Intelligent System Software Lab

지능형 시스템 소프트웨어 연구실

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지능형 시스템 소프트웨어 연구실

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.

Major research field

System software for machine learning, ML-augmented system software, scalable parallel and distributed computing, computer systems security

Desired field of research

System software for machine learning, ML-augmented system software, scalable parallel and distributed computing, computer systems security, system software for emerging memory systems

Research Keywords and Topics

1. System Software for High-Performance and Efficient Machine Learning
- Characterizing and optimizing the machine-learning frameworks using high-performance accelerators
- Resource management for large-scale distributed systems for high-performance machine learning

2. Machine Learning-Augmented System Software
- Improving the efficiency of parallel and distributed task schedulers and resource managers using machine learning
- Machine learning-augmented dynamic data placement and migration techniques

Research Publications
MORE

- Myeonggyun Han, Jihoon Hyun, Seongbeom Park, and Woongki Baek, “Hotness- and Lifetime-Aware Data Placement and Migration for High-Performance Deep Learning on Heterogeneous Memory Systems,” in the IEEE Transactions on Computers (TC), 2020.

- Myeonggyun Han, Jihoon Hyun, Seongbeom Park, Jinsu Park, and Woongki Baek, “MOSAIC: Heterogeneity-, Communication-, and Constraint-Aware Model Slicing and Execution for Accurate and Efficient Inference,” in the Proceedings of the 28th International Conference on Parallel Architectures and Compilation Techniques (PACT), Sep. 2019.

- Jinsu Park, Seongbeom Park, and Woongki Baek, “CoPart: Coordinated Partitioning of Last-Level Cache and Memory Bandwidth for Fairness-Aware Workload Consolidation on Commodity Servers,” in the Proceedings of the 14th European Conference on Computer Systems (EuroSys), Mar. 2019.

Patents

- Lightweight architecture for aliased memory operations, Woongki Baek and Seung Hoe Kim, US Patent 10,223,261, 2019

- Reliability-aware application scheduling, Woongki Baek et al., US Patent 9,436,517, 2016

국가과학기술표준분류

  • EE. 정보/통신
  • EE02. 소프트웨어
  • EE0203. System Integration

국가기술지도분류

  • 정보-지식-지능화 사회 구현
  • 011300. 차세대 정보시스템기술

녹색기술분류

  • 고효율화기술
  • 전력효율성 향상
  • 323. 그린 IT기술

6T분류

  • IT 분야
  • 정보처리 시스템 및 S/W
  • 010316. 기타 정보처리시스템 및 S/W 기술