61 research outputs found

    [์ด์Šˆ๋ถ„์„] ์ฒญ๋…„์ธต ํ•ด์™ธ์ทจ์—…ยทํ•ด์™ธ์ธํ„ด ์‚ฌ์—…์˜ ๊ธ€๋กœ๋ฒŒ ์—ญ๋Ÿ‰ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•œ ๊ณผ์ œ

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    โ… . ๋…ผ์˜ ๋ฐฐ๊ฒฝ๊ณผ ๋ชฉ์  โ…ก. ํ•ด์™ธ์ทจ์—…์ง€์›์‚ฌ์—… ์ถ”์ง„ํ˜„ํ™ฉ ๋ฐ ๋ฌธ์ œ์  1. ํ•ด์™ธ์ทจ์—…์ง€์›์‚ฌ์—… ์ถ”์ง„๊ฒฝ๊ณผ 2. ํ•ด์™ธ์ทจ์—…์ง€์›์‚ฌ์—… ํ˜„ํ™ฉ 3. ์ฒญ๋…„ ํ•ด์™ธ์ง„์ถœ์‚ฌ์—…์˜ ๋ฌธ์ œ์  โ…ข. ํ–ฅํ›„ ๊ณผ

    ๋†’์€ ์„ฑ๋Šฅ๊ณผ ํ™•์žฅ์„ฑ์„ ์œ„ํ•œ ๋ฉ”๋ชจ๋ฆฌ ๊ด€๋ฆฌ ์‹œ์Šคํ…œ ์ตœ์ ํ™”

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€,2019. 8. ์—ผํ—Œ์˜.One common characteristic of modern workloads which appeared with recent computing paradigms including cloud, big data and machine learning is memory intensiveness. Such workloads usually have huge working sets that cannot be fully accommodated in DRAM in many case. Those also tend to show only low locality so that the small CPU cache cannot hide DRAM or lower level memory access overhead. Meanwhile, computing hardware has also evolved to keep pace with this change. (1) Computing systems are increasing the size of their main memory so that those could accommodate more of the huge working sets. As a result, data center servers utilizing few hundreds of gigabytes of DRAM have been common and even terabytes of DRAM equipped systems exist. (2) Massive parallelism is becomming common and essential. CPU vendors have started to increase the number of CPU cores instead of the CPU frequency due to the heat dissipation and power consumption problem since the early 2000s. Prevalent datacenter systems provide few hundreds of CPU cores; Few thousands of CPU cores are not rare. Such many-core systems are normally constructed in non-uniform memory access (NUMA) architecture. Therefore, efficient, effective and NUMA-awared use of this parallelism is especially important for the memory intensive workloads. Compared to these rapid changes of workload characteristics and hardware, memory management system software has not sufficiently optimized. Consequently, the memory management system software has been a bottleneck. In other words, the memory intensive modern workloads cannot fully utilize the evolved modern hardware unless the underlying memory management system is completely optimized. This paper provides an overview of a few limitations in existing memory management systems and introduces two optimization approaches for high performance and scalability of the memory management systems. The first approach improves the performance of the memory systems by guaranteeing huge page utilization under memory fragmentation situation. For the guarantee, we introduce a contiguous memory allocator that guarantees success and low latency of its allocations. The second approach intends to optimize the NUMA-aware system scalability. For that, we optimize virtual memory address space management system by substituting virtual memory area (VMA) managing red-black tree protection from global reader-writer locking to an RCU extension. Because no RCU extension including state-of-the-arts are NUMA oblivious, we also designed new RCU extension that provides NUMA-aware scalable update-side synchronization.Abstract 1 Chapter 1 Introduction 6 1.1 Motivation 6 1.2 Approaches 7 1.2.1 An Optimization for High Performance 7 1.2.2 An Optimization for High Scalability 9 1.3 Dissertation Structure 10 Chapter 2 Guaranteed Transparent Huge Pages Allocations 12 2.1 Introduction 12 2.2 Background 16 2.2.1 Devices using DMA 16 2.2.2 Huge Pages 17 2.2.3 Buddy Allocator 20 2.2.4 Memory Reservation 21 2.2.5 Contiguous Memory Allocator 21 2.3 Guaranteed CMA 22 2.3.1 Secondary Class Clients of GCMA 23 2.3.2 Limitations and Optimizations 26 2.4 Implementation 27 2.4.1 Contiguous Memory Allocation 29 2.4.2 DMEM: Discardable Memory 30 2.5 Guaranteed THP 30 2.6 Evaluation 32 2.6.1 Evaluation on a Mobile System 32 2.6.2 Evaluation on a Server System 38 2.7 Related Work 45 2.8 Conclusion 47 Chapter 3 A Scalable Virtual Address Space Protected by an HTM-based NUMA-aware RCU Extension 48 3.1 Introduction 48 3.2 Background and Related Work 50 3.2.1 Read-Copy Update 50 3.2.2 Hardware Transactional Memory 53 3.2.3 Related Work 54 3.3. An RCU Extension for NUMA Systems 57 3.3.1 Root Cause of HTM Performance Degradation on NUMA systems 57 3.3.2 Design of RCX 62 3.3.3 Implementation 70 3.4 Evaluation 71 3.4.1 Evaluation Setup 71 3.4.2 Micro-benchmarks 72 3.4.3 Macro-benchmark 76 3.5 Conclusion. 80 Chapter 4 Conculsion 81Docto

    Social Engagement Moderates the Relationship between Age-related White Matter Hyperintensity Volume and Episodic Memory in Older Adults

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ์‹ฌ๋ฆฌํ•™๊ณผ, 2021. 2. ์ตœ์ง„์˜.๋…ธ๋…„๊ธฐ์—๋Š” ์‹ ์ฒด์  ๊ธฐ๋Šฅ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ธ์ง€๊ธฐ๋Šฅ์ด ๊ฐํ‡ดํ•˜๋Š” ์–‘์ƒ์„ ๋ณด์ธ๋‹ค. ํŠนํžˆ ๋…ธ๋…„๊ธฐ์— ๋น ๋ฅธ ์†๋„๋กœ ๊ฐํ‡ดํ•˜๋Š” ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ์€ ๊ฐœ์ธ์˜ ์ผ์ƒ์ƒํ™œ ์ˆ˜ํ–‰๋Šฅ๋ ฅ์„ ์˜ˆ์ธกํ•˜๋Š” ๊ฒƒ์„ ์•Œ๋ ค์ ธ ์žˆ์–ด ์„ฑ๊ณต์  ๋…ธ๋…„๊ธฐ๋ฅผ ์˜์œ„ํ•˜๋Š” ๋ฐ์— ๋งค์šฐ ์ค‘์š”ํ•œ ์š”์ธ์ด๋‹ค. ๋…ธํ™”์— ๋”ฐ๋ฅธ ๋ฐฑ์งˆ๋ณ‘๋ณ€์˜ ์ถ•์ ์€ ์ด๋Ÿฌํ•œ ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ์„ ๋น„๋กฏํ•ด ๋…ธ๋…„๊ธฐ ์ธ์ง€๊ธฐ๋Šฅ ๊ฐํ‡ด๋ฅผ ์•ผ๊ธฐํ•˜๋Š” ์œ„ํ—˜์š”์ธ์œผ๋กœ ๋ฐํ˜€์ ธ ์™”๋‹ค. ํ•œํŽธ, ์‚ฌํšŒ์ฐธ์—ฌ๋Š” ๋…ธ๋…„๊ธฐ ์ธ์ง€๊ธฐ๋Šฅ์— ๋Œ€ํ•œ ๋ณดํ˜ธ์š”์ธ์œผ๋กœ ์ง€๋ชฉ๋˜์–ด ํ™œ๋ฐœํ•˜๊ฒŒ ์—ฐ๊ตฌ๋˜์–ด ์™”์œผ๋‚˜, ๋‡Œ ๋ณ‘๋ฆฌ์˜ ์ˆ˜์ค€๊ณผ ์ธ์ง€๊ธฐ๋Šฅ ๊ฐ„์˜ ๊ด€๊ณ„์—์„œ ์‚ฌํšŒ์ฐธ์—ฌ์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ํƒ์ƒ‰ํ•œ ์—ฐ๊ตฌ๋Š” ๋งŽ์ง€ ์•Š๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 163๋ช…์˜ ์ •์ƒ ๋…ธ์ธ์„ ๋Œ€์ƒ์œผ๋กœ ์‹ ๊ฒฝ์‹ฌ๋ฆฌ๊ฒ€์‚ฌ ๋ฐ ์ž๊ธฐ๊ณต๋ช…์˜์ƒ ์ดฌ์˜์„ ์‹ค์‹œํ•˜์—ฌ ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ๊ณผ ๋ฐฑ์งˆ๋ณ‘๋ณ€ ๋ถ€ํ”ผ๋ฅผ ์ธก์ •ํ•˜์˜€๊ณ , ์‚ฌํšŒ์ฐธ์—ฌ์˜ ์–‘์  ์ธก๋ฉด์— ํ•ด๋‹นํ•˜๋Š” ์‚ฌํšŒ์—ฐ๊ฒฐ๋ง ํฌ๊ธฐ, ์ ‘์ด‰ ๋นˆ๋„, ๊ทธ๋ฆฌ๊ณ  ์‚ฌํšŒํ™œ๋™ ์ˆ˜์ค€์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๋…ธํ™”์— ๋”ฐ๋ฅธ ๋ฐฑ์งˆ๋ณ‘๋ณ€ ๋ถ€ํ”ผ์™€ ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ ์‚ฌ์ด์˜ ๊ด€๊ณ„๋ฅผ ์‚ฌํšŒ์ฐธ์—ฌ๊ฐ€ ์กฐ์ ˆํ•˜๋Š”์ง€ ํ™•์ธํ•˜๊ธฐ ์œ„ํ•ด ์กฐ์ ˆ๋œ ๋งค๊ฐœ๋ชจํ˜•์„ ์‚ฌ์šฉํ•˜์—ฌ ๊ฒ€์ฆํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ ๊ฒฐ๊ณผ, ์—ฐ๋ น์— ๋”ฐ๋ฅธ ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ์˜ ๊ฐํ‡ด์—์„œ ๋ฐฑ์งˆ๋ณ‘๋ณ€ ๋ถ€ํ”ผ์˜ ๋งค๊ฐœํšจ๊ณผ๊ฐ€ ์œ ์˜ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์กฐ์ ˆํšจ๊ณผ ๋ถ„์„ ๊ฒฐ๊ณผ, ๋ฐฑ์งˆ๋ณ‘๋ณ€ ๋ถ€ํ”ผ์™€ ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ์˜ ๊ด€๊ณ„๋Š” ์ค‘์š”ํ•œ ๊ด€๊ณ„์— ์žˆ๋Š” ์‚ฌ๋žŒ๊ณผ์˜ ์ ‘์ด‰ ๋นˆ๋„์™€ ์‚ฌํšŒํ™œ๋™ ์ˆ˜์ค€์— ์˜ํ•ด ์กฐ์ ˆ๋˜์—ˆ๋‹ค. ์ ‘์ด‰ ๋นˆ๋„๊ฐ€ ๋†’๊ฑฐ๋‚˜ ์‚ฌํšŒํ™œ๋™ ์ˆ˜์ค€์ด ๋†’์€ ๋…ธ์ธ์ผ์ˆ˜๋ก ๋ฐฑ์งˆ๋ณ‘๋ณ€ ๋ถ€ํ”ผ๊ฐ€ ์ผํ™”๊ธฐ์–ต ๊ธฐ๋Šฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋œ ๋ฐ›๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” ์‚ฌํšŒ์ฐธ์—ฌ๊ฐ€ ๋‡Œ ๋ณ‘๋ฆฌ์˜ ์กด์žฌ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ  ์ธ์ง€๊ธฐ๋Šฅ์„ ์œ ์ง€ํ•  ์ˆ˜ ์žˆ๋„๋ก ๋•๋Š” ์ธ์ง€ ์˜ˆ๋น„๋Šฅ์œผ๋กœ์„œ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.Episodic memory, which shows accelerated decline pattern in old age, predicts instrumental activities of daily living(IADL) well so it is an important factor for independence of daily life and successful aging. White matter hyperintensity volume, which increases according to aging, has been reported as a risk factor for episodic memory decline. Meanwhile, social engagement has been reported as a protective factor for cognitive function in old age and has been actively studied. However, few studies have explored the moderation effect of social engagement in the relationship between the level of brain pathology and cognitive function. In this study, neuropsychological test and magnetic resonance imaging were performed on 163 normal elderly people to measure episodic memory and white matter hyperintensity volume. As measure of quantitative aspect of social engagement, social network size, contact frequency, and social activity level were investigated. The present work performed moderated-mediation analysis to examine the moderating role of social engagement in the relationship between age-related white matter hyperintensity volume and episodic memory. As a result, the relationship between white matter hyperintensity volume and episodic memory was moderated by contact frequency and social activity level. Elderly people with high contact frequency or high level of social activity were found to be less affected by the white matter hyperintensity volume on episodic memory. These results suggests that social engagement may contribute to cognitive reserve that explains the gap between brain states and the level of cognitive function.์„œ ๋ก  1 1. ๋…ธํ™”์™€ ๊ธฐ์–ต ๊ธฐ๋Šฅ ๊ฐํ‡ด 2 2. ๋ฐฑ์งˆ๋ณ‘๋ณ€์˜ ์ถ•์ ๊ณผ ์ธ์ง€๋…ธํ™” 3 3. ์ธ์ง€๋…ธํ™”์˜ ๊ฐœ์ธ์ฐจ์™€ ์ธ์ง€ ์˜ˆ๋น„๋Šฅ 5 4. ๋…ธ๋…„๊ธฐ ์‚ฌํšŒ์ฐธ์—ฌ์™€ ์ธ์ง€๊ธฐ๋Šฅ 7 5. ์—ฐ๊ตฌ ๋ชฉ์  10 ์—ฐ๊ตฌ ๋ฐฉ๋ฒ• 12 1. ์—ฐ๊ตฌ ์ฐธ๊ฐ€์ž 12 2. ์ธก์ • ๋„๊ตฌ 13 3. ๋ถ„์„ ๋ฐฉ๋ฒ• 18 ์—ฐ๊ตฌ ๊ฒฐ๊ณผ 20 ๋…ผ ์˜ 41 ์ฐธ๊ณ ๋ฌธํ—Œ 48 Abstract 56Maste

    Development of electrocardiography system for implantation

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) --์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต,2010.2.Maste

    The Adhesion Properties of CF/PEKK Composites with PEI and PEEK Adhesive Film in Oven Welding Process

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    Recently, there has been an increasing movement in the aerospace industry to increase the use of thermoplastic composites. Moreover, interest in fusion bonding and adhesive bonding is increasing because it can reduce cracking, unlike mechanical bonding. In addition, interest in the oven welding process increases in bonding such thermoplastic composite materials by applying fusion bonding and adhesive bonding. However, there is a lack of research on the adhesion performance when using a thermoplastic polymer as an adhesive for bonding using a thermoplastic composite material as a substrate material. In addition, research about the effect of the interphase on an adhesion properties is insufficient, even though adhesive bonding generate interphase through chemical bonding between a substrate and adhesive. And it is unknown whether applying the vacuum bag only(VBO) method to the oven welding process is appropriate. Therefore, lap shear strength test and fracture surfaces observation through digital optical microscope(Digital OM) and scanning electron microscope(SEM) were performed after bonding by varying the pressing force of carbonfiber/polyetherketoneketone(CF/PEKK) fusion bonding and adhesive bonding using polyetheretherketone(PEEK) and polyetherimide(PEI) as adhesive materials in order to evaluate whether the application of the VBO method is appropriate. In addition, lap shear strength tests were performed at room temperature, 100 and 160 and analysis using Fourier transform infrared spectroscopy(FT-IR) and differential scanning calorimetry(DSC) and fracture surface observation using digital OM and SEM were conducted to understand the adhesion properties of the bonding of PEEK and PEI adhesives having ether and ketone groups. Moreover, the conclusion is as follows. Applying vacuum pressure by the VBO method was inappropriate when bonding CF/PEKK through oven welding process, as the pressing force was too high. In addition, the PEEK and PEI adhesive bonded CF/PEKK showed higher adhesive strength than the fusion bonded CF/PEKK because the increase of molecular formation of ether, ketone and imide constituting the interphase during the adhesive bonding enhance the adhesive strength. Furthermore, at high temperatures, the tendency of the change in strength differed depending on the type of bonding, which was judged to be due to the formation of adhesive layer, respectively different glass transition temperature, and the increased ductility with temperature increase. Therefore, this experiment shows the effect of adhesive application having similar chemical structure with thermoplastic of substrate material on the adhesive strength enhancement. In addition, it was suggested that it is essential to consider the glass transition temperature of the adhesive when using the structure to which adhesive bonding is applied in a high temperature environment.1. ์„œ๋ก  1 1.1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ 1 1.2. ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ๊ณผ ๋ชฉ์  3 2. ์žฌ๋ฃŒ ๋ฐ ์‹คํ—˜ ๋ฐฉ๋ฒ• 5 2.1. ์‹คํ—˜ ์žฌ๋ฃŒ 5 2.1.1. ํƒ„์†Œ์„ฌ์œ  5 2.1.2. Polyetherketoneketone(ํด๋ฆฌ์—ํ…Œ๋ฅด์ผ€ํ†ค์ผ€ํ†ค, PEKK) 7 2.1.3. Polyetheretherketone(ํด๋ฆฌ์—ํ…Œ๋ฅด์—ํ…Œ๋ฅด์ผ€ํ†ค, PEEK) 9 2.1.4. Polyetherimide(ํด๋ฆฌ์—ํ…Œ๋ฅด์ด๋ฏธ๋“œ, PEI) 11 2.2 ์ œ์ž‘ ๊ณต์ • 13 2.2.1. ์ง„๊ณต ๋ฐฑ ์„ฑํ˜• ๊ณต์ •(Vacuum bag only process, VBO process) 13 2.2.2. ์˜ค๋ธ ์ ‘ํ•ฉ ๊ณต์ •(Oven welding process) 16 2.2.3. ํ•ซ ํ”„๋ ˆ์Šค ๊ณต์ •(Hot press process) 20 2.3 ํŠน์„ฑ ๋ถ„์„ ์‹œํ—˜ 23 2.3.1. Lap ์ „๋‹จ ๊ฐ•๋„ ์‹œํ—˜(Lap shear strength test, LSS test) 23 2.3.2. ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜ ์ ์™ธ์„  ๋ถ„๊ด‘๋ฒ•(Fourier transform infrared spectroscopy, FT-IR) 30 2.3.3. ์‹œ์ฐจ ์ฃผ์‚ฌ ์—ด๋Ÿ‰๋ฒ•(Differential scanning calorimetry, DSC) 32 2.4 ํŒŒ๋‹จ๋ฉด ๊ด€์ฐฐ 34 2.4.1. ๋””์ง€ํ„ธ ๊ด‘ํ•™ ํ˜„๋ฏธ๊ฒฝ(Digital optical microscope) 34 2.4.2. ์ฃผ์‚ฌ ์ „์ž ํ˜„๋ฏธ๊ฒฝ(Scanning electron microscope, SEM) 36 3. ์‹คํ—˜ ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 38 3.1. Lap ์ „๋‹จ ๊ฐ•๋„ ์‹œํ—˜(LSS test) ๊ฒฐ๊ณผ 38 3.1.1. ์‹ค์˜จ Lap ์ „๋‹จ ๊ฐ•๋„ ์‹œํ—˜ ๊ฒฐ๊ณผ 38 3.1.2. ๊ณ ์˜จ ํ™˜๊ฒฝ Lap ์ „๋‹จ ๊ฐ•๋„ ์‹œํ—˜ ๊ฒฐ๊ณผ 40 3.2. ํ‘ธ๋ฆฌ์— ๋ณ€ํ™˜ ์ ์™ธ์„  ๋ถ„๊ด‘๋ฒ•(FT-IR) ๊ฒฐ๊ณผ 41 3.3. ์‹œ์ฐจ ์ฃผ์‚ฌ ์—ด๋Ÿ‰๋ฒ•(DSC) ์‹œํ—˜ ๊ฒฐ๊ณผ 45 3.4. ํŒŒ๋‹จ๋ฉด ๊ด€์ฐฐ ๊ฒฐ๊ณผ 47 3.4.1. ๋””์ง€ํ„ธ ๊ด‘ํ•™ ํ˜„๋ฏธ๊ฒฝ(Digital optical microscope) ํŒŒ๋‹จ๋ฉด ๊ด€์ฐฐ ๊ฒฐ๊ณผ 47 3.4.2. ์ฃผ์‚ฌ ์ „์ž ํ˜„๋ฏธ๊ฒฝ(SEM) ํŒŒ๋‹จ๋ฉด ๊ด€์ฐฐ ๊ฒฐ๊ณผ 63 3.5. ๊ณ ์ฐฐ 71 4. ๊ฒฐ๋ก  75 ์ฐธ๊ณ ๋ฌธํ—Œ 78 ๊ตญ๋ฌธ์ดˆ๋ก 83 ๊ฐ์‚ฌ์˜ ๊ธ€ 86Maste

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์–ธ๋ก ์ •๋ณดํ•™๊ณผ,2005.Maste
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