6 research outputs found

    ๋งค๋‹ˆ์ฝ”์–ด ํ™˜๊ฒฝ์—์„œ์˜ ํ–ฅ์ƒ๋œ ๊ธฐ์ˆ˜์ •๋ ฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2019. 2. ์‹ ์˜๊ธธ.3์ฐจ์› ๋ฉ”์‰ฌ ๋ณต์…€ํ™”๋Š” ์ž…๋ ฅ์œผ๋กœ ๋“ค์–ด์˜จ ๋ฉ”์‰ฌ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณต์…€ ๋ฐ์ดํ„ฐ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๊ณผ์ •์œผ๋กœ ํ”„๋กœ์„ธ์Šค์—๋Š” ์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด ํฌํ•จ๋œ๋‹ค. ์ผ๋ฐ˜์ ์ธ ์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ GPU ๊ตฌ์กฐ์—์„œ ์„ฑ๋Šฅ์ด ์ข‹์ง€ ์•Š์€ ์•Œ๊ณ ๋ฆฌ์ฆ˜์ด๊ธฐ ๋•Œ๋ฌธ์— 3์ฐจ์› ๋ฉ”์‰ฌ ๋ณต์…€ํ™”์˜ ์ˆ˜ํ–‰์‹œ๊ฐ„์˜ ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•˜๋Š” ๋ฌธ์ œ์ ์ด ์žˆ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” GPU ํ™˜๊ฒฝ์—์„œ์˜ ๋ณ‘๋ ฌํ™”๊ฐ€ ํž˜๋“ค์–ด 3์ฐจ์› ๋ฉ”์‰ฌ ๋ณต์…€ํ™” ์ˆ˜ํ–‰์‹œ๊ฐ„ ์ค‘ ๋Œ€๋ถ€๋ถ„์„ ์ฐจ์ง€ํ•˜๊ณ  ์žˆ๋Š” ์ •๋ ฌ ๋ถ€๋ถ„์„ ๋งค๋‹ˆ์ฝ”์–ด ํ™˜๊ฒฝ์„ ์ด์šฉํ•˜์—ฌ ์ตœ์ ํ™”ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋‘ ๋‹จ๊ณ„๋กœ ์ด๋ฃจ์–ด์ง„ ๊ฐœ์„ ๋œ ๊ธฐ์ˆ˜ ์ •๋ ฌ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•œ๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ ์ œ์•ˆํ•œ ๋ฐฉ๋ฒ•์œผ๋กœ ๊ธฐ์กด์˜ ๋ฐฉ๋ฒ•๊ณผ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ, ๊ธฐ์กด ๋ฐฉ๋ฒ•์œผ๋กœ ํ•ด๊ฒฐํ•  ์ˆ˜ ์—†๋Š” ๋ฐ์ดํ„ฐ ๋ฒ”์œ„๋ฅผ ์ •๋ ฌํ•  ์ˆ˜ ์žˆ์œผ๋ฉฐ ์ •๋ ฌ ๊ฐ€๋Šฅํ•œ ๋ฐ์ดํ„ฐ์˜ ๊ฒฝ์šฐ ๋ฐ์ดํ„ฐ ํฌ๊ธฐ์— ๋”ฐ๋ผ ์ตœ๋Œ€ 2.25๋ฐฐ์˜ ์†๋„ ํ–ฅ์ƒ์„ ์–ป์„ ์ˆ˜ ์žˆ์—ˆ๋‹ค.3D mesh voxelization is the process of converting inputted mesh data into voxel data, which includes sorting algorithm. Since a general sorting algorithm does not have a good performance in the GPU structure, its problem is that it takes up most of the time for 3D mesh voxelization. Using a many-core environment, this paper seeks to optimize the performing time of sorting time which takes up the most time for 3D mesh voxelization because sorting is difficult in a GPU environment. For this, it uses the improved radix sorting method consisting of two-step. According to the result of comparing the existing method to a new method proposed in this paper, the data range that cant be solved by the previous method can now be sorted, with up to 2.25 times of speed improvement gained depending on the data size for the sortable data.1. ์„œ๋ก  1 2. ๋ฐฐ๊ฒฝ ์ง€์‹ ๋ฐ ๊ด€๋ จ ์—ฐ๊ตฌ 4 2.1. GPU ๊ตฌ์กฐ 4 2.2. ๋งค๋‹ˆ์ฝ”์–ด 5 2.3. ๋ฉ”์‰ฌ 6 2.4. ๋ณต์…€ 8 2.5. 3์ฐจ์› ๋ฉ”์‰ฌ ๋ณต์…€ํ™” 9 2.6. GPU ๊ธฐ๋ฐ˜ ์ •๋ ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜ 13 2.6.1. ์ •๋ ฌ ๋„คํŠธ์›Œํฌ 13 2.6.2. ๋ฐ”์ดํ† ๋‹‰ ์ •๋ ฌ[8][11] 15 2.6.3. ์ •๋ ฌ ๋„คํŠธ์›Œํฌ ๊ธฐ๋ฐ˜ ์ •๋ ฌ์˜ ํ•œ๊ณ„์  17 3. ๋งค๋‹ˆ์ฝ”์–ด ํ™˜๊ฒฝ์—์„œ์˜ ํ–ฅ์ƒ๋œ ๊ธฐ์ˆ˜ ์ •๋ ฌ 18 3.1. Morton code[3] 18 3.2. ๋ณ‘๋ ฌํ™”๋œ ๊ธฐ์ˆ˜ ์ •๋ ฌ 20 3.3. ์ „์ฒด ์•Œ๊ณ ๋ฆฌ์ฆ˜ ํ๋ฆ„๋„ 23 3.4. ์ „์—ญ ๊ธฐ์ˆ˜ ์ •๋ ฌ 25 3.5. ์ง€์—ญ ๊ธฐ์ˆ˜ ์ •๋ ฌ 26 3.6. ๋งค๋‹ˆ์ฝ”์–ด ํ™˜๊ฒฝ์—์„œ์˜ ์ตœ์ ํ™” 28 4. ์‹คํ—˜ ๊ฒฐ๊ณผ 31 4.1. ์ผ๋ฐ˜์ ์ธ ๊ธฐ์ˆ˜ ์ •๋ ฌ๊ณผ์˜ ๋น„๊ต 31 4.2. ๋ณต์…€ ์กฐ๊ฐ ๋ฐ์ดํ„ฐ ์‹คํ—˜ ๊ฒฐ๊ณผ 33 5. ๊ฒฐ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ ๊ณผ์ œ 36 6. ์ฐธ๊ณ ๋ฌธํ—Œ 38Maste

    Studies on CPEB4-mediated memory consolidation and protein degradation-mediated reconsolidation using Aplysia neuronal cultures

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋‡Œ์ธ์ง€๊ณผํ•™๊ณผ ๋‡Œ์ธ์ง€๊ณผํ•™์ „๊ณต, 2016. 2. ๊ฐ•๋ด‰๊ท .๋‹จ๊ธฐ๊ธฐ์–ต์ด ์žฅ๊ธฐ๊ธฐ์–ต์œผ๋กœ ์ €์žฅ๋˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ๋‹จ๋ฐฑ์งˆ ํ•ฉ์„ฑ์„ ํ•„์š”๋กœ ํ•˜๋Š” ๊ฒฝํ™”๊ณผ์ •์ด ํ•„์š”ํ•˜๋‹ค. ๊ธฐ์–ต ์žฌ๊ฒฝํ™” ๊ฐ€์„ค์€ ๊ฒฝํ™”๋œ ์žฅ๊ธฐ๊ธฐ์–ต์ด ํšŒ์ƒ์„ ํ†ตํ•ด ๋– ์˜ฌ๋ ค์ง€๋ฉฐ ๊ทธ ํ›„ ์žฌ๊ฒฝํ™” ๊ณผ์ •์„ ๊ฑฐ์ณ์•ผ๋งŒ ๋‹ค์‹œ ์•ˆ์ •ํ™” ๋  ์ˆ˜ ์žˆ๋‹ค๊ณ  ์„ค๋ช…ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ํšŒ์ƒ์— ์˜ํ•ด ๊ธฐ์–ต์ด ์ธ์ถœ๋˜๋Š” ๊ณผ์ • ์ค‘ ์‹œ๋ƒ…์Šค์—์„œ ์ผ์–ด๋‚˜๋Š” ๋‹จ๋ฐฑ์งˆ ๋ถ„ํ•ด์™€ ์žฌํ•ฉ์„ฑ ๊ณผ์ •์ด ๋™์ผํ•œ ์‹œ๋ƒ…์Šค์—์„œ ์ผ์–ด๋‚˜๋Š”์ง€์— ๋Œ€ํ•ด์„œ๋Š” ์•„์ง ํ™•์‹คํ•˜๊ฒŒ ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š์•˜๋‹ค. ์ด๋Ÿฌํ•œ ํ˜„์ƒ์„ ์ž์„ธํžˆ ๋ฐํžˆ๊ธฐ ์œ„ํ•ด ๋น„๊ต์  ๋‹จ์ˆœํ•œ ์‹ ๊ฒฝ๊ณ„๋กœ ๊ตฌ์„ฑ๋œ ๋ฐ”๋‹ค๋‹ฌํŒฝ์ด์˜ ๋ฏผ๊ฐํ™”๊ธฐ์–ต์— ์ดˆ์ ์„ ๋งž์ถ”๊ธฐ๋กœ ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ฏผ๊ฐํ™” ๊ธฐ์–ต์€ ๊ตฐ์†Œ ๋‹จ์ผ์‹ ๊ฒฝ์„ธํฌ ๋ฐฐ์–‘๋ฒ•์„ ์ด์šฉํ•ด ์žฅ๊ธฐ๊ธฐ์–ต์ด ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„์œผ๋กœ ์„ธํฌ์ˆ˜์ค€์—์„œ ์žฌํ˜„๋  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ๊ธฐ์–ต์˜ ํšŒ์ƒ์— ์˜ํ•ด ์ผ์–ด๋‚˜๋Š” ์‹œ๋ƒ…์Šค ๋‹จ๋ฐฑ์งˆ์˜ ๋ถ„ํ•ด์™€ ์žฌํ•ฉ์„ฑ์ด ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„์ด ์ผ์–ด๋‚˜๋Š” ๋™์ผํ•œ ์‹œ๋ƒ…์Šค์—์„œ ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ ์™ธ์—๋„, ๊ตฐ์†Œ์˜ ์‹œ๋ƒ…์Šค ํŠน์ด์ ์ธ ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„ ๊ณผ์ •์˜ ํ˜•์„ฑ๊ณผ ์œ ์ง€์— ํ•„์š”ํ•œ ๊ตญ์†Œ ๋‹จ๋ฐฑ์งˆ ํ•ฉ์„ฑ ๊ณผ์ •์ด ๊ฐ๊ฐ ๋‹ค๋ฅธ ์‹ ํ˜ธ์ „๋‹ฌ ๊ฒฝ๋กœ๋กœ ์ผ์–ด๋‚˜๋Š” ๊ฒƒ์—๋„ ๊ด€์‹ฌ์„ ๊ฐ€์กŒ๋‹ค. ๊ธฐ์กด ์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด ApCPEB ์ด๋ผ๋Š” ๋‹จ๋ฐฑ์งˆ์€ rapamycin์— ์˜ํ•ด ์–ต์ œ๋˜๋Š” ๋‹จ๋ฐฑ์งˆ ํ•ฉ์„ฑ ๊ฒฝ๋กœ๋ฅผ ํ†ตํ•ด ํ˜•์„ฑ๋˜๋ฉฐ ์ด๊ฒƒ์˜ ๋ฐœํ˜„์ด ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„๊ณผ์ •์˜ ์œ ์ง€์— ํ•„์š”ํ•œ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ emetine์— ์˜ํ•ด ์–ต์ œ๋˜๋Š” ๋‹จ๋ฐฑ์งˆ ํ•ฉ์„ฑ ๊ฒฝ๋กœ๋ฅผ ํ†ตํ•œ ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„์˜ ํ˜•์„ฑ์— ํ•„์š”ํ•œ ๊ตญ์†Œ ๋‹จ๋ฐฑ์งˆ ํ•ฉ์„ฑ ๊ณผ์ •์€ ์•„์ง ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š์•˜๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ๊ธฐ์กด์— ์•Œ๋ ค์ ธ ์žˆ์ง€ ์•Š๋˜ ์ƒˆ๋กœ์šด Aplysia CPEB๋ฅผ ํด๋กœ๋‹ ํ•˜์˜€๊ณ , ApCPEB4-like protein ์ด๋ผ ๋ช…๋ช…ํ•˜์˜€๋‹ค. ์ด ๋‹จ๋ฐฑ์งˆ์€ 5-HT์— ์˜ํ•ด ์ฆ๊ฐ€ํ•˜์˜€๊ณ , ํ”„๋ฆฌ์˜จ ๋„๋ฉ”์ธ์ด ์—†์—ˆ๋‹ค. ๋˜ํ•œ emetine์— ์˜ํ•ด ํ•ฉ์„ฑ์ด ์–ต์ œ ๋˜์ง€๋งŒ rapamycin์— ์˜ํ•ด์„œ๋Š” ํ•ฉ์„ฑ์ด ์–ต์ œ๋˜์ง€ ์•Š๋Š” ๊ฒƒ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ApCPEB4๋Š” ๊ธฐ์กด์— ์•Œ๋ ค์ ธ ์žˆ๋˜ ApCPEB์™€ ๋‹ค๋ฅธ target mRNA์— ๋ถ™๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ ๋˜์—ˆ๊ณ , ์ด ๋‹จ๋ฐฑ์งˆ์˜ ๋ฐœํ˜„์„ ์ €ํ•ดํ•˜์˜€์„ ๋•Œ ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„์˜ ํ˜•์„ฑ์ด ์–ต์ œ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๊ณผ๋ฐœํ˜„ ์‹œ์—๋Š” ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„์ด ํ˜•์„ฑ๋˜๊ธฐ ์œ„ํ•œ ์—ญ์น˜๊ฐ’์ด ๊ฐ์†Œํ•œ ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์„ ํ†ตํ•ด ApCPEB์€ ์žฅ๊ธฐ์‹œ๋ƒ…์Šค ์ด‰์ง„์˜ ์œ ์ง€๋ฅผ ์œ„ํ•ด ํ•„์š”ํ•œ ๋ฐ˜๋ฉด, ํ”„๋ฆฌ์˜จ ๋„๋ฉ”์ธ์ด ์—†๋Š” ApCPEB4๋Š” ์žฅ๊ธฐ ์‹œ๋ƒ…์Šค ์ด‰์ง„์˜ ํ˜•์„ฑ์— ์ค‘์š”ํ•œ ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค.Dynamic process of memory requires consolidation to store memory in long-term. The memory reconsolidation hypothesis suggests that this memory trace becomes labile after retrieval and needs to be reconsolidated before it can be stabilized. However, it is unclear from earlier studies whether the same synapses involved in encoding the memory trace are those that are destabilized and restabilized after the synaptic reactivation that accompanies memory retrieval, or whether new and different synapses are recruited. To address this issue, I focused on simple form of non-associative memory, long-term sensitization of the gill- and siphon-withdrawal reflex in Aplysia. Using its cellular analog, long-term facilitation (LTF) at the sensory-to-motor neuron synapse, I found that on the cellular level, long-term facilitation at the sensory-to-motor neuron synapse that mediates long-term sensitization is also destabilized by protein degradation and is restabilized by protein synthesis after synaptic reactivation, a procedure that parallels memory retrieval or retraining evident on the behavioral level. In addition, I also focused on the issues that two pharmacologically distinct types of local protein synthesis are required for synapse-specific LTF in Aplysia: one for initiation and the other for maintenance. ApCPEB, a rapamycin sensitive prion-like molecule regulates a form of local protein synthesis that is specifically required for the maintenance of the LTF. However, the molecular component of the local protein synthesis that is required for the initiation of LTF and sensitive to emetine is not known. Here, I identified a homolog of ApCPEB responsible for the initiation of LTF. The ApCPEB homolog which we have named ApCPEB4-like protein is responsive to 5-hydroxytryptamine (5-HT), lacks a prion-like domain, and is translated (but not transcribed) in an emetine-sensitive but rapamycin-insensitive and PKA-dependent manner. The ApCPEB4 binds to different target RNAs than does ApCPEB. Knock-down of ApCPEB4 blocked the induction of LTF, whereas overexpression of ApCPEB4 reduces the threshold of the formation of LTF. Thus, our findings suggest that the two different forms of CPEBs play distinct roles in LTFApCPEB is required for maintenance, whereas the ApCPEB4, which lacks a prion-like domain, is required for initiation.Chapter I. Introduction 1 Background 2 Purpose of this study 10 Chapter II. A cellular model of memory reconsolidation involves reactivation-induced destabilization and restabilization at the sensorimotor synapse in Aplysia 11 Introduction 12 Experimental Procedures 15 Results 18 Discussion 37 Chapter III. ApCPEB4-like protein, a non-prion domain containing homolog of ApCPEB, is involved in the initiation of long-term facilitation 40 Introduction 41 Experimental Procedures 43 Results 49 Discussion 72 Chapter IV. Conclusion 77 References 79 ๊ตญ๋ฌธ์ดˆ๋ก 90Docto

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์ƒ๋ฆฌํ•™ ์ „๊ณต,1999.Maste

    LB2-Tree: A Index Structure Specialized for Key-Value SSDs

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2021. 2. ๊น€์ง„์ˆ˜.ํ‚ค-๋ฐธ๋ฅ˜ SSD๋Š” ํ‚ค-๋ฐธ๋ฅ˜ ์Šคํ† ์–ด I/O ์Šคํ† ๋ฆฌ์ง€ ์Šคํƒ์„ ๊ตฌ์„ฑํ•จ์— ์žˆ์–ด ์ƒˆ๋กœ์šด ํŒจ๋Ÿฌ๋‹ค์ž„์„ ์ œ์‹œํ•œ๋‹ค. ํ‚ค-๋ฐธ๋ฅ˜ SSD๋Š” ์‚ฌ์šฉ์ž์—๊ฒŒ ํ‚ค-๋ฐธ๋ฅ˜ ์ธํ„ฐํŽ˜์ด์Šค๋ฅผ ์ œ๊ณตํ•˜์—ฌ ํ˜ธ์ŠคํŠธ ์‹œ์Šคํ…œ ์ž์›์˜ ์‚ฌ์šฉ๋Ÿ‰์„ ํฌ๊ฒŒ ์ค„์ด์ง€๋งŒ ์—ฌ๋Ÿฌ ํ•œ๊ณ„์ ์œผ๋กœ ์ธํ•ด ํ‚ค-๋ฐธ๋ฅ˜ ์Šคํ† ์–ด๋ฅผ ๋Œ€์ฒดํ•  ์ˆ˜๋Š” ์—†๋‹ค. ์ด ๋…ผ๋ฌธ์—์„œ๋Š” ํ‚ค-๋ฐธ๋ฅ˜ SSD์˜ ํŠน์ง•๋“ค์„ ๋ถ„์„ํ•˜๊ณ  ์ด๋ฅผ ํ‚ค-๋ฐธ๋ฅ˜ ์Šคํ† ์–ด์˜ ๋Œ€์ฒด์ œ๋กœ ์‚ฌ์šฉํ•˜๊ธฐ ์–ด๋ ค์šด ์ด์œ ๋ฅผ ์„ค๋ช…ํ•œ๋‹ค. ๋˜ํ•œ, ์ด๋Ÿฌํ•œ ๋ถ„์„๋“ค์„ ๋ฐ”ํƒ•์œผ๋กœ LB2-Tree๋ผ๋Š” ์ƒˆ๋กœ์šด ์ธ๋ฑ์Šค ๊ตฌ์กฐ๋ฅผ ์ œ์•ˆํ•œ๋‹ค. LB2-Tree๋Š” ๊ธฐ์กด Copy-On-Write B+Tree์—์„œ ๋‚˜ํƒ€๋‚˜๋Š” ๋ฌธ์ œ์ ์ธ compaction๊ณผ cascading update๋ฅผ ํ‚ค-๋ฐธ๋ฅ˜ SSD์˜ ํŠน์„ฑ๋“ค์„ ํ™œ์šฉํ•ด ํ•ด๊ฒฐํ•œ๋‹ค. LB2-Tree๋Š” ํŠนํžˆ ์—…๋ฐ์ดํŠธ๊ฐ€ ์žฆ์€ ํ™˜๊ฒฝ์—์„œ CoW B+Tree์— ๋น„ํ•ด ๋งค์šฐ ๋†’์€ ์„ฑ๋Šฅ๊ณผ ๋งค์šฐ ๋‚ฎ์€ ์“ฐ๊ธฐ๋Ÿ‰์„ ๋ณด์ธ๋‹ค.Key-Value SSDs presents a new paradigm in constructing the Key-Value Store I/O storage stack. Key-Value SSDs provide users with a key-value interface, which significantly reduces the use of host system resources, but can not replace Key-Value stores due to multiple limitations. This paper analyzes the characteristics of Key-Value SSDs and explains why they can not be used as an alternative to Key-Value stores. Based on these analyses, this paper propose a new index structure called LB2-Tree. LB2-Tree is based on Copy-on-Write B+Tree and solves problems like compaction, high write amplification, high CPU overhead, and cascading updates by using the characteristics of a Key-Value SSDs. LB2-Tree shows high throughput and low write and read amplification compared to CoW B+tree, especially in update-intensive workloads.์ œ 1 ์žฅ ์„œ๋ก  1 ์ œ2์žฅ ํ‚ค-๋ฐธ๋ฅ˜ SSD 3 2.1 ์‚ผ์„ฑ ํ‚ค-๋ฐธ๋ฅ˜ SSD 4 2.2 ํ‚ค-๋ฐธ๋ฅ˜ SSD ์„ฑ๋Šฅ ๋ถ„์„ 4 2.2.1 ์‹คํ—˜ ํ™˜๊ฒฝ 5 2.2.2 ์“ฐ๊ธฐ ๋ฐ ์ฝ๊ธฐ ์„ฑ๋Šฅ 5 2.3 ํ‚ค-๋ฐธ๋ฅ˜ SSD ํŠน์„ฑ ๋ถ„์„ 7 2.3.1 ๋ฌธ์ž์—ด ํ‚ค๋ฅผ ์„ฑ๋Šฅ ์˜ˆ์ธก์„ ์–ด๋ ต๊ฒŒ ํ•œ๋‹ค 8 2.3.2 ์ €์žฅ๋œ ๋ฐ์ดํ„ฐ๊ฐ€ ๋งŽ์„์ˆ˜๋ก ์ฝ๊ธฐ ์„ฑ๋Šฅ์€ ์ €ํ•˜๋œ๋‹ค 9 2.3.3 ๊ธธ์ด๊ฐ€ ๊ธด ํ‚ค๋Š” ์„ฑ๋Šฅ์„ ์ €ํ•˜ํ•œ๋‹ค 10 2.4 ํ‚ค-๋ฐธ๋ฅ˜ SSD์˜ ํ•œ๊ณ„ 10 ์ œ3์žฅ ํŠธ๋ฆฌ ๋ฐ์ดํ„ฐ ๊ตฌ์กฐ 12 3.1 Log-Structured Merge-Tree 12 3.2 Log-Structured Merge-Tree์˜ ๋ฌธ์ œ์  13 3.3 Copy-On-Write B+Tree 15 3.4 Copy-On-Write B+Tree์˜ ๋ฌธ์ œ์  15 ์ œ4์žฅ Log-structured Blind write B+Tree 17 4.1 ๊ฐœ์š” 17 4.2 ์“ฐ๊ธฐ ๋™์ž‘ 18 4.3 ์ฝ๊ธฐ ๋™์ž‘ 20 4.4 ๋กœ๊ทธ ๋ณ‘ํ•ฉ ์ž‘์—… 21 4.5 ๊ฐ€๋ณ€ ๊ธธ์ด ๋…ธ๋“œ ์“ฐ๊ธฐ ๊ธฐ๋ฒ• 21 4.6 ๊ฒฝ๋Ÿ‰ ๊ณต๊ฐ„ ์žฌํ™•๋ณด ๊ธฐ๋ฒ• 22 4.7 ๋ฉ€ํ‹ฐ ์Šค๋ ˆ๋“œ ๋ณต๊ตฌ ๊ธฐ๋ฒ• 23 ์ œ5์žฅ ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๋ถ„์„ 25 5.1 ์‹คํ—˜ ํ™˜๊ฒฝ 25 5.2 ์ฒ˜๋ฆฌ๋Ÿ‰ ๋น„๊ต 26 5.3 ์“ฐ๊ธฐ๋Ÿ‰ ๋ฐ ์ฝ๊ธฐ๋Ÿ‰ ๋น„๊ต 27 5.4 ๋ณต๊ตฌ ์†๋„ ๋น„๊ต 29 ์ œ6์žฅ ๊ฒฐ๋ก  31 ์ฐธ๊ณ ๋ฌธํ—Œ 32 Abstract 34Maste

    ๊ธฐ๋‹ˆํ”ผ๊ทธ ์œ„ ํ‰ํ™œ๊ทผ์—์„œ ATP ์˜์กด์„ฑ ํฌํƒ€์Š˜ํ†ต๋กœ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์˜ํ•™๊ณผ ์ƒ๋ฆฌํ•™์ „๊ณต,2002.Docto
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