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Yoongu Kim
Researcher at Carnegie Mellon University
Publications - 29
Citations - 5313
Yoongu Kim is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Dram & Memory controller. The author has an hindex of 18, co-authored 28 publications receiving 4602 citations.
Papers
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Journal ArticleDOI
Flipping bits in memory without accessing them: an experimental study of DRAM disturbance errors
Yoongu Kim,Ross Daly,Jeremie S. Kim,Chris Fallin,Ji-Hye Lee,Donghyuk Lee,Christopher B. Wilkerson,Konrad K. Lai,Onur Mutlu +8 more
TL;DR: This paper exposes the vulnerability of commodity DRAM chips to disturbance errors, and shows that it is possible to corrupt data in nearby addresses by reading from the same address in DRAM by activating the same row inDRAM.
Journal ArticleDOI
Ramulator: A Fast and Extensible DRAM Simulator
TL;DR: This paper presents Ramulator, a fast and cycle-accurate DRAM simulator that is built from the ground up for extensibility, and is able to provide out-of-the-box support for a wide array of DRAM standards.
Proceedings ArticleDOI
ATLAS: A scalable and high-performance scheduling algorithm for multiple memory controllers
TL;DR: It is shown that the implementation of least-attained-service thread prioritization reduces the time the cores spend stalling and significantly improves system throughput, and ATLAS's performance benefit increases as the number of cores increases.
Proceedings ArticleDOI
RowClone: fast and energy-efficient in-DRAM bulk data copy and initialization
Vivek Seshadri,Yoongu Kim,Chris Fallin,Donghyuk Lee,Rachata Ausavarungnirun,Gennady Pekhimenko,Yixin Luo,Onur Mutlu,Phillip B. Gibbons,Michael Kozuch,Todd C. Mowry +10 more
TL;DR: RowClone is proposed, a new and simple mechanism to perform bulk copy and initialization completely within DRAM — eliminating the need to transfer any data over the memory channel to perform such operations.
Proceedings ArticleDOI
Thread Cluster Memory Scheduling: Exploiting Differences in Memory Access Behavior
TL;DR: This paper presents a new memory scheduling algorithm that addresses system throughput and fairness separately with the goal of achieving the best of both, and evaluates TCM on a wide variety of multiprogrammed workloads and compares its performance to four previously proposed scheduling algorithms, finding that TCM achieves both the best system throughputand fairness.