961 citations · 1.4k across the 33 of their papers we have counts for
3 papers · 1 filter
Mirage: Towards Low-interruption Services on Batch GPU Clusters with Reinforcement Learning
Qiyang Ding, Pengfei Zheng, Shreyas Kudari +2
Accommodating long-running deep learning (DL) training and inference jobs is challenging on GPU clusters that use traditional batch schedulers, such as Slurm. Given fixed wall cloc…
From FASTER to F2: Evolving Concurrent Key-Value Store Designs for Large Skewed Workloads
Konstantinos Kanellis, Badrish Chandramouli, Ted Hart +1
Modern large-scale services such as search engines, messaging platforms, and serverless functions, rely on key-value (KV) stores to maintain high performance at scale. When such se…
Does compressing activations help model parallel training?
Song Bian, Dacheng Li, Hongyi Wang +2
Large-scale Transformer models are known for their exceptional performance in a range of tasks, but training them can be difficult due to the requirement for communication-intensiv…