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Daegun Yoon
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2020 – today
- 2024
- [i4]Daegun Yoon, Sangyoon Oh:
Preserving Near-Optimal Gradient Sparsification Cost for Scalable Distributed Deep Learning. CoRR abs/2402.13781 (2024) - 2023
- [j7]Daegun Yoon
, Minjoong Jeong, Sangyoon Oh
:
WAVE: designing a heuristics-based three-way breadth-first search on GPUs. J. Supercomput. 79(6): 6889-6917 (2023) - [j6]Daegun Yoon
, Minjoong Jeong, Sangyoon Oh:
SAGE: toward on-the-fly gradient compression ratio scaling. J. Supercomput. 79(10): 11387-11409 (2023) - [c4]Daegun Yoon, Sangyoon Oh:
MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training. HiPC 2023: 87-96 - [c3]Daegun Yoon
, Sangyoon Oh
:
DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification. ICPP 2023: 746-755 - [c2]Seungjun Lee, Miri Yu, Daegun Yoon, Sangyoon Oh:
Can hierarchical client clustering mitigate the data heterogeneity effect in federated learning? IPDPS Workshops 2023: 799-808 - [i3]Daegun Yoon, Sangyoon Oh:
DEFT: Exploiting Gradient Norm Difference between Model Layers for Scalable Gradient Sparsification. CoRR abs/2307.03500 (2023) - [i2]Daegun Yoon, Sangyoon Oh:
MiCRO: Near-Zero Cost Gradient Sparsification for Scaling and Accelerating Distributed DNN Training. CoRR abs/2310.00967 (2023) - 2022
- [j5]Juwon Park, Daegun Yoon, Sangho Yeo
, Sangyoon Oh
:
AMBLE: Adjusting mini-batch and local epoch for federated learning with heterogeneous devices. J. Parallel Distributed Comput. 170: 13-23 (2022) - [j4]Daegun Yoon
, Sangyoon Oh
:
SURF: Direction-Optimizing Breadth-First Search Using Workload State on GPUs. Sensors 22(13): 4899 (2022) - [i1]Daegun Yoon, Sangyoon Oh:
Empirical Analysis on Top-k Gradient Sparsification for Distributed Deep Learning in a Supercomputing Environment. CoRR abs/2209.08497 (2022) - 2021
- [j3]Daegun Yoon
, Gyudong Park, Sangyoon Oh
:
Exploring a system architecture of content-based publish/subscribe system for efficient on-the-fly data dissemination. Concurr. Comput. Pract. Exp. 33(18) (2021) - [j2]Seungjun Lee
, Daegun Yoon
, Sangho Yeo
, Sangyoon Oh
:
Mitigating Cold Start Problem in Serverless Computing with Function Fusion. Sensors 21(24): 8416 (2021) - [j1]Daegun Yoon
, Zhetao Li, Sangyoon Oh
:
Balanced content space partitioning for pub/sub: a study on impact of varying partitioning granularity. J. Supercomput. 77(12): 13676-13702 (2021) - 2020
- [c1]Daegun Yoon, Gyudong Park, Sangyoon Oh:
CPartition: a Correlation-Based Space Partitioning for Content-Based Publish/Subscribe Systems with Skewed Workload. BigComp 2020: 377-384
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