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Gyeong-In Yu
Gyeong-In Yu
其他姓名Gyeongin Yu
FriendliAI
在 friendli.ai 的电子邮件经过验证 - 首页
标题
引用次数
引用次数
年份
Orca: A Distributed Serving System for Transformer-Based Generative Models
GI Yu, JS Jeong, GW Kim, S Kim, BG Chun
16th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2022
2292022
Parallax: Sparsity-aware Data Parallel Training of Deep Neural Networks
S Kim, GI Yu, H Park, S Cho, E Jeong, H Ha, S Lee, JS Jeong, BG Chun
Proceedings of the Fourteenth EuroSys Conference 2019, 1-15, 2019
1052019
Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning
W Kwon, GI Yu, E Jeong, BG Chun
Advances in Neural Information Processing Systems 33, 2020
652020
A Tensor Compiler for Unified Machine Learning Prediction Serving
S Nakandala, K Saur, GI Yu, K Karanasos, C Curino, M Weimer, ...
14th {USENIX} Symposium on Operating Systems Design and Implementation …, 2020
562020
JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
E Jeong, S Cho, GI Yu, JS Jeong, DJ Shin, BG Chun
16th {USENIX} Symposium on Networked Systems Design and Implementation …, 2019
292019
Dolphin: Runtime Optimization for Distributed Machine Learning
BG Chun, B Cho, B Jeon, JS Jeong, G Kim, JY Kim, WY Lee, YS Lee, ...
ML Systems Workshop at ICML, 2016
21*2016
Bpipe: Memory-balanced pipeline parallelism for training large language models
T Kim, H Kim, GI Yu, BG Chun
International Conference on Machine Learning, 16639-16653, 2023
172023
Automating System Configuration of Distributed Machine Learning
WY Lee, Y Lee, JS Jeong, GI Yu, JY Kim, HJ Park, B Jeon, W Song, G Kim, ...
2019 IEEE 39th International Conference on Distributed Computing Systems …, 2019
172019
Improving the expressiveness of deep learning frameworks with recursion
E Jeong, JS Jeong, S Kim, GI Yu, BG Chun
Proceedings of the Thirteenth EuroSys Conference, 1-13, 2018
172018
Speculative Symbolic Graph Execution of Imperative Deep Learning Programs
E Jeong, S Cho, GI Yu, JS Jeong, DJ Shin, T Kim, BG Chun
ACM SIGOPS Operating Systems Review 53 (1), 26-33, 2019
112019
WindTunnel: towards differentiable ML pipelines beyond a single model
GI Yu, S Amizadeh, S Kim, A Pagnoni, C Zhang, BG Chun, M Weimer, ...
Proceedings of the VLDB Endowment 15 (1), 11-20, 2021
82021
Accelerating Multi-Model Inference by Merging DNNs of Different Weights
JS Jeong, S Kim, GI Yu, Y Lee, BG Chun
arXiv preprint arXiv:2009.13062, 2020
72020
Making Classical Machine Learning Pipelines Differentiable: A Neural Translation Approach
GI Yu, S Amizadeh, BG Chun, M Weimer, M Interlandi
Workshop on Systems for ML and Open Source Software at NeurIPS 2018, 2018
62018
Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs
T Kim, E Jeong, GW Kim, Y Koo, S Kim, GI Yu, BG Chun
Advances in Neural Information Processing Systems 34, 2021
52021
Compiling Classical ML Pipelines into Tensor Computations for One-size-fits-all Prediction Serving
S Nakandala, GI Yu, M Weimer, M Interlandi
Systems for ML workshop at NeurIPS, 2019
52019
Dynamic batching for inference system for transformer-based generation tasks
G Yu, G Kim, JS Jeong, S Kim, B Chun
US Patent 11,442,775, 2022
42022
Stage-based Hyper-parameter Optimization for Deep Learning
A Shin, DJ Shin, S Cho, DY Kim, E Jeong, GI Yu, BG Chun
arXiv preprint arXiv:1911.10504, 2019
42019
Auto-Parallelizing Deep Learning for Multi-machine, Multi-GPU Environments
S Kim, E Jeong, JS Jeong, H Park, GI Yu, BG Chun
Workshop on AI Systems at Symposium on Operating Systems Principles (SOSP), 2017
22017
Taming Model Serving Complexity, Performance and Cost: A Compilation to Tensor Computations Approach
S Nakandala, K Saur, GI Yu, K Karanasos, C Curino, M Weimer, ...
2
Demonstration of JANUS: Fast and Flexible Deep Learning via Symbolic Graph Execution of Imperative Programs
E Jeong, S Cho, GI Yu, JS Jeong, DJ Shin, BG Chun
SysML Conference, Stanford, CA, USA, Mar, 0
1
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