엄형상 교수
Hyeonsang Eom
서울대학교 컴퓨터공학부 · 컴퓨터과학
연구실 소개
엄형상 교수의 연구실은 고성능 스토리지 시스템과 네트워크 라우팅 기반의 시스템 소프트웨어 최적화를 핵심으로 합니다. 특히 플래시 메모리의 병렬성과 성능을 극대화하기 위한 순서 없는 컨트롤러 설계, 그리고 현대 저장장치의 잠재력을 최대로 발휘할 수 있도록 운영체제 소프트웨어 오버헤드를 최소화하는 기술 개발에 주력하고 있습니다. 또한, 응용 프로그램 성능 예측 및 분석을 위한 고속 시뮬레이터 설계와, 미래 지연을 고려한 지능형 라우팅 기법 등 시스템 수준의 성능 향상 기법을 연구하고 있습니다.
연구 현황
연구 성과 추이
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
주요 논문
15Ozone (O3) is a flash memory controller that increases the performance of a flash storage system by executing multiple flash operations out of order. In the O3 flash controller, data dependencies are the only ordering constraints on the execution of multiple flash operations. This allows O3 to exploit the multichip parallelism inherent in flash memory much more effectively than interleaving. The O3 controller also provides a prioritized handling of flash operations, equipping flash management so
Fast storage devices are an emerging solution to satisfy data-intensive applications. They provide high transaction rates for DBMS, low response times for Web servers, instant on-demand paging for applications with large memory footprints, and many similar advantages for performance-hungry applications. In spite of the benefits promised by fast hardware, modern operating systems are not yet structured to take advantage of the hardware’s full potential. The software overhead caused by an OS, negl
This paper presents a family of simulators that have been developed for data-intensive applications, and a methodology to select the most efficient one based on a user-supplied requirement for accuracy. The methodology consists of a series of tests that select an appropriate simulation based on the attributes of the application. In addition, each simulator provides two estimates of application execution time: one for the minimum expected time and the other for the maximum. We present the results
We introduce a new performance metric, called Load Balancing Factor (LBF), to assist programmers with evaluating different tuning alternatives. The LBF metric differs from traditional performance metrics since it is intended to measure the performance implications of a specific tuning alternative rather than quantifying where time is spent in the current version of the program. A second unique aspect of the metric is that it provides guidance about moving work within a distributed or parallel pr
In link-state routing, routes are determined based on estimates of the current delays on the links.Ideally, a data packet should be routed based on the delays it will encounter at each link of the path at the time the packet gets to the link.To address this issue, we have developed a new approach that improves link-state routing by estimating and using the future link delays encountered by data packets.In link-state routing, link-delay estimates are periodically flooded throughout the network.Th
In link-state routing, routes are determined based on the estimates of the current delays on the links, i.e. without considering the dynamics of the link-delay information. Ideally, a data packet should be routed based on the delays it will encounter at each link of the path at the time the packet gets to the link. To address this issue, we have designed a new routing software that improves link-state routing by estimating and using the future link delays encountered by data packets. In link-sta
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