CPartition: a Correlation-Based Space Partitioning for Content-Based Publish/Subscribe Systems with Skewed Workload. - Daegun Yoon; Gyudong Park; Sangyoon Oh 001 (BigComp, 2020)

Exploring a system architecture of content-based publish/subscribe system for efficient on-the-fly data dissemination. - Daegun Yoon; Gyudong Park; Sangyoon Oh 001 (Concurr. Comput. Pract. Exp., 2021)

Mitigating Cold Start Problem in Serverless Computing with Function Fusion. - Seungjun Lee; Daegun Yoon; Sangho Yeo; Sangyoon Oh 001 (Sensors, 2021)

Balanced content space partitioning for pub/sub: a study on impact of varying partitioning granularity. - Daegun Yoon; Zhetao Li; Sangyoon Oh 001 (J. Supercomput., 2021)

Empirical Analysis on Top-k Gradient Sparsification for Distributed Deep Learning in a Supercomputing Environment. - Daegun Yoon; Sangyoon Oh 001 (CoRR, 2022)

AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices. - Juwon Park; Daegun Yoon; Sangho Yeo; Sangyoon Oh 001 (J. Parallel Distributed Comput., 2022)

SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs. - Daegun Yoon; Sangyoon Oh 001 (Sensors, 2022)

MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training. - Daegun Yoon; Sangyoon Oh 001 (HiPC, 2023)

DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification. - Daegun Yoon; Sangyoon Oh 001 (ICPP, 2023)

Can hierarchical client clustering mitigate the data heterogeneity effect in federated learning? - Seungjun Lee; Miri Yu; Daegun Yoon; Sangyoon Oh 001 (IPDPS Workshops, 2023)

WAVE: designing a heuristics-based three-way breadth-first search on GPUs. - Daegun Yoon; Minjoong Jeong; Sangyoon Oh 001 (J. Supercomput., 2023)

SAGE: toward on-the-fly gradient compression ratio scaling. - Daegun Yoon; Minjoong Jeong; Sangyoon Oh 001 (J. Supercomput., 2023)

Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning. - Daegun Yoon; Sangyoon Oh 001 (CCGrid, 2024)

HPU: High-Bandwidth Processing Unit for Scalable, Cost-effective LLM Inference via GPU Co-processing. - Myunghyun Rhee; Joonseop Sim; Taeyoung Ahn; Seungyong Lee; Daegun Yoon; Euiseok Kim; Kyoung Park; Youngpyo Joo; Hosik Kim (CoRR, 2025)