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  • Scopus

Baek, Woongki (백웅기)

Department
Department of Computer Science and Engineering(컴퓨터공학과)
Website
https://sites.google.com/site/woongkibaek/
Lab
Intelligent System Software Lab. (지능형 시스템 소프트웨어 연구실)
Research Keywords
시스템 소프트웨어, 기계학습, 시스템 보안, 병렬 컴퓨팅, 분산 컴퓨팅, System Software, Machine Learning, ML-augmented system software, scalable parallel and distributed computing, computer systems security
Research Interests
Intelligent System Software Lab (ISSL) investigates innovative system software techniques that significantly improve the performance, efficiency, security, and reliability of computer systems. ISSL takes a vertically integrated research approach to maximize the synergistic effects across the entire computer system hierarchy including computer architecture, system software, runtimes, and applications. Currently, ISSL focuses on the following research projects – (1) system software for high-performance and efficient machine learning, (2) machine learning-augmented system software, (3) scalable and efficient parallel and distributed computing, and (4) computer systems security.
This table browses all dspace content
Issue DateTitleAuthor(s)TypeViewAltmetrics
2022-07SDRP: Safe, Efficient, and SLO-Aware Workload Consolidation through Secure and Dynamic Resource PartitioningHan, Myeonggyun; Baek, WoongkiARTICLE1848 SDRP: Safe, Efficient, and SLO-Aware Workload Consolidation through Secure and Dynamic Resource Partitioning
2021-09Holistic VM Placement for Distributed Parallel Applications in Heterogeneous ClustersKim, Seontae; Pham, Nguyen; Baek, Woongki, et alARTICLE1652 Holistic VM Placement for Distributed Parallel Applications in Heterogeneous Clusters
2021-05Design and Implementation of a Criticality- and Heterogeneity-Aware Runtime System for Task-Parallel ApplicationsHan, Myeonggyun; Park, Jinsu; Baek, WoongkiARTICLE1197 Design and Implementation of a Criticality- and Heterogeneity-Aware Runtime System for Task-Parallel Applications
2020-03Hotness- and Lifetime-Aware Data Placement and Migration for High-Performance Deep Learning on Heterogeneous Memory SystemsHan, Myeonggyun; Hyun, Jihoon; Park, Seongbeom, et alARTICLE671 Hotness- and Lifetime-Aware Data Placement and Migration for High-Performance Deep Learning on Heterogeneous Memory Systems
2019-05Analyzing and optimizing the performance and energy efficiency of transactional scientific applications on large-scale NUMA systems with HTM supportPark, Jinsu; Baek, WoongkiARTICLE1465 Analyzing and optimizing the performance and energy efficiency of transactional scientific applications on large-scale NUMA systems with HTM support
2019-03Improving the Performance and Energy Efficiency of GPGPU Computing through Integrated Adaptive Cache ManagementKim, Kyu Yeun; Park, Jinsu; Baek, WoongkiARTICLE984 Improving the Performance and Energy Efficiency of GPGPU Computing through Integrated Adaptive Cache Management
2016-06Quantifying the performance and energy efficiency of advanced cache indexing for GPGPU computingKim, Kyu Yeun; Baek, WoongkiARTICLE1372 Quantifying the performance and energy efficiency of advanced cache indexing for GPGPU computing
2015-02NVRAM Persistency 모델에 따른 In-Memory Key-Value 스토어 구현에 관한 서베이Kim, Wookhee; Baek, Woongki; Nam, BeomseokARTICLE2080

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