Efficient Large-Scale Deep Learning Framework for Heterogeneous Multi-GPU Cluster. - Youngrang Kim; Hyeonseong Choi; Jaehwan Lee 001; Jik-Soo Kim; Hyunseung Jei; Hongchan Roh (FAS*W@SASO/ICAC, 2019)

Towards an optimized distributed deep learning framework for a heterogeneous multi-GPU cluster. - Youngrang Kim; Hyeonseong Choi; Jaehwan Lee 001; Jik-Soo Kim; Hyunseung Jei; Hongchan Roh (Clust. Comput., 2020)

Diagnostic Assessment Generation via Combinatorial Search. - Daehan Kim; Hyeonseong Choi; Guik Jung (CoRR, 2021)

Empirical Performance Evaluation of Communication Libraries for Multi-GPU based Distributed Deep Learning in a Container Environment. - Hyeonseong Choi; Youngrang Kim; Jaehwan Lee 001; Yoonhee Kim (KSII Trans. Internet Inf. Syst., 2021)

Efficient Data-parallel Distributed DNN Training for Big Dataset under Heterogeneous GPU Cluster. - Shinyoung Ahn; Sookwang Lee; Hyeonseong Choi; Jaehyun Lee (IEEE Big Data, 2024)

EDDIS: Accelerating Distributed Data-Parallel DNN Training for Heterogeneous GPU Cluster. - Shinyoung Ahn; Hooyoung Ahn; Hyeonseong Choi; Jaehyun Lee (IPDPS (Workshops), 2024)

FLASH: Fast All-to-All Communication in GPU Clusters. - Yiran Lei; Dongjoo Lee; Liangyu Zhao; Daniar Kurniawan; Chanmyeong Kim; Heetaek Jeong; Changsu Kim 004; Hyeonseong Choi; Liangcheng Yu; Arvind Krishnamurthy; Justine Sherry; Eriko Nurvitadhi (CoRR, 2025)