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Learn Ractor

Learn Ractor

RubyKaigi 2023 Day2

seki at druby.org

May 12, 2023
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  1. Learn Ractor


    [email protected]

    View Slide

  2. E-book version still available
    The dRuby Book is out of print, but...
    スタンプラリー対象外です。Not eligible for stamp rally
    2

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  3. Agenda
    Concurrency is everywhere


    Introduction to Ractor


    case study
    3

    View Slide

  4. Agenda
    Concurrency is everywhere


    About me


    Concurrency is everywhere


    Introduction to Ractor


    case study
    4

    View Slide

  5. About me
    Masatoshi Seki, Ruby Core Committer (dRuby, Rinda, ERB), Programmer


    I started programming 40 years ago


    writing complex embedded systems for 30 years


    system with concurrency
    40年くらいコード書いてて、30年くらいは複雑なシステム書いてるよ
    5

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  6. Complex embedded systems
    dRuby
    in the real-world embedded systems.
    ΩϠϊϯϝσΟΧϧγεςϜζגࣜձࣾ
    ҩ༻ίϯϙʔωϯτٕज़։ൃηϯλʔɹؔকढ़ɹԂ઒ཾ໵
    4FQ ɹ
    - ref. RubyKaigi takeout 2021
    ミニマム構成の製品を使って若者をRubyistにした事例発表
    About us
    Masatoshi Seki, Ruby Core Committer (dRuby, Rinda, ERB), Programmer
    Tatsuya Sonokawa, Programmer
    Miwa Fukaya, Tochigi RubyKaigi Content Committee Chair, Software
    tester
    ਓؒυοΫޙͷ࠶ݕࠪͰࡱͬͯ΋ΒͬͨࣸਅͰ͢ɻେਓͳͷͰɻ
    9
    404

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  7. Complex embedded systems
    dRuby
    in the real-world embedded systems.
    ΩϠϊϯϝσΟΧϧγεςϜζגࣜձࣾ
    ҩ༻ίϯϙʔωϯτٕज़։ൃηϯλʔɹؔকढ़ɹԂ઒ཾ໵
    4FQ ɹ
    - ref. RubyKaigi takeout 2021
    X-ray CT image of my gallstones
    About us
    Masatoshi Seki, Ruby Core Committer (dRuby, Rinda, ERB), Programmer
    Tatsuya Sonokawa, Programmer
    Miwa Fukaya, Tochigi RubyKaigi Content Committee Chair, Software
    tester
    ਓؒυοΫޙͷ࠶ݕࠪͰࡱͬͯ΋ΒͬͨࣸਅͰ͢ɻେਓͳͷͰɻ
    9
    404
    Gallstones

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  8. Complex embedded systems
    Lots of processes, and explicit and implicit IPC
    無数のプロセスとプロセス間通信がある。明示的だったり暗黙的だったりするよ
    8

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  9. Concurrency is everywhere
    Concurrency exists at various levels of abstraction
    そこら中で時間的にオーバーラップして実行してる。ということはつまり...
    9

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  10. Concurrency is everywhere
    Sequential execution is a special case
    むしろ、逐次実行は特殊なケースである
    10

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  11. RubyKaigi takeout 2020
    Rinda
    in the real-world embedded systems.
    [email protected]
    First printing from 2005 (Still available)
    dRuby
    ʹΑΔ
    ؔকढ़ஶ
    ෼ࢄ
    ɾ
    Web
    ϓϩάϥϛϯά
    dRubyとRindaによる天文台のロボット望遠鏡(ガチ)の話
    - 🤖🔭

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  12. RubyKaigi takeout 2020
    自律して動作する装置/センサー群を統合するシステム。抽象度が高い感じする
    - Linda coordination model
    software block diagram
    1
    psz117
    X
    f/2.5
    20151207_yanagisawa.pdf ΑΓ
    OAOWFC䛾እほ
    2015/12/07 奛㷔塭伖㈨埻WS 16
    X

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  13. OpenMP
    比較的低いレベルの例
    13
    int main(int argc, char *argv[])


    {


    int i;


    #pragma omp parallel for


    for (i = 0; i < 10000; ++i)


    {


    /* ... */


    }


    return 0;


    }

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  14. OpenMP
    14
    0...10000
    int main(int argc, char *argv[])


    {


    int i;


    #pragma omp parallel for


    for (i = 0; i < 10000; ++i)


    {


    /* ... */


    }


    return 0;


    }

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  15. OpenMP
    15
    0...2000
    2000...4000
    4000...6000
    6000...8000
    8000...10000

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  16. Agenda
    Concurrency is everywhere


    Introduction to Ractor


    case study
    16

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  17. Ractor
    parallel


    avoid race condition


    isolation
    本当のスレッドと保護されたメモリー(オブジェクト)で
    17

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  18. Thread to Ractor
    単純なケースではThreadのように使えるよ
    18
    >> t = Thread.new(*arg) {|*arg| ... }


    ...


    >> t.value
    >> r = Ractor.new(*arg) {|*arg| ... }


    ...


    >> r.take

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  19. Thread to Ractor
    Ractorはtakeで値を取り出すのがちがうよ。
    19
    >> t = Thread.new(25) {|n| (1..n).inject(1, :*)}


    ...


    >> t.value


    => 15511210043330985984000000
    >> r = Ractor.new(25) {|n| (1..n).inject(1, :*)}


    ...


    >> r.take


    => 15511210043330985984000000

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  20. avoid race conditions
    isolation


    block scope


    global variables


    shareable object
    競合状態を嫌ってRactor間をまたぐオブジェクトに強い制限がある
    20

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  21. isolation
    block scope
    Procだけど、外側の変数にはアクセスできないよ
    21
    >> hash = {'foo'=>'bar'}


    >> r = Ractor.new {hash['foo']}


    :271:in `new': can not isolate a Proc
    because it accesses outer variables (i). (ArgumentError)

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  22. isolation
    global variable
    グローバル変数にはアクセスできないぞ
    22
    >> $foo = 1


    >> r = Ractor.new {$foo}


    # terminated with exception
    (report_on_exception is true):


    (irb):11:in `block in ': can not access
    global variables $foo from non-main Ractors
    (Ractor::IsolationError)

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  23. pass by value
    deep copy
    引数経由して渡せるよ。でもRactorをまたぐときに複製になる。dRubyみたいだ
    23
    >> hash = {'foo'=>'bar'}


    >> r = Ractor.new(hash) {|it| it['foo'] = 'baz'}


    ...


    >> r.take


    => "baz"


    >> hash


    => {"foo"=>"bar"}


    >> r = Ractor.new(hash) {|it| it.object_id}


    ...


    >> [hash.object_id, r.take]


    => [2070360, 2006160]

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  24. shareable
    Ractor.make_shareable
    make_shareableするとRactor間で共有されるぞ!でもfreezeされてしまうのだ
    DRbUndumpedみたい
    24
    >> hash = {'foo'=>'bar'}


    >> Ractor.make_shareable(hash)


    >> Ractor.new(hash) {|it| it.object_id}.take


    => 221520


    >> hash.object_id


    => 221520


    >> hash['foo'] = 'baz'


    (irb):16:in `': can't modify frozen Hash:
    {"foo"=>"bar"} (FrozenError)

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  25. can't make shareable
    Queue, Monitor, TupleSpace
    同期プリミティブは渡せない
    25
    >> Ractor.make_shareable(Queue.new)


    :820:in `make_shareable': can not make
    shareable object for #
    (Ractor::Error)


    >> Ractor.make_shareable(Monitor.new)


    :820:in `make_shareable': can not make
    shareable object for #
    (Ractor::Error)


    >> Ractor.make_shareable(Rinda::TupleSpace.new)


    :820:in `make_shareable': can not make
    shareable object for #
    (Ractor::Error)

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  26. shareable object
    immutable


    class/module


    special object


    Ractor


    ENV
    26

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  27. Ractor has two queues.
    takeしか使わなかった...
    27
    incoming queue


    Ractor#send / Ractor.receive


    outgoing queue


    Ractor.yield / Ractor#take

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  28. avoid race conditions
    Using Ractor is easy, but writing for Ractor is hard.


    straitjacket
    拘束衣。オブジェクト指向プログラミングのように書くのはたいへんだと思う
    28

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  29. PG
    Thought it wouldn't work, but it didn't
    動かないと思うけど動かないもの
    29
    >> conn = PG.connect("postgres:///masaki")


    >> Ractor.make_shareable(conn)


    :820:in `make_shareable': can not make shareable
    object for # (Ractor::Error)


    >> Ractor.new { PG.connect("postgres:///masaki").exec("select 1") }


    # terminated with exception
    (report_on_exception is true):


    /opt/homebrew/lib/ruby/gems/3.2.0/gems/pg-1.4.6/lib/pg/
    connection.rb:70:in `conninfo_parse': ractor unsafe method called
    from not main ractor (Ractor::UnsafeError)


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  30. pp
    Thought it would work, but it didn't
    動きそうで動かなかったもの。私の使い方が悪いのかもしれないです。
    30
    >> Ractor.new {pp 1}


    # terminated with exception
    (report_on_exception is true):


    core_ext/kernel_require.rb>:40:in `require': can not access
    non-shareable objects in constant
    Kernel::RUBYGEMS_ACTIVATION_MONITOR by non-main ractor.
    (Ractor::IsolationError)

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  31. a situation suitable for Ractor
    Accelerate loops with parallelism


    numeric calculation


    in-memory database
    さっき説明したOpenMP見たい領域が得意そう。
    31

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  32. There is an in-memory search engine
    用意しておきました!
    32

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  33. Agenda
    Concurrency is everywhere


    Introduction to Ractor


    case study
    33

    View Slide

  34. RubyKaigi 2022
    Create my own search engine.
    [email protected]
    ࣗ෼༻ͷݕࡧγεςϜΛ࡞Δ࿩
    - There is an in-memory search engine
    dRubyについて話せなかったが、ポケモンカードの検索エンジンの話

    View Slide

  35. Pokémon
    Trainer Energy
    Pokémon TCG
    Build a deck with 60 cards

    View Slide

  36. Pokémon TCG similar deck search
    Heroku → Oracle Cloud Infrastructure


    VM : Arm x 4 core


    do not use containers
    OCIに移行したので、コンテナを使うのもやめた
    36
    2023

    View Slide

  37. Weekly deck trend analysis
    新機能!
    37
    case 1

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  38. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    38

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  39. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    39

    View Slide

  40. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    m
    C2
    40

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  41. Hierarchical clustering


    Calculate distances for all combinations → mCn
    m
    C2
    41
    irb(main):001:0>
    Array.new(6).combination(2).size


    => 15


    irb(main):002:0>
    Array.new(900).combination(2).size


    => 404550

    View Slide

  42. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    42

    View Slide

  43. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    43

    View Slide

  44. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    44

    View Slide

  45. Hierarchical clustering


    Calculate distances for all combinations


    Merge closest node into cluster
    45

    View Slide

  46. Hierarchical clustering
    0.0
    0.1
    0.2
    0.3
    0.4
    0.5
    0.6
    0.7
    0.8
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    nLgNgL-jK0iJz-QQnnng

    View Slide

  47. Hierarchical clustering
    0.0
    0.1
    0.2
    0.3
    0.4
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    kfFvFv-8pxrcr-Fkkkkk
    6ggnnL-3n7MKb-NnnnHQ
    4YD8c8-udiY9H-K8c88c
    9HPnQn-T5VVou-NgHnnn
    c8axx8-qVDXly-cxYcYD
    nLnLgN-L3ErQj-Q9Hgng
    66n6Ln-irXTVx-LngnQg
    a8YD88-7wjh7o-8ccca8
    nggngg-6QpEdq-nQiHLN
    8Y8aD8-PxTvBD-c88xJ8
    ypSMpM-ywy4eI-yy2pyS
    9Qgn6N-riIe1q-nnngig
    8DGcx8-lqWmWH-acDcY4
    F5V5kV-F54puu-VvkdVv
    VkFkvk-Hl7mGl-fkfk5k
    fVfkdv-RZJBcC-vFkVFb
    kvFFdF-UpHnMV-5k5kkk
    gnnN9i-E039Jw-nLgggQ
    Ff5vkk-1HlTBb-F5kkkF
    HgLgHg-4OjrcS-nQnnnL
    SpypEp-1S1DBJ-y3M2yS
    nnHLnL-oJk1fq-QiLgLg

    View Slide

  48. Hierarchical clustering
    0.6
    0.7
    0.8
    ↑ Height

    View Slide

  49. Weekly deck trend analysis
    適当な高さで切るとデッキアーキテクチャごとのクラスタになる!
    49

    View Slide

  50. Pokémon TCG similar deck search 2023
    50
    card normalize map


    data/uniq_*.txt
    new deck
    metadata
    known deck


    sqlite3
    sqlite3
    My MacBook
    App
    deck

    similarity
    Search
    Initialize
    Crowler
    Web UI
    @deck


    @idf


    @norm
    @name


    @id_norm
    Make Vector
    Update Deck
    Build page
    periodic task
    検索エンジンを流用して

    View Slide

  51. Weekly deck trend analysis
    \
    51
    card normalize map


    data/uniq_*.txt
    new decks
    known deck


    sqlite3
    sqlite3 Rakefile
    deck

    similarity
    Initialize
    Crowler
    Clustering
    @deck


    @idf


    @norm
    @name


    @id_norm
    Make Vector
    hierarchical


    clustering
    クラスタリング
    n weeks

    View Slide

  52. Weekly deck trend analysis
    \
    52
    card normalize map


    data/uniq_*.txt
    new decks
    known deck


    sqlite3
    sqlite3 Rakefile
    deck

    similarity
    Initialize
    Crowler
    Clustering
    @deck


    @idf


    @norm
    @name


    @id_norm
    Make Vector
    hierarchical


    clustering
    クラスタリング
    n weeks

    View Slide

  53. Ractor-ize
    Using Ractor is easy, but writing for Ractor is hard.


    Refactoring was very hard


    Move calculation part to mix-in
    めちゃめちゃリファクタリングした...
    53

    View Slide

  54. n週間分のRactorをつくるよ
    54
    def weekly_analyze(world, deck_and_date)


    origin = Date.parse('2022-10-07') # ༵ۚ೔࢝·Γ


    weekly = deck_and_date.sort_by {|x| x[1]}.chunk {|x|


    (Date.parse(x[1]) - origin).to_i / 7


    }


    result = weekly.map {|_, decks|


    {


    'range' => Range.new(decks[0][1], decks[-1][1]),


    'deck_count' => decks.size,


    'cluster' => Cluster.new(world, decks.map {|x, d| x})


    }


    }


    result.each do |x|


    x['cluster'].join


    end


    result


    end
    Ractor.new
    take

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  55. 55
    2022-10-07 .. 2022-10-13
    2022-10-14 .. 2022-10-20
    2022-10-21 .. 2022-10-27
    2022-11-07 .. 2022-11-03
    2022-11-04 ... 2022-11-11

    View Slide

  56. map { Ractor.new }.map {|r| r.take }
    Ractor起動!
    56
    >> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
    [ ]

    View Slide

  57. map { Ractor.new }.map {|r| r.take }
    終わったRactorもいるし、動いてるRactorもいるぞ
    57
    >> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
    [ ]

    View Slide

  58. map { Ractor.new }.map {|r| r.take }
    気にせずtakeで待ち合わせ!
    58
    >> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
    [ ]
    >> result.map {|r| r.take}

    View Slide

  59. map { Ractor.new }.map {|r| r.take }
    Linda本でもこういうイディオムあったような気がする
    59
    >> result = weekly.map {|w| Ractor.new(w) {|it| do_cluster(it)}}
    [ ]
    >> result.map {|r| r.take}
    [ ]

    View Slide

  60. 28 Ractors, 8 cores (6 performance and 2 efficiency)
    40%くらい速くなった。デッキ数が週ごとにばらつくため、多い週に律速した?
    60
    w/o Ractor


    % /usr/bin/time -l rake


    51.03 real 50.28 user 0.55 sys


    Ractor


    % /usr/bin/time -l rake


    31.84 real 83.81 user 89.54 sys

    View Slide

  61. Pokémon TCG similar deck search
    61
    card normalize map


    data/uniq_*.txt
    new deck
    metadata
    known deck


    sqlite3
    sqlite3
    My MacBook
    App
    deck

    similarity
    Search
    Initialize
    Crowler
    Web UI
    @deck


    @idf


    @norm
    @name


    @id_norm
    Make Vector
    Update Deck
    Build page
    periodic task
    検索の高速化
    case 2
    n decks

    View Slide

  62. Pokémon TCG similar deck search
    62
    card normalize map


    data/uniq_*.txt
    new deck
    metadata
    known deck


    sqlite3
    sqlite3
    My MacBook
    App
    deck

    similarity
    Search
    Initialize
    Crowler
    Web UI
    @deck


    @idf


    @norm
    @name


    @id_norm
    Make Vector
    Update Deck
    Build page
    periodic task
    検索の高速化
    n decks

    View Slide

  63. Ractor-ize
    Using Ractor is easy, but writing for Ractor is hard.


    Refactoring was very hard


    Add decks while searching


    I want an appendable shared object
    追記可能な共有可能なオブジェクトがほしいです!
    63

    View Slide

  64. 変化がない領域だけRactorで検索する
    64
    if USING_RACTOR


    top = (_search_by_deck_core(@added_deck, v, n) + search_using_ractor(v, n)).max(n)


    else


    top = _search_by_deck_core(@deck, v, n)


    end


    search added decks


    (w/o Ractor)
    search static decks


    (Ractor)

    View Slide

  65. 大きな配列を作るのを嫌ってイテレータを作った
    65
    def each_in(range)


    return enum_for(__method__, range) unless block_given?


    range.each {|x| yield(@deck[x])}


    end


    def _search_by_deck(v, n)


    (([email protected]).step(@deck.size / @nproc) + [@deck.size]).each_cons(2).map {|s, e|


    Ractor.new(self, each_in(s...e), v, n) {|world, sub_decks, v, n|


    world._search_by_deck_core(sub_decks, v, n)


    }


    }.map {|r| r.take}.sum([]).max(n)


    end


    enum of subrange
    subrange iterator


    View Slide

  66. 10Ractors, 46400 decks
    66
    0...5811
    5911...11622
    11622...17433
    17433...23244
    23244...29055

    View Slide

  67. map { Ractor.new }.map {|r| r.take }.sum.max
    max(n)を集めてmax(n)
    67
    .map {|r| r.take}
    e.map {Ractor.new(r) {|it| it.max(n)}}
    [ ]
    [ ]
    .sum([]).max(n)

    View Slide

  68. 10Ractors, 46400 decks
    だいぶ速くなった!
    68
    M1 Pro MacBook


    w/o Ractor


    0.272264 sec


    Ractor


    0.070405 sec
    OCI ARM4 core


    w/o Ractor


    0.369918735 sec


    Ractor


    0.099914693 sec

    View Slide

  69. Peak performance issue
    When tweeting, search slows down


    Multiple requests generate a lot of Ractor
    宣伝ツイートすると検索がめっちゃ遅くなる
    69

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  70. Agenda
    Concurrency is everywhere


    Introduction to Ractor


    case study
    70

    View Slide

  71. Pokémon
    Trainer Energy
    Pokémon TCG
    Build a deck with 60 cards

    View Slide