• CCL is accepted to appear in EuroSys 2026!

    "a novel Carbon-footprint-aware Continuous Learning (CCL) scheme that minimizes carbon emissions during model retraining without sacrificing inference accuracy."

  • Garen is accepted to appear in EuroSys 2026!

    "a system implementing a concept called atomic state reconciliation (ASR), which ensures atomicity and consistency of reconciliation to protect the cluster against state inconsistencies."

  • Taeyoon is awarded Best Poster by KIISE for our work FusionFlow!

  • Xinyue Ma is accepted to the intern program at Microsoft Research Redmond!

    She will work under the RiSE group in MSR Redmond for a 3-month internship program from September.

  • “FusionFlow" is accepted to appear in VLDB 2024!

    "orchestrating data preprocessing tasks across CPUs and GPUs while minimizing interference with GPU-based model training"

Our Research

Our research goal is to advance the state of the art in emerging large-scale system platforms by making them more efficient, responsive, intelligent, and programmable. Our current research topics lie in the following areas:

Systems and 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.

Bigdata 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 the performance and reliability of their services.

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, and performance predictability.

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Selected Publications

FusionFlow: Accelerating Data Preprocessing for Machine Learning with CPU-GPU Cooperation

Taeyoon Kim, Chanho Park, Mansur Mukimbekov, Heelim Hong, Minseok Kim, Ze Jin, Changdae Kim, Ji-Yong Shin, Myeongjae Jeon

Cost-effective On-device Continual Learning over Memory Hierarchy with Miro

Xinyue Ma, Suyeon Jeong, Minjia Zhang, Di Wang, Jonghyun Choi, Myeongjae Jeon

CarM: Hierarchical Episodic Memory for Continual Learning

Soobee Lee, Minindu Weerakoon, Jonghyun Choi, Minjia Zhang, Di Wang, Myeongjae Jeon

Jarvis: Large-scale Server Monitoring with Adaptive Near-data Processing

Atul Sandur, ChanHo Park, Stavros Volos, Gul Agha, Myeongjae Jeon

Zico: Efficient GPU Memory Sharing for Concurrent DNN Training

Gangmuk Lim, Jeongseob Ahn, Wencong Xiao, Youngjin Kwon, Myeongjae Jeon

Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads

Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, Junjie Qian, Wencong Xiao, Fan Yang