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ࡾ୐༔հ / Pepabo R&D Institute, GMO Pepabo, Inc. 2018.11.25 Go Conference 2018 Autumn ϑΟʔυόοΫ੍ޚʹΑΔ Goroutineىಈ਺ͷ࠷దԽ

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ϓϦϯγύϧΤϯδχΞ ࡾ୐༔հ!NPOPDISPNFHBOF (.0ϖύϘגࣜձࣾϖύϘݚڀॴ IUUQTCMPHNPOPDISPNFHBOFDPN

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3

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1. ฒߦॲཧͱνϡʔχϯάʹ͓͚Δ՝୊ 2. KaburayaΞʔΩςΫνϟ 3. Goݴޠ΁ͷద༻ 4. ධՁ 5. ·ͱΊ 4 ໨࣍

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1. ฒߦॲཧͱνϡʔχϯάʹ͓͚Δ՝୊

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ΦϨ͸ΰϧʔνϯͰߦ͘ʂ

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• ϚϧνίΞ࣌୅ʹ͓͍ͯɼ୯ҰͷϚγϯͰॲཧੑೳΛ࠷େԽ͢ΔͨΊॲཧͷฒ ߦԽ͕ߦΘΕΔɽ • ࠷దͳฒߦ਺ΛٻΊ͍ͨɽ • ϊϯϒϩοΩϯάͳॲཧํࣜͰ͸࠷దͳฒߦ਺͸CPUίΞ਺ͱ౳͍͠ • ϒϩοΩϯά͕Ұ෦෼Ͱ΋ൃੜ͢Δ৔߹͸CPUίΞ਺Ҏ্͕࠷దʹͳΔ৔ ߹͕ଟ͍ 7 ฒߦॲཧͱ࠷దͳฒߦ਺

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• ϒϩοΩϯά͕ൃੜ͢Δঢ়گʹ͓͍ͯ͸ɼ࠷దͳฒߦ਺ͷܾఆ͸ґવͱͯ͠։ ൃऀͷܦݧͱ஍ಓͳνϡʔχϯάʹґଘ • nginx, unicornͷworker_processesɼSidekiqͷconcurrencyɼGoroutineͷ ىಈ਺ͳͲ • ࣮ߦ͞ΕΔॲཧͷಛੑͱϥϯλΠϜ΍εέδϡʔϦϯάͷಛੑΛߟྀͨ͠ νϡʔχϯά͕ඞཁ • ͜ΕΒΛશͯߟྀͨ͠ීวతͳνϡʔχϯάख๏ͷൃݟ͸ࠔ೉ 8 ࠷దͳฒߦ਺ΛٻΊΔ

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• ద੾ͳࢦඪ͕બఆͰ͖Ε͹୯७ͳࢦඪͰ΋ޮՌ͕͋Δ • Ծ૝αʔόͷ༧ଌతΦʔτεέʔϦϯά (*1)Ͱ͸ɼΞϓϦέʔγϣϯॲཧ಺ ༰Ͱ͸ͳ͘ɼӡ༻ܦݧ͔Βಘͨαʔό୯ҐͷεϧʔϓοτΛࢦඪͱͨ͠ɽ • ʮશࣗಈύϥϝʔλνϡʔχϯά͞Μʯ(*2)Ͱ͸ϝτϦΫεΛҰ఺ʹߜΓε ϧʔϓοτ͕࠷େԽ͢Δ஋Λ୳ࡧతʹٻΊͨɽ 9 ैདྷख๏ *1. ࡾ୐ ༔հ, দຊ ྄հ, ྗ෢ ݈࣍, ܀ྛ ݈ଠ࿠, ΞΫηεස౓༧ଌʹجͮ͘Ծ૝αʔόͷܭըతΦʔτεέʔϦϯά, FIT 2018 ୈ17ճ৘ใ Պֶٕज़ϑΥʔϥϜ, CL-002, Sep 2018. *2. https://github.com/mirakui/tuningsan

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• ༧ଌతɼ୳ࡧతͳํࣜ͸WebαʔόʔϓϩηεͷΑ͏ʹ௕ظՔಈ͢Δ৔߹ʹ ޮՌత͕ͩɼCLIͷΑ͏ͳ୹ظՔಈͰଈ࣌݁Ռ͕ٻΊΒΕΔ৔߹ʹద༻͕೉͠ ͍ 10 ैདྷख๏ • ࢦඪͷݶఆʹՃ͑ɼ൓Ԡత͔ͭޡ͕ࠩগͳ͘ࢦඪ΁௥ै͢Δख๏͕ٻΊΒΕΔ

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2. KaburayaΞʔΩςΫνϟ

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• ॲཧ΍ϥϯλΠϜͷಛੑʹґଘͤͣʹɼϚγϯͷෛՙʹԠͯ͡
 ൓Ԡత͔ͭܧଓతʹɼฒߦ਺Λ࠷దԽ͢Δ͜ͱ͸Մೳ͔ʁ 12 ຊݚڀͷʮ໰͍ʯ

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• ॲཧ΍ϥϯλΠϜͷಛੑʹґଘͤͣʹɼϚγϯͷෛՙʹԠͯ͡൓Ԡత͔ͭܧଓ తʹɼฒߦ਺Λ࠷దԽ͢Δ • 1. ॲཧ΍ϥϯλΠϜΛϒϥοΫϘοΫεͱͯ͠ѻ͍ɼܧଓతʹಈ࡞Λ੍ޚ͢ Δ࢓૊ΈͰ͋ΔϑΟʔυόοΫ੍ޚΛ༻͍ͯ࠷దͳฒߦ਺ΛٻΊΔ • 2. ฒߦ਺ͷ੍ޚʹηϚϑΥΛ༻͍Δ • Kaburaya is “ధ໼”. It is japanese arrow with a whistle. 13 Kaburaya ΞʔΩςΫνϟ

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• ໨ඪ஋ͱग़ྗͷϑΟʔυόοΫͷޡࠩΛ༻͍ͯγεςϜͷೖྗΛܾఆ͠ɼग़ྗ Λ໨ඪ஋ʹ͚ۙͮΔ • ภࠩΛॲཧ͢Δ੍ޚثɼͦͯ͠ೖྗͱग़ྗʹԿΛ༻͍Δ͔ͷઃܭ͕ॏཁ 14 ϑΟʔυόοΫ੍ޚ ੍ޚث γεςϜ ೖྗ ग़ྗ ภࠩ ϑΟʔυόοΫ ໨ඪ஋

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Slide 16

Slide 16 text

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 e 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1. ॳظ஋͔ΒϫʔΧʔ਺Λม͑ͳ͍FixController (ൺֱ༻) 2. CPU࢖༻཰100%Λ໨ඪ஋ͱ͠ɼෆ଍ͨ͠৔߹͸ workerΛ1ͣͭ૿Ճͤ͞Δ SimpleController 3. CPU࢖༻཰100%Λ໨ඪͱ͠ɼ໨ඪͱͷࠩ෼ͷKഒΛՃ͑Δ PController(ൺྫ੍ޚ) 4. 3.ͷCPU࢖༻཰ͷ໨ඪ஋Λ90%ͱͨ͠΋ͷ 5. ௚ۙ3؍ଌ఺ͷฏۉΛ໨ඪ஋ͱͯ͠3؍ଌ͝ͱʹPControllerͷ໨ඪ஋ΛมԽͤ͞ΔDynamicController 6. ҰఆظؒͷCPU࢖༻཰ͷඪ४ภࠩΛͱͬͯͦΕ͕Ұఆͷ஋ҎԼͩͬͨΒ҆ఆͨ͠ͱΈͳͯ͠ɼworkerΛݮΒ͍ͯ͠ ͘StabilityController 7. CPU࢖༻཰Ͱ͸ͳͯ͘CPU࢖༻཰ͷมԽ཰Λݩʹ੍ޚ͢ΔRateController 8. 5.ͷDynamicControllerΛݩʹఆظͰ͸ͳ͘େ͖ͳมಈ͝ͱʹ໨ඪ஋Λݟ௚͢ํࣜ 9. 8.ͷDynamicControllerΛݩʹఆৗ࣌ͷෆཁͳworkerΛ࡟ݮ͢Δํࣜ 10. 9.ͷDynamicControllerΛݩʹมಈͷਫ਼౓ͱ଎౓Λ޲্ͤ͞ΔͨΊʹੵ෼ඍ෼੍ޚΛಋೖ͢Δํࣜ 17 ࠷దͳ੍ޚثΛٻΊͯ

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• ઃܭ੍ͨ͠ޚثΛฒߦԽ͞ΕͨλεΫΛܭ ࢉࢿݯ্ݶ·Ͱฒྻॲཧ͢Δ؆қతͳγϛϡ ϨʔλͰධՁ 18 ؆қධՁ • λεΫಛੑʹΑͬͯCPU্ݶ͸ҟͳΔ • ࠷దͳฒߦ਺͸࣮ߦ࣌ͷΈ൑அͰ͖ΔͨΊ CPU࢖༻཰͕ҰఆͱͳΔΑ͏ʹ૿ݮ͕ඞཁ • CPU࢖༻཰ͷ૿ݮΛܖػʹ໨ඪ஋Λݟ௚͢ ͜ͱ͕௥ै଎౓ͷ޲্ʹޮՌ͋Γ

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19 Kaburaya: DynamicTargetController ภࠩ ໨ඪ஋ 1 * % r AAACknichVG7SgNBFD1ZXzE+Eh+FYCMGRSzCrAiKVdTGwsIk5iFRZHcddchmd9mdBDT4A9oqFlYKFmLhH9jY+AMWfoKkjGBj4c1mQVTUu+zMmTP3nLmXqzum8CRjzyGlrb2jsyvcHenp7euPxgYGc55dcQ2eNWzTdgu65nFTWDwrhTR5wXG5VtZNntdLy837fJW7nrCtdXng8K2ytmeJXWFokqiUux2LswTzY+wnUAMQRxBrduwem9iBDQMVlMFhQRI2ocGjrwgVDA5xW6gR5xIS/j3HESKkrVAWpwyN2BKte3QqBqxF56an56sNesWk3yXlGCbYE7thDfbIbtkLe//Vq+Z7NGs5oF1vabmzHT0eybz9qyrTLrH/qfpDoVN2q7Jndkd11Temi/k/e5TYxbzfm6BeHZ9pdm20fKqH543MQnqiNsmuWJ36vSTnB3K2qq/GdYqnL2hc6vfh/AS5mYTKEmpqNp5cCgYXxijGMUXTmUMSK1hDll7lOMEpzpRhZUFZVJZbqUoo0AzhSyirH/1ElaE= 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 e 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 (PBQQ ϑΟʔυόοΫ 4FNBQIPSF $166TBHF มԽ཰ • ฒྻॲཧͷݶ౓ͱͳΔCPU࢖༻཰Λ࣮ߦ࣌ʹଊ͑ΔͨΊ໨ඪ஋ΛมԽ཰͔Βߋ ৽͢Δೋॏϧʔϓ੍ޚثΛ࣮૷ɽ • ಺ଆͷϧʔϓ͸ฒߦ਺ΛೖྗɼCPU࢖༻཰Λग़ྗͱ͢ΔPID੍ޚث

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• ༧ߘʮKaburaya: CPUෛՙʹԠͯ͡ܧଓతʹ্ݶ஋Λ࠷దԽ͢ΔಈతηϚ ϑΥʯͱͯ͠ୈ3ճWeb System Architectureݚڀձˏ౦ژ #wsaݚͰൃද • https://blog.monochromegane.com/blog/2018/11/25/wsa_3_kaburaya/ • https://speakerdeck.com/monochromegane/wsa-3-kaburaya 20 ༧ߘ

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3. Goݴޠ΁ͷద༻

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22 • CPUό΢ϯυͳॲཧͱͯ͠ϑΟϘφον(35)ͱ(10)ΛҰఆճ਺ٻΊΔॲཧ࣌ؒΛܭଌ • CPUΛ100%࢖͍੾ΔΑ͏ͳ৔߹͸ίΞ਺Ҏ্ͷฒߦ਺͸ੑೳ޲্͠ͳ͍ • CPUʹ༨༟͕͋Δ৔߹͸ίΞ਺Ҏ্ͷฒߦ਺Ͱ΋ੑೳ޲্ͷ༨஍͕͋Δ

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23 • ϒϩοΩϯάॲཧͱͯ͠Sleep΍NetworkΛҰఆճ਺ߦ͏ॲཧ࣌ؒΛܭଌ • ඇৗʹ୹͍SleepͰ͸ίΞ਺Ҏ্ͷฒߦ਺Ͱ΋ੑೳ޲্ͷ༨஍͕͋Δ • ωοτϫʔΫॲཧͰ͸଴ͪͷ௕͕͞ฒྻԽͷԸܙΛͳͨ͘͢Ίੑೳ޲্͕ฒߦ਺ʹґଘ

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24 • ϥϯλΠϜ͸ٖࣅతͳฒྻԽ΋͘͠͸ϚϧνίΞΛར༻͢ΔฒྻԽΛఏڙ͢Δ͕ɼδϣϒͷಛ ੑʹΑͬͯɼλεΫॲཧ࣌ͷCPU࢖༻཰͸CPU্ݶ΋͘͠͸ಛఆͷ࣌఺ͰมԽ͠ͳ͘ͳΔ

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• λεΫͷಛੑɼϥϯλΠϜͷಛੑʹΑͬͯ࠷దͳฒߦ਺͸ҟͳΔɽ • ฒྻॲཧͷ݁ՌɼCPU࢖༻཰͕CPU্ݶ·ͨ͸Ұఆ஋Ͱۉߧ͢Δɽ 25 ฒߦ਺ͷࢉग़ • KaburayaΞʔΩςΫνϟͷલఏʹԊ͏͜ͱ͔Βɼ੍ޚثͱͯ͠ DynamicTargetControllerΛར༻

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Goݴޠʹ͓͚ΔόοϑΝ෇͖νϟϯωϧΛ ηϚϑΥͱ͢Δಉ࣮࣌ߦ਺੍ޚ 26 ฒߦ਺ͷ੍ޚ func main() { var wg sync.WaitGroup sem := make(chan struct{}, 3) for i := 0; i < 10; i++ { wg.Add(1) sem <- struct{}{} go func() { defer wg.Done() defer func() { <-sem }() task() }() } wg.Wait() close(sem) } • ηϚϑΥͷ্ݶ஋͕ಈతʹมߋՄೳͰ ͋Δ͜ͱ • ௨ৗͷηϚϑΥͱಉ༷ʹPૢ࡞ʹ͓͍ ͯ஋͕ෛʹͳΔ৔߹ʹ࣮ߦ͕ϒϩοΫ ͞ΕΔ͜ͱ • ͜ΕΒͷ஋ͷมߋ͕ΞτϛοΫʹߦΘ ΕΔ͜ͱ • ্ݶ஋ΛมߋՄೳͳόοϑΝ෇͖νϟ ϯωϧ͕ඞཁ

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• ೖྗͱग़ྗͷೋͭͷνϟϯωϧͱΩϡʔΛ΋ͭߏ଄ମ • Ωϡʔͷঢ়ଶʹԠͯ͡ೖྗͱग़ྗͷՄ൱Λ੍ޚ͢Δ • Ref: https://github.com/npat-efault/musings/wiki/Elastic-channels • Kaburaya༻్Ͱ͸Ωϡʔͷ஋Λར༻͢Δ͜ͱ͸ͳ͍ͷͰΧ΢ϯλͰ୅༻ 27 όοϑΝ্ݶ஋ΛมߋՄೳͳElasticChannel DIBO DIBO &MBTUJD$IBOOFM 2VFVF &NQUZ*OPOMZ 'VMM0VUPOMZ 0UIFS*O0VU $IBOHFMJNJU

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• Kaburaya optimize the number of goroutines by feedback control. 28 monochromegane/kaburaya func main() { sem := kaburaya.NewSem(100 * time.Millisecond) var wg sync.WaitGroup for i := 0; i < 10; i++ { wg.Add(1) sem.Wait() go func() { defer sem.Signal() defer wg.Done() task() }() } wg.Wait() sem.Stop() } Create semaphore and specify time span for update the semaphore and CPU target. Wait for decrement operation. Increment operation. Stop update the semaphore and CPU target.

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4. ධՁ

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• ߴ଎Խ཰(Speedup): ஞ࣮࣍ߦ࣌ؒͱฒྻ࣮ߦ࣌ؒͷൺ཰ • ࣮ߦޮ཰(Efficiency): ฒߦ਺͋ͨΓͷߴ଎Խ཰ɽܭࢉࢿݯͷޮ཰ੑࢦඪɽ 30 ࠷దͳฒߦ਺ͱ͸ $ ./score fib 40 64 [S] 41.20142734s [1] 40.94367043s -> Speedup: 1.006301 Efficiency: 1.006301 [2] 21.138453817s -> Speedup: 1.949144 Efficiency: 0.974572 [4] 12.296148779s -> Speedup: 3.350764 Efficiency: 0.837691 [8] 12.371473761s -> Speedup: 3.330450 Efficiency: 0.416306 [16] 12.385120065s -> Speedup: 3.326686 Efficiency: 0.207918 • ͳ͓ɼKaburayaͰ͸ฒߦ਺͕૿ݮ͠ɼಛੑ্֎Ε஋ؚ͕·ΕΔ͜ͱ͔Βɼ࣮ ߦޮ཰ͷฒߦ਺ʹ͸ɼॲཧظؒͷฒߦ਺ͷதԝ஋Λ༻͍Δ

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network(1) * 8192 31 $ ./score network 1 8192 2> record.txt [S] 18.906590961s [1] 18.114622479s -> Speedup: 1.043723 Efficiency: 1.043723 [2] 8.311113276s -> Speedup: 2.274817 Efficiency: 1.137408 [4] 3.406973694s -> Speedup: 5.550793 Efficiency: 1.387698 [8] 1.690554271s -> Speedup: 11.186982 Efficiency: 1.398373 [16] 920.629201ms -> Speedup: 20.550000 Efficiency: 1.284375 [32] 664.033522ms -> Speedup: 28.472892 Efficiency: 0.889778 [64] 655.373022ms -> Speedup: 28.864122 Efficiency: 0.451002 [K] 738.116568ms -> Speedup: 25.617886 Efficiency: 0.711608 • K{p,i,d}=0.25ɼDuration=50ms • CPU্ݶʹୡ͠ͳ͍λεΫʹରͯ͠ൺֱతଥ౰ͳฒߦ਺ͷ੍ޚ͕ߦ͑ͨɽ

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fib(40) * 64 32 $ ./score fib 40 64 2> record.txt [S] 42.498952641s [1] 42.727265764s -> Speedup: 0.994640 Efficiency: 0.994640 [2] 21.084712153s -> Speedup: 2.015652 Efficiency: 1.007826 [4] 11.907186913s -> Speedup: 3.569161 Efficiency: 0.892290 [8] 12.128384712s -> Speedup: 3.504123 Efficiency: 0.438015 [16] 11.923092477s -> Speedup: 3.564371 Efficiency: 0.222773 [32] 11.911846969s -> Speedup: 3.567962 Efficiency: 0.111499 [64] 11.459039734s -> Speedup: 3.708701 Efficiency: 0.057948 [K] 13.368050152s -> Speedup: 3.179084 Efficiency: 0.635817 • K{p,i,d}=0.05ɼDuration=50ms • CPU্ݶʹୡ͢ΔλεΫʹରͯ͠ൺֱతଥ౰ͳฒߦ਺ͷ੍ޚ͕ߦ͑ͨɽ • λεΫ׬ྃ·ͰͷσΟϨΠʹΑͬͯηϚϑΥ஋ͷ௿ݮ͕ਐΈ͗͢Δ܏޲͋Γ

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fib(15) * 3276800 33 $ ./score fib 15 3276800 2> record.txt [S] 13.069192052s [1] 15.275737367s -> Speedup: 0.855581 Efficiency: 0.855581 [2] 10.518883281s -> Speedup: 1.242537 Efficiency: 0.621268 [4] 6.452542685s -> Speedup: 2.025573 Efficiency: 0.506393 [8] 4.261144318s -> Speedup: 3.067120 Efficiency: 0.383390 [16] 4.173533581s -> Speedup: 3.131800 Efficiency: 0.195737 [32] 4.096993901s -> Speedup: 3.190674 Efficiency: 0.099709 [64] 4.111361424s -> Speedup: 3.179032 Efficiency: 0.049672 [K] 15.535130172s -> Speedup: 0.841262 Efficiency: 0.052579 • K{p,i,d}=0.01ɼDuration=50ms • CPUෛՙͷগͳ͍େྔͷλεΫʹରͯ͠͸ߴ଎Խ཰ɼ࣮ߦޮ཰ͷ྆໘Ͱࣦഊ • ݱ࣮૷Ͱ͸ElasticChannelͷWaitૢ࡞Ͱbuffer0ͷChannel͕ϘτϧωοΫ

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• ϑΟʔυόοΫ੍ޚҰൠʹର͢Δ՝୊ • ήΠϯ(K{p,i,d})ͷ࠷ద஋ΛٻΊΔඞཁ͕͋Δ • σΟϨΠʹΑΔௐ੔ޡࠩΛݮΒ͢ඞཁ͕͋Δ • ϑΟʔυόοΫ੍ޚ෼໺ͷϊ΢ϋ΢Λ༗ޮ׆༻ͯ͠ղܾ͍ͨ͠ • ࣮૷ʹର͢Δ՝୊ • GoݴޠʹΑΔಈతηϚϑΥͷੑೳ໰୊Λղܾ͢Δඞཁ͕͋Δ • System V semaphoreͳͲ௿ϨΠϠ࣮૷Ͱղܾ͍ͨ͠ 34 ՝୊ͱରԠࢦ਑

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5. ·ͱΊ

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• ॲཧ΍ϥϯλΠϜͷಛੑʹґଘͤͣʹɼϚγϯͷෛՙʹԠͯ͡൓Ԡత͔ͭܧଓ తʹɼฒߦ਺Λ࠷దԽ͢Δख๏ͱͯ͠ɼCPUෛՙʹԠͯ͡ܧଓతʹ্ݶ஋Λ࠷ దԽ͢ΔKaburayaΞʔΩςΫνϟΛGoݴޠʹద༻ • ฒߦ਺ͷ૿Ճ͕ੑೳʹد༩͢ΔλεΫʹ͓͍ͯ͸ɼଥ౰ͳฒߦ਺Λࢉग़Մೳ • ࠓޙ͸՝୊Λղܾͯ͠ਫ਼౓ͱ଎౓Λ޲্ͤ͞ɼݴޠΛ௒͑ͨ൚༻ੑΛ໨ࢦ͢ • Go 36 ·ͱΊ

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