time table, but I thought I'd point out that Zach is not entirely off-base. Clojure 1.4.0 was released April 5th, 2012. Clojure 1.5.0 was released March 1st, 2013 with 1.6.0 showing up March 25th, 2014. So it appears that the current cadence is around a year.

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John Szakmeister added a comment - You are all free to determine the time table, but I thought I'd point out that Zach is not entirely off-base. Clojure 1.4.0 was released April 5th, 2012. Clojure 1.5.0 was released March 1st, 2013 with 1.6.0 showing up March 25th, 2014. So it appears that the current cadence is around a year.
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Alex Miller added a comment -

John, there is no point to comments like this. Let's please keep issue comments focused on the issue.

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Alex Miller added a comment - John, there is no point to comments like this. Let's please keep issue comments focused on the issue.
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Zach Tellman added a comment -

I did a small write-up on this patch which should help in the eventual code review: http://blog.factual.com/using-clojure-to-generate-java-to-reimplement-clojure

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Zach Tellman added a comment - I did a small write-up on this patch which should help in the eventual code review: http://blog.factual.com/using-clojure-to-generate-java-to-reimplement-clojure
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Zach Tellman added a comment -

Per my conversation with Alex at the Conj, here's a patch that only contains the unrolled vectors, and uses the more efficient constructor for PersistentVector when spilling over.

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Zach Tellman added a comment - Per my conversation with Alex at the Conj, here's a patch that only contains the unrolled vectors, and uses the more efficient constructor for PersistentVector when spilling over.
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Alex Miller added a comment -

Zach, I created a new placeholder for the map work at http://dev.clojure.org/jira/browse/CLJ-1610.

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Alex Miller added a comment - Zach, I created a new placeholder for the map work at http://dev.clojure.org/jira/browse/CLJ-1610.
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Jean Niklas L'orange added a comment -

It should probably be noted that core.rrb-vector will break for small vectors by this patch, as it peeks into the underlying structure. This will also break other libraries which peeks into the vector implementation internals, although I'm not aware of any other – certainly not any other contrib library.

Also, two comments on unrolled-vector.patch:

private transient boolean edit = true;
in the Transient class should probably be
private volatile boolean edit = true;
as transient means something entirely different in Java.

conj in the Transient implementation could invalidate itself without any problems (edit = false;) if it is converted into a TransientVector (i.e. spills over) – unless it has a notable overhead. The invalidation can prevent some subtle bugs related to erroneous transient usage.

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Jean Niklas L'orange added a comment - It should probably be noted that core.rrb-vector will break for small vectors by this patch, as it peeks into the underlying structure. This will also break other libraries which peeks into the vector implementation internals, although I'm not aware of any other – certainly not any other contrib library. Also, two comments on unrolled-vector.patch: private transient boolean edit = true; in the Transient class should probably be private volatile boolean edit = true; as transient means something entirely different in Java. conj in the Transient implementation could invalidate itself without any problems (edit = false;) if it is converted into a TransientVector (i.e. spills over) – unless it has a notable overhead. The invalidation can prevent some subtle bugs related to erroneous transient usage.
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Alex Miller added a comment -

Jean - understanding the scope of the impact will certainly be part of the integration process for this patch. I appreciate the heads-up. While we try to minimize breakage for things like this, it may be unavoidable for libraries that rely on implementation internals.

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Alex Miller added a comment - Jean - understanding the scope of the impact will certainly be part of the integration process for this patch. I appreciate the heads-up. While we try to minimize breakage for things like this, it may be unavoidable for libraries that rely on implementation internals.
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Michał Marczyk added a comment -

I'll add support for unrolled vectors to core.rrb-vector the moment they land on master. (Probably with some conditional compilation so as not to break compatibility with earlier versions of Clojure – we'll see when the time comes.)

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Michał Marczyk added a comment - I'll add support for unrolled vectors to core.rrb-vector the moment they land on master. (Probably with some conditional compilation so as not to break compatibility with earlier versions of Clojure – we'll see when the time comes.)
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Michał Marczyk added a comment - - edited

I should say that it'd be possible to add generic support for any "vector lookalikes" by pouring them into regular vectors in linear time. At first glance it seems to me that that'd be out of line with the basic promise of the library, but I'll give it some more thought before the changes actually land.

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Michał Marczyk added a comment - - edited I should say that it'd be possible to add generic support for any "vector lookalikes" by pouring them into regular vectors in linear time. At first glance it seems to me that that'd be out of line with the basic promise of the library, but I'll give it some more thought before the changes actually land.
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Zach Tellman added a comment -

Somewhat predictably, the day after I cut the previous patch, someone found an issue [1]. In short, my use of the ArrayChunk wrapper applied the offset twice.

This was not caught by collection-check, which has been updated to catch this particular failure. It was, however, uncovered by Michael Blume's attempts to merge the change into Clojure, which tripped a bunch of alarms in Clojure's test suite. My own attempt to do the same to "prove" that it worked was before I added in the chunked seq functionality, hence this issue persisting until now.

As always, there may be more issues lurking. I hope we can get as many eyeballs on the code between now and 1.8 as possible.

[1] https://github.com/ztellman/cambrian-collections/commit/2e70bbd14640b312db77590d8224e6ed0f535b43
[2] https://github.com/MichaelBlume/clojure/tree/test-vector

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Zach Tellman added a comment - Somewhat predictably, the day after I cut the previous patch, someone found an issue [1]. In short, my use of the ArrayChunk wrapper applied the offset twice. This was not caught by collection-check, which has been updated to catch this particular failure. It was, however, uncovered by Michael Blume's attempts to merge the change into Clojure, which tripped a bunch of alarms in Clojure's test suite. My own attempt to do the same to "prove" that it worked was before I added in the chunked seq functionality, hence this issue persisting until now. As always, there may be more issues lurking. I hope we can get as many eyeballs on the code between now and 1.8 as possible. [1] https://github.com/ztellman/cambrian-collections/commit/2e70bbd14640b312db77590d8224e6ed0f535b43 [2] https://github.com/MichaelBlume/clojure/tree/test-vector
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Zach Tellman added a comment -

As a companion to the performance analysis in the unrolled map issue, I've run the benchmarks and posted the results at https://gist.github.com/ztellman/10e8959501fb666dc35e. Some notable results:

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Zach Tellman added a comment - As a companion to the performance analysis in the unrolled map issue, I've run the benchmarks and posted the results at https://gist.github.com/ztellman/10e8959501fb666dc35e. Some notable results:
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Alex Miller added a comment -

Stu: I do not think this patch should be marked "screened" until the actual integration and build work (if the generator is integrated) has been completed.

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Alex Miller added a comment - Stu: I do not think this patch should be marked "screened" until the actual integration and build work (if the generator is integrated) has been completed.
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Alex Miller added a comment -

FYI, we have "reset" all big features for 1.8 for the moment (except the socket repl work). We may still include it - that determination will be made later.

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Alex Miller added a comment - FYI, we have "reset" all big features for 1.8 for the moment (except the socket repl work). We may still include it - that determination will be made later.
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Zach Tellman added a comment -

Okay, any idea when the determination will be made? I was excited that we seemed to be finally moving forward on this.

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Zach Tellman added a comment - Okay, any idea when the determination will be made? I was excited that we seemed to be finally moving forward on this.
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Alex Miller added a comment -

No, but it is now on my work list.

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Alex Miller added a comment - No, but it is now on my work list.
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Rich Hickey added a comment -

I wonder if all of the overriding of APersistentVector yields important benefits - e.g. iterator, hashcode etc.

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Rich Hickey added a comment - I wonder if all of the overriding of APersistentVector yields important benefits - e.g. iterator, hashcode etc.
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Zach Tellman added a comment -

In the case of hashcode, definitely: https://gist.github.com/ztellman/10e8959501fb666dc35e#file-gistfile1-txt-L1013-L1076. This was actually one of the original things I wanted to speed up.

In the case of the iterator, probably not. I'd be fine removing that.

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Zach Tellman added a comment - In the case of hashcode, definitely: https://gist.github.com/ztellman/10e8959501fb666dc35e#file-gistfile1-txt-L1013-L1076. This was actually one of the original things I wanted to speed up. In the case of the iterator, probably not. I'd be fine removing that.
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Zach Tellman added a comment -

So am I to infer from https://github.com/clojure/clojure/commit/36d665793b43f62cfd22354aced4c6892088abd6 that this issue is defunct? If so, I think there's a lot of improvements being left on the table for no particular reason.

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Zach Tellman added a comment - So am I to infer from https://github.com/clojure/clojure/commit/36d665793b43f62cfd22354aced4c6892088abd6 that this issue is defunct? If so, I think there's a lot of improvements being left on the table for no particular reason.
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Rich Hickey added a comment -

Yes, that commit covers this functionality. It takes a different approach from the patch in building up from a small core, and maximizing improvements to the bases rather than having a lot of redundant definitions per class. That also allowed for immediate integration without as much concern for correctness, as there is little new code. It also emphasizes the use case for tuples, e.g. small vectors used as values that won't be changed, thus de-emphasizing the 'mutable' functions. I disagree that many necessary improvements are being left out. The patch 'optimized' many things that don't matter. Further, there are not big improvements to the pervasive inlining. In addition, the commit includes the integration work at a fraction of the size of the patch. In all, it would have taken much more back and forth to get the patch to conform with this approach than to just get it all done, but I appreciate the inspiration and instigation - thanks!

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Rich Hickey added a comment - Yes, that commit covers this functionality. It takes a different approach from the patch in building up from a small core, and maximizing improvements to the bases rather than having a lot of redundant definitions per class. That also allowed for immediate integration without as much concern for correctness, as there is little new code. It also emphasizes the use case for tuples, e.g. small vectors used as values that won't be changed, thus de-emphasizing the 'mutable' functions. I disagree that many necessary improvements are being left out. The patch 'optimized' many things that don't matter. Further, there are not big improvements to the pervasive inlining. In addition, the commit includes the integration work at a fraction of the size of the patch. In all, it would have taken much more back and forth to get the patch to conform with this approach than to just get it all done, but I appreciate the inspiration and instigation - thanks!
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Rich Hickey added a comment -

That said, this commit need not be the last word - it can serve as a baseline for further optimization. But I'd rather that be driven by need. Clojure could become 10x as large optimizing things that don't matter.

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Rich Hickey added a comment - That said, this commit need not be the last word - it can serve as a baseline for further optimization. But I'd rather that be driven by need. Clojure could become 10x as large optimizing things that don't matter.
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Zach Tellman added a comment -

What is our reference for "relevant" performance? I (or anyone else) can provide microbenchmarks for calculating hashes or whatever else, but that doesn't prove that it's an important improvement. I previously provided benchmarks for JSON decoding in Cheshire, but that's just one of many possible real-world benchmarks. It might be useful to have an agreed-upon list of benchmarks that we can use when debating what is and isn't useful.

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Zach Tellman added a comment - What is our reference for "relevant" performance? I (or anyone else) can provide microbenchmarks for calculating hashes or whatever else, but that doesn't prove that it's an important improvement. I previously provided benchmarks for JSON decoding in Cheshire, but that's just one of many possible real-world benchmarks. It might be useful to have an agreed-upon list of benchmarks that we can use when debating what is and isn't useful.
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Mike Anderson added a comment - - edited

I was interested in this implementation so created a branch that integrates Zach's unrolled vectors on top of clojure 1.8.0-alpha2. I also simplified some of the code (I don't think the metadata handling or unrolled seqs are worthwhile, for example)

Github branch: https://github.com/mikera/clojure/tree/clj-1517

Then I ran a set of micro-benchmarks created by Peter Taoussanis

Results: https://gist.github.com/mikera/72a739c84dd52fa3b6d6

My findings from this testing:

  • Performance is comparable (within +/- 20%) on the majority of tests
  • Zach's approach is noticeably faster (by 70-150%) for 4 operations (reduce, mapv, into, equality)

My view is that these additional optimisations are worthwhile. In particular, I think that reduce and into are very important operations. I also care about mapv quite a lot for core.matrix (It's fundamental to many numerical operations on arrays implemented using Clojure vectors).

Happy to create a patch along these lines if it would be acceptable.

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Mike Anderson added a comment - - edited I was interested in this implementation so created a branch that integrates Zach's unrolled vectors on top of clojure 1.8.0-alpha2. I also simplified some of the code (I don't think the metadata handling or unrolled seqs are worthwhile, for example) Github branch: https://github.com/mikera/clojure/tree/clj-1517 Then I ran a set of micro-benchmarks created by Peter Taoussanis Results: https://gist.github.com/mikera/72a739c84dd52fa3b6d6 My findings from this testing:
  • Performance is comparable (within +/- 20%) on the majority of tests
  • Zach's approach is noticeably faster (by 70-150%) for 4 operations (reduce, mapv, into, equality)
My view is that these additional optimisations are worthwhile. In particular, I think that reduce and into are very important operations. I also care about mapv quite a lot for core.matrix (It's fundamental to many numerical operations on arrays implemented using Clojure vectors). Happy to create a patch along these lines if it would be acceptable.
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Zach Tellman added a comment -

The `reduce` improvements are likely due to the unrolled reduce and kvreduce impls, but the others are probably because of the unrolled transient implementation. The extra code required to add these would be pretty modest.

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Zach Tellman added a comment - The `reduce` improvements are likely due to the unrolled reduce and kvreduce impls, but the others are probably because of the unrolled transient implementation. The extra code required to add these would be pretty modest.
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Mike Anderson added a comment -

I actually condensed the code down to a single implementation for `Transient` and `TupleSeq`. I don't think these really need to be fully unrolled for each Tuple type. That helps by making the code even smaller (and probably also helps performance, given JVM inline caching etc.)

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Mike Anderson added a comment - I actually condensed the code down to a single implementation for `Transient` and `TupleSeq`. I don't think these really need to be fully unrolled for each Tuple type. That helps by making the code even smaller (and probably also helps performance, given JVM inline caching etc.)
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Peter Taoussanis added a comment -

Hey folks,

Silly question: is there actually a particular set of reference benchmarks that everyone's currently using to test the work on tuples? It took me a while to notice how bad the variance was with my own set of micro benchmarks.

Bumping all the run counts up till the noise starts ~dying down, I'm actually seeing numbers now that don't seem to agree with others here .

Google Docs link: https://docs.google.com/spreadsheets/d/1QHY3lehVF-aKrlOwDQfyDO5SLkGeb_uaj85NZ7tnuL0/edit?usp=sharing
gist with the benchmarks: https://gist.github.com/ptaoussanis/0a294809bc9075b6b02d

Thanks, cheers!

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Peter Taoussanis added a comment - Hey folks, Silly question: is there actually a particular set of reference benchmarks that everyone's currently using to test the work on tuples? It took me a while to notice how bad the variance was with my own set of micro benchmarks. Bumping all the run counts up till the noise starts ~dying down, I'm actually seeing numbers now that don't seem to agree with others here . Google Docs link: https://docs.google.com/spreadsheets/d/1QHY3lehVF-aKrlOwDQfyDO5SLkGeb_uaj85NZ7tnuL0/edit?usp=sharing gist with the benchmarks: https://gist.github.com/ptaoussanis/0a294809bc9075b6b02d Thanks, cheers!
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Zach Tellman added a comment -

Hey Peter, I can't reproduce your results, and some of them are so far off what I'd expect that I have to think there was some data gathering error. For instance, the assoc operation being slower is kind of inexplicable, considering the unrolled version doesn't do any copying, etc. Also, all of your numbers are significantly slower than the ones on my 4 year old laptop, which is also a bit strange.

Just to make sure that we're comparing apples to apples, I've adapted your benchmarks into something that pretty-prints the mean runtime and variance for 1.7, 1.8-alpha2, and Mike's 1517 fork. It can be found at https://github.com/ztellman/tuple-benchmark, and the results of a run at https://gist.github.com/ztellman/3701d965228fb9eda084.

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Zach Tellman added a comment - Hey Peter, I can't reproduce your results, and some of them are so far off what I'd expect that I have to think there was some data gathering error. For instance, the assoc operation being slower is kind of inexplicable, considering the unrolled version doesn't do any copying, etc. Also, all of your numbers are significantly slower than the ones on my 4 year old laptop, which is also a bit strange. Just to make sure that we're comparing apples to apples, I've adapted your benchmarks into something that pretty-prints the mean runtime and variance for 1.7, 1.8-alpha2, and Mike's 1517 fork. It can be found at https://github.com/ztellman/tuple-benchmark, and the results of a run at https://gist.github.com/ztellman/3701d965228fb9eda084.
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Mike Anderson added a comment -

Hey Zach just looked at your benchmarks and they are definitely more consistent with what I am seeing. The overall nanosecond timings look about right from my experience with similar code (e.g. working with small vectors in vectorz-clj).

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Mike Anderson added a comment - Hey Zach just looked at your benchmarks and they are definitely more consistent with what I am seeing. The overall nanosecond timings look about right from my experience with similar code (e.g. working with small vectors in vectorz-clj).
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Peter Taoussanis added a comment -

Hi Zach, thanks for that!

Have updated the results -
Gist: https://gist.github.com/ptaoussanis/0a294809bc9075b6b02d
Google docs: https://goo.gl/khgT83

Note that I've added an extra sheet/tab to the Google doc for your own numbers at https://gist.github.com/ztellman/3701d965228fb9eda084.

Am still struggling to produce results that show any consistent+significant post-JIT benefit to either of the tuple implementations against the micro benchmarks and one larger small-vec-heavy system I had handy.

It's looking to me like it's maybe possible that the JIT's actually optimising away most of the non-tuple inefficiencies in practice?

Of course it's very possible that my results are off, or my expectations wrong. The numbers have been difficult to pin down.

It definitely helped to have a standardised reference micro benchmark to work against (https://github.com/ztellman/tuple-benchmark). Could I perhaps suggest a similar reference macro benchmark (maybe something from core.matrix, Mike?)

Might also be a good idea to define a worthwhile target performance delta for ops like these that run in the nanosecond range (or for the larger reference macro benchmark)?

Just some thoughts from someone passing through in case they're at all useful; know many of you have been deeply involved in this for some time so please feel free to ignore any input that's not helpful

Appreciate all the efforts, cheers!

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Peter Taoussanis added a comment - Hi Zach, thanks for that! Have updated the results - Gist: https://gist.github.com/ptaoussanis/0a294809bc9075b6b02d Google docs: https://goo.gl/khgT83 Note that I've added an extra sheet/tab to the Google doc for your own numbers at https://gist.github.com/ztellman/3701d965228fb9eda084. Am still struggling to produce results that show any consistent+significant post-JIT benefit to either of the tuple implementations against the micro benchmarks and one larger small-vec-heavy system I had handy. It's looking to me like it's maybe possible that the JIT's actually optimising away most of the non-tuple inefficiencies in practice? Of course it's very possible that my results are off, or my expectations wrong. The numbers have been difficult to pin down. It definitely helped to have a standardised reference micro benchmark to work against (https://github.com/ztellman/tuple-benchmark). Could I perhaps suggest a similar reference macro benchmark (maybe something from core.matrix, Mike?) Might also be a good idea to define a worthwhile target performance delta for ops like these that run in the nanosecond range (or for the larger reference macro benchmark)? Just some thoughts from someone passing through in case they're at all useful; know many of you have been deeply involved in this for some time so please feel free to ignore any input that's not helpful Appreciate all the efforts, cheers!
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Rich Hickey added a comment -

I think everyone should back off on their enthusiasm for this approach. After much analysis, I am seeing definite negative impacts to tuples, especially the multiple class approach proposed by Zach. What happens in real code is that the many tuple classes cause call sites that see different sized vectors to become megamorphic, and nothing gets adequately optimized. In particular, call sites that will see tuple-sized and large vectors (i.e. a lot of library code) will optimize differently depending upon which they see often first. So, if you run your first tight loop on vector code that sees tuples, that code could later do much worse (50-100%) on large vectors than before the tuples patch was in place. Being much slower on large collections is a poor tradeoff for being slightly faster on small ones.

Certainly different tuple classes for arity 0-6 is a dead idea. You get as good or better optimization (at some cost in size) from a single class e.g. one with four fields, covering sizes 0-4. I have a working version of this in a local branch. It is better in that sites that include pvectors are only bi-morphic, but I am still somewhat skittish given what I've seen.

The other takeaway is that the micro benchmarks are nearly worthless for detecting these issues.

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Rich Hickey added a comment - I think everyone should back off on their enthusiasm for this approach. After much analysis, I am seeing definite negative impacts to tuples, especially the multiple class approach proposed by Zach. What happens in real code is that the many tuple classes cause call sites that see different sized vectors to become megamorphic, and nothing gets adequately optimized. In particular, call sites that will see tuple-sized and large vectors (i.e. a lot of library code) will optimize differently depending upon which they see often first. So, if you run your first tight loop on vector code that sees tuples, that code could later do much worse (50-100%) on large vectors than before the tuples patch was in place. Being much slower on large collections is a poor tradeoff for being slightly faster on small ones. Certainly different tuple classes for arity 0-6 is a dead idea. You get as good or better optimization (at some cost in size) from a single class e.g. one with four fields, covering sizes 0-4. I have a working version of this in a local branch. It is better in that sites that include pvectors are only bi-morphic, but I am still somewhat skittish given what I've seen. The other takeaway is that the micro benchmarks are nearly worthless for detecting these issues.
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Zach Tellman added a comment -

I'm pretty sure that all of my real-world applications of the tuples (via clj-tuple) have been fixed cardinality, and wouldn't have surfaced any such issue. Thanks for putting the idea through its paces.

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Zach Tellman added a comment - I'm pretty sure that all of my real-world applications of the tuples (via clj-tuple) have been fixed cardinality, and wouldn't have surfaced any such issue. Thanks for putting the idea through its paces.
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Mike Anderson added a comment -

Rich these are good insights - do you have a benchmark that you are using as representative of real world code?

I agree that it is great if we can avoid call sites becoming megamorphic, though I also believe the ship has sailed on that one already when you consider the multiple types of IPersistentVector that already exist (MapEntry, PersistentVector, SubVector plus any library-defined IPersistentVector instances such as clojure.core.rrb-vector). As a consequence, the JVM is usually not going to be able to prove that a specific IPersistentVector interface invocation is monomorphic, which is when the really big optimisations happen.

In most of the real world code that I've been working with, the same size/type of vector gets used repeatedly (Examples: iterating over map entries, working with a sequence of size-N vectors), so in such cases we should be able to rely on the polymorphic inline cache doing the right thing.

The idea of a single Tuple class for sizes 0-4 is interesting, though I can't help thinking that a lot of the performance gain from this may stem from the fact that a lot of code does stuff like (reduce conj [] .....) or the transient equivalent which is a particularly bad use case for Tuples, at least from the call site caching perspective. There may be a better way to optimise such cases rather than simply trying to make Tuples faster.... e.g. calling asTransient() on a Tuple0 could perhaps switch straight into the PersistentVector implementation.

Show
Mike Anderson added a comment - Rich these are good insights - do you have a benchmark that you are using as representative of real world code? I agree that it is great if we can avoid call sites becoming megamorphic, though I also believe the ship has sailed on that one already when you consider the multiple types of IPersistentVector that already exist (MapEntry, PersistentVector, SubVector plus any library-defined IPersistentVector instances such as clojure.core.rrb-vector). As a consequence, the JVM is usually not going to be able to prove that a specific IPersistentVector interface invocation is monomorphic, which is when the really big optimisations happen. In most of the real world code that I've been working with, the same size/type of vector gets used repeatedly (Examples: iterating over map entries, working with a sequence of size-N vectors), so in such cases we should be able to rely on the polymorphic inline cache doing the right thing. The idea of a single Tuple class for sizes 0-4 is interesting, though I can't help thinking that a lot of the performance gain from this may stem from the fact that a lot of code does stuff like (reduce conj [] .....) or the transient equivalent which is a particularly bad use case for Tuples, at least from the call site caching perspective. There may be a better way to optimise such cases rather than simply trying to make Tuples faster.... e.g. calling asTransient() on a Tuple0 could perhaps switch straight into the PersistentVector implementation.

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