Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
これまでの研究経過と希望する研究 / Research Plan for Doctoral C...
Search
Yuuki Tsubouchi (yuuk1)
February 06, 2020
Research
430
1
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
これまでの研究経過と希望する研究 / Research Plan for Doctoral Course
令和2年度4月期入学 京都大学大学院 情報学研究科
博士後期課程 第2次学力検査 口頭試問
Yuuki Tsubouchi (yuuk1)
February 06, 2020
More Decks by Yuuki Tsubouchi (yuuk1)
See All by Yuuki Tsubouchi (yuuk1)
「アラーティング」の話をしよう— SREconや論文等の最先端とのギャップをみる
yuukit
7
2.1k
SAKURAONE:An Open Ethernet-based AI HPC System And Its Observed Workload Dynamicsin a Single-Tenant LLM Development Environment
yuukit
1
670
AIスーパーコンピュータにおけるLLM学習処理性能の計測と可観測性 / AI Supercomputer LLM Benchmarking and Observability
yuukit
1
1.2k
SREはサイバネティクスの夢をみるか? / Do SREs Dream of Cybernetics?
yuukit
3
850
SREのためのテレメトリー技術の探究 / Telemetry for SRE
yuukit
14
4k
AIスパコン「さくらONE」の オブザーバビリティ / Observability for AI Supercomputer SAKURAONE
yuukit
2
1.6k
AIスパコン「さくらONE」のLLM学習ベンチマークによる性能評価 / SAKURAONE LLM Training Benchmarking
yuukit
2
1.3k
とあるSREの博士「過程」 / A Certain SRE’s Ph.D. Journey
yuukit
11
7.7k
eBPFを用いたAIネットワーク監視システム論文の実装 / eBPF Japan Meetup #4
yuukit
3
1.9k
Other Decks in Research
See All in Research
実例から見るLLMのマンガ理解:実務VQAタスクによる長期的文脈と視覚情報の定性評価
kzmssk
0
180
OWASP AISVS - C7
shiell
2
830
Être éleveur dans un monde en transition : Comprendre les changements pour mieux les accompagner
institutdelelevage
PRO
0
120
【中間報告】国会議員の立法・政策実務を支える環境を巡る現状と課題
polipoli
0
720
SAM3を用いたコマ・吹き出しの 領域検出と分割構造からの読み順推定
kzmssk
0
180
The Conductor Pattern -Multi-Granularity Feedback for Creative Agents-
shibuiwilliam
2
110
JPA2026_NetworkTutorial_JunKashihara
junkashihara
0
230
ros2-perf-multihost: Automated Coordination Framework for Objective Architecture Evaluation in Distributed Systems
takasehideki
0
140
Détecter les vulnérabilités dans un environnement souverain
hellosct1
0
110
論文紹介: Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind
hisaokatsumi
0
190
[SNLP2026] Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
wataruuuuu
0
330
Visual SLAM未来予測 / Future Prediction in Visual SLAM
koide3
1
1.2k
Featured
See All Featured
The Art of Delivering Value - GDevCon NA Keynote
reverentgeek
16
2.2k
Designing Experiences People Love
moore
143
24k
Building AI with AI
inesmontani
PRO
1
1.3k
Automating Front-end Workflow
addyosmani
1369
210k
The World Runs on Bad Software
bkeepers
PRO
72
12k
Building Better People: How to give real-time feedback that sticks.
wjessup
370
20k
Gemini Prompt Engineering: Practical Techniques for Tangible AI Outcomes
mfonobong
2
580
Darren the Foodie - Storyboard
khoart
PRO
4
4k
JAMstack: Web Apps at Ludicrous Speed - All Things Open 2022
reverentgeek
1
630
Skip the Path - Find Your Career Trail
mkilby
1
250
Digital Ethics as a Driver of Design Innovation
axbom
PRO
1
450
Conquering PDFs: document understanding beyond plain text
inesmontani
PRO
4
3.2k
Transcript
͜Ε·Ͱͷݚڀܦաͱر͢Δݚڀ ௶ ༎थ 20202݄6 ྩ24݄ظೖֶ ژେֶେֶӃ ใֶݚڀՊ ത࢜ޙظ՝ఔ ୈֶ̎࣍ྗݕࠪ ޱ಄ࢼ
2 1. ֶ࢜՝ఔɾम࢜՝ఔͰͷݚڀ 2. ر͢Δݚڀͷഎܠͱత 3. ر͢Δݚڀͷ՝ͱํ๏ 4. ظ͞ΕΔݚڀՌ ࣍
1. ֶ࢜՝ఔɾम࢜՝ఔͰͷݚڀ
4 ֶ࢜՝ఔ: SHA-1ܭࢉγεςϜͷߴεϧʔϓοτԽ ɾഎܠ: ετϨʔδ্ͷσʔλอଘྔ͕૿େ͍ͯ͠Δ ɾ: σʔλͷॏෳՕॴΛഉআ͢Δ͜ͱʹΑΓɼσʔλอଘྔΛ ݮՄೳ͕ͩɼॏෳ෦ͷൃݟॲཧ͕ϘτϧωοΫͱͳΔ ɾఏҊ: ෳͷҟͳΔνϟϯΫʹର͢ΔॏෳൃݟॲཧΛSIMDԋࢉثʹ
ΑΓฒྻॲཧ͠ɼߴԽ͢Δ ɾ࣮ݧ: ઌߦख๏ͱൺֱͯ͠ɼ2.0ഒͷεϧʔϓοτ্Λୡͨ͠ ௶༎थ, ҏจ, ஔాਅੜ, ࢁ૱, ദַ, ഡݪ݉Ұ, ॏෳഉআετϨʔδͷͨΊͷ SHA-1ܭࢉγεςϜͷSSE໋ྩʹ ΑΔߴεϧʔϓοτԽ, ిࢠใ௨৴ֶձจࢽ D, 96(10), 2101-2109 2013.
5 म࢜՝ఔ: TCP/IPνΣοΫαϜͷGPUʹΑΔੑೳ্ ɾഎܠ: ίϯϐϡʔλωοτϫʔΫ͕ଳҬԽ͍ͯ͠Δ ɾ: OS෦ͷ௨৴ॲཧͷͨΊͷCPUෛՙ͕ߴ·ͬͯ ͍Δ ɾఏҊ: ௨৴ॲཧͷ͏ͪ௨৴σʔλʹର͢ΔνΣοΫαϜܭࢉΛGPU
ʹҕৡ͢Δ͜ͱʹΑΓɼCPUෛՙΛݮͤ͞Δ ɾ࣮ݧ: CPUʹΑΔνΣοΫαϜΛߦ͏ͱൺֱ͠ɼσʔλసૹε ϧʔϓοτ͕࠷େͰ13%্ ௶༎थ, ୩߶, ୩ޱٛ໌, தതོ, দԬໜొ, TCP/IPελοΫʹ͓͚ΔνΣοΫαϜܭࢉͷ GPU Φϑ ϩʔυʹΑΔ ੑೳ্ख๏, ిࢠใ௨৴ֶձٕज़ݚڀใࠂ, NS, ωοτϫʔΫγεςϜ, 113(244), pp.67-72 2013 10݄.
ཧతʹࢄͨ͠ΞϓϦέʔγϣϯͷ ߴ৴པԽͷͨΊͷΞʔΩςΫνϟʹؔ͢Δݚڀ ത࢜՝ఔͰر͢Δݚڀͷ
2. ر͢Δݚڀͷഎܠͱత
8 WebαʔϏεͷཁٻͷߴԽ ・サービスの信頼性の期待値向上 ・冗⻑性の確保,アクセス増に対するスケーラビリティの向上 ɾ୯ҰͷࣄۀऀʹΑΔෳͷαʔϏεల։ ɾڞ௨෦Λ֤αʔϏεͰڞ༗͢Δ͜ͱͰαʔϏεಉ͕࢜௨৴ ɾ10୯ҐͷظؒͷαʔϏεఏڙ ɾػೳͷՃɼιϑτΣΞͷߋ৽ɼγεςϜߏͷ৽ͳͲΛܧଓ ɾར༻ऀͷ͔ΒΫϥυͷωοτϫʔΫԆΛॖ ɾཧతʹࢄͨ͠ڥʹ͓͚ΔࢄΞϓϦέʔγϣϯઃܭ͕ࠓޙඞ
ཁͱͳΔ => ۭؒతӨڹൣғͷ૿େ => ࣌ؒతӨڹൣғͷ૿େ
9 ཧࢄԽʹؔ͢Δҙࣝ γεςϜͷ෦ঢ়ଶͷѲ ͕͘͠ͳΔ ωοτϫʔΫԆͷ૿Ճ ɾγεςϜʹมߋΛՃ͑Δલ ʹɼϦεΫͷൣғΛݟੵΕ ͳ͘ͳΔ ɾϦεΫ͕ݦࡏԽͨ͠ͱ͖ʹɼ ݪҼͷಛఆ͕͘ͳΔ
ɾ֤ڌʹࢄ͢Δσʔλͷ ಉظ͕ͪ࣌ؒେ͖͘ͳΔ ɾσʔλϕʔε(DBMS)ͷҰ؏ ੑͱੑೳΛཱ྆ͤͮ͞Β͍ ߏཁૉͷ૿Ճ
10 ઌߦݚڀͱ՝: Մ؍ଌੑ ࣌ؒ࣠ํͷՄ؍ଌੑ ۭؒ࣠ํͷՄ؍ଌੑ ɾ࣌ܥྻσʔλϕʔεʹ֤छ ܭଌΛอଘ͢Δ ɾੑೳΛॏࢹ࣮ͯ͠Λ࣌ܥ ྻσʔλʹ࠷దԽ͍ͯ͠Δ ɾطଘͷDBMSΛར༻ͨ͠ޓ
ੑ֦ுੑ͕ࣦΘΕΔ ɾαϒγεςϜؒͷґଘؔΛ ࣗಈͰ͢Δ ɾطଘͷΞϓϦέʔγϣϯίʔ υͷมߋΛͱͳ͏ɼ͋Δ͍ ΞϓϦέʔγϣϯʹ༩͑Δ ੑೳӨڹ͕େ͖͍
11 ઌߦݚڀͱ՝: ੑೳͱσʔλҰ؏ੑ ɾ֤ڌؒͰσʔλΛಉظߋ৽ ɾωοτϫʔΫԆͷେ͖͍ڌ ʹಉظ͕࣌ؒ͞ΕΔ ɾ֤ڌؒͷσʔλΛඇಉظߋ৽ ɾσʔλҰ؏ੑʹର͢ΔΞϓϦ έʔγϣϯ։ൃऀͷൣғ͕ େ͖͘ͳΔ
ऑ͍Ұ؏ੑ(݁Ռ߹ੑ) Ԇͷେ͖ͳڥͰͷੑೳͱσʔλҰ؏ੑͷཱ྆ ڧ͍Ұ؏ੑ
ݚڀͷత ɾత: ཧతʹࢄͨ͠ΞϓϦέʔγϣϯͷ৴པੑ্ ɾ੍: طଘͷΞϓϦέʔγϣϯϛυϧΣΞͷίʔυΛมߋͤͣɼ ੑೳӨڹΛ͋ͨ͑ͳ͍ ɾ3ͭͷݚڀ՝ 1. ࣌ؒ࣠ͷՄ؍ଌੑ: ࣌ܥྻσʔλϕʔεͷੑೳͱޓੑͷཱ྆
2. ۭؒ࣠ͷՄ؍ଌੑ: ґଘؔΛෛՙ͔ͭཏతʹ 3. σʔλͷҰ؏ੑΛอূͭͭ͠ɼԠੑೳΛ࠷େԽ 12
3. ر͢Δݚڀͷ՝ͱํ๏
՝(1) ࣌ؒ࣠ํͷՄ؍ଌੑ ࣌ܥྻσʔλϕʔεʹ͓͚Δੑೳͱ֦ுੑͷཱ྆
15 ఏҊ(1): ՝ͱఏҊ ࣌ܥྻσʔλϕʔεͷ՝ ɾطଘͷDBMSΛར༻ͨ͠ޓ ੑ͕ࣦΘΕΔ ఏҊ ɾ୯ҰͷDBMSΛσʔλߏ୯ҐͰ ղ͠ɼૄ݁߹Խ ɾΠϯϝϞϦͱΦϯσΟεΫͷKVSΛ
֊Խͯ͠ॲཧͱอଘͷޮԽ ɾ֤σʔλߏʹରͯ͠طଘͷDBMS Λར༻Մೳ In-Memory KVS On—Disk KVS Tiering Single DBMS DBMS Index DBMS Log ఏҊΞʔΩςΫνϟ
՝(2) ۭؒ࣠ํʹ͓͚ΔՄ؍ଌੑ ґଘؔΛ͢Δ্Ͱ ཏੑͱΦʔόʔϔουΛཱ྆
17 ఏҊ(2): ιέοτࢹ ґଘؔͷ՝ ɾطଘͷΞϓϦέʔγϣϯ ίʔυͷมߋΛͱͳ͏ɼ ͋Δ͍ΞϓϦέʔγϣϯ ʹ༩͑ΔੑೳӨڹ͕େ͖͍ ఏҊ ɾશαʔό্ͰɼOSΧʔωϧͷTCP
ଓͷऴͰ͋ΔιέοτΛࢹ ɾιέοτࢹϓϩηεΛஔ͢Δͷ ΈͰՄೳ ɾιέοτࢹΞϓϦέʔγϣϯͷ ௨৴ʹׂΓ͜·ͣɼΦʔόϔου Kernel Process TCP Flows . . . User ιέοτࢹ Process
՝(3) ཧࢄڥʹ͓͚Δσʔλϕʔε ͷҰ؏ੑͱੑೳͷཱ྆
19 ఏҊ(3): σʔλͷಉظൣғΛదԠతʹܾఆ σʔλҰ؏ੑͱੑೳͷ՝ ɾ֤ڌؒͰσʔλΛಉظߋ৽ ɾωοτϫʔΫԆͷେ͖ͳڌ ʹಉظॲཧ͕͞ΕΔ ఏҊ ɾಡΈࠐΈॲཧͷΈͷॖʹண͠ɼ தԝͷڌʹॻ͖ࠐΈΛ͚Δ
ɾશڌͰಉظͤͣʹɼதԝͷڌ͔ ΒωοτϫʔΫԆ͕ҰఆΑΓখ ͍͞Ұ෦ͷڌͷΈಉظ ɾΞϓϦέʔγϣϯ୯ҐͷಡΈॻ͖ൺ ʹج͖ͮಉظൣғΛదԠతʹܾఆ Origin தԝͷڌ Replica Τοδͷڌ Replica ಉظൣғ
4. ظ͞ΕΔݚڀՌ
21 ݚڀશମͷظ͞ΕΔߩݙ ɾطଘͷΞϓϦέʔγϣϯίʔυDBMSΛมߋͤͣɼ͔ͭੑೳʹେ ͖ͳӨڹΛ༩͑ͳ͍ͱ͍͏੍ͷͱ 1. ཧࢄΞϓϦέʔγϣϯͷՄ؍ଌੑΛ্Մೳ 2. ؍ଌ݁ՌΛར༻͠ԠੑೳΛ࠷େԽ͢ΔΑ͏ʹదԠతʹ੍ޚՄೳ ɾγεςϜཧऀͷෛ୲Λ૿ͣ͞ʹWebαʔϏεͷ৴པੑΛ্͞ ͤɼਓʑ͕WebαʔϏεΛշద͔ͭ࣋ଓతʹར༻Ͱ͖ΔΑ͏ʹͳΔ