대용량 데이터처리 시스템 연구실

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대용량 데이터처리 시스템 연구실

OMNIA is doing cutting-edge research on large-scale systems. Our research interests span distributed systems, data analytics engines, computer architecture, and applied machine learning. Our research goal is to advance the state of the art in emerging large-scale computing platforms by making them more efficient, responsive, intelligent and programmable. Our current research topics at UNIST are on the following areas.

1. Systems + AI: We build systems support for improving machine learning frameworks and prediction-serving systems, as well as leverage machine learning in producing intelligent system software.
2. Big data analytics: We build data processing pipelines for real-time big data analytics at cloud/IoT scale that enable system operators to promptly troubleshoot system anomalies, improving performance and reliability of their services.
3. Systems for new HW: We produce substantially better system software in the face of the recent explosion of hardware features and heterogeneity, such as accelerators and processor/memory tailored to improve efficiency, density, performance predictability.

Major research field

General area: systems, parallel and distributed computing, and applied machine learning. Recent topics: distributed stream processing, GPU cluster res

Desired field of research

systems, parallel and distributed computing, applied machine learning

Research Keywords and Topics

systems, parallel and distributed computing, applied machine learning

- GPU resource management
- AI hardware design
- On-device AI systems
- Real-time big data processing systems
- Large-scale monitoring systems

Research Publications

USENIX ATC, Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads, Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang, 2019
ASPLOS, StreamBox-HBM: Stream Analytics on High Bandwidth Hybrid Memory, Hongyu Miao, Myeongjae Jeon, Gennady Pekhimenko, Kathryn S. McKinley, Felix Xiaozhu Lin, 2019
NSDI, Tiresias: A GPU Cluster Manager for Distributed Deep Learning, Juncheng Gu, Mosharaf Chowdhury, Kang G. Shin, Yibo Zhu, Myeongjae Jeon, Junjie Qian, Hongqiang Liu, Chuanxiong Guo, 2019


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