1 citations · 1 across the 6 of their papers we have counts for
6 papers
Accurate Simulation of Distributed Training Jobs with Network Contention Modeling
Yeonho Yoo, Hyunho Lee, Hyunmok Choi +2
Trace-driven simulation is widely used to evaluate distributed training (DT) jobs in GPU clusters, but existing simulators either ignore network contention or approximate it with a…
Parallelism Strategy Chaining for Fast Training Convergence
Minchul Kang, Changyong Shin, Younghun Go +4
Selecting a parallelism strategy - the configuration of data, tensor, and pipeline parallelism degrees together with micro- and global-batch sizes - largely determines the training…
Training Time Prediction for Mixed Precision-based Distributed Training
Minchul Kang, Changyong Shin, Jinwoo Jeong +5
Accurate prediction of training time in distributed deep learning is crucial for resource allocation, cost estimation, and job scheduling. We observe that the floating-point precis…
GPU Memory Prediction for Multimodal Model Training
Jinwoo Jeong, Minchul Kang, Younghun Go +5
As deep learning models in agentic AI systems grow in scale and complexity, GPU memory requirements increase and often exceed the available GPU memory capacity, so that out-of-memo…
On the Importance of Feature Decorrelation for Unsupervised Representation Learning in Reinforcement Learning
Hojoon Lee, Koanho Lee, Dongyoon Hwang +3
Recently, unsupervised representation learning (URL) has improved the sample efficiency of Reinforcement Learning (RL) by pretraining a model from a large unlabeled dataset. The un…
JORLDY: a fully customizable open source framework for reinforcement learning
Kyushik Min, Hyunho Lee, Kwansu Shin +4
Recently, Reinforcement Learning (RL) has been actively researched in both academic and industrial fields. However, there exist only a few RL frameworks which are developed for res…