6 citations · 9 across the 2 of their papers we have counts for
3 papers
cs.DC2026
Heddle: Learning Structural Templates for Parallelism Planning on Heterogeneous GPU Clusters
Taeyoon Kim, Yonguk Song, Seoyeong Choy +4
Training large machine learning models on shared GPU infrastructures faces two challenges: (1) GPU availability shifts dynamically with varying resource demands from tenants, and (…
cs.DC2021★ 3 cited
Efficient Strong Scaling Through Burst Parallel Training
Seo Jin Park, Joshua Fried, Sunghyun Kim +2
As emerging deep neural network (DNN) models continue to grow in size, using large GPU clusters to train DNNs is becoming an essential requirement to achieving acceptable training…
cs.DC2017★ 6 cited
Exploiting Commutativity For Practical Fast Replication
Seo Jin Park, John Ousterhout
Traditional approaches to replication require client requests to be ordered before making them durable by copying them to replicas. As a result, clients must wait for two round-tri…