3 citations · 3 across the 4 of their papers we have counts for
4 papers
Flare: Anomaly Diagnostics for Divergent LLM Training in GPU Clusters of Thousand-Plus Scale
Weihao Cui, Ji Zhang, Han Zhao +5
The rapid proliferation of large language models has driven the need for efficient GPU training clusters. However, it is challenging due to the frequent occurrence of training anom…
Couler: Unified Machine Learning Workflow Optimization in Cloud
Xiaoda Wang, Yuan Tang, Tengda Guo +6
Machine Learning (ML) has become ubiquitous, fueling data-driven applications across various organizations. Contrary to the traditional perception of ML in research, ML workflows c…
mLoRA: Fine-Tuning LoRA Adapters via Highly-Efficient Pipeline Parallelism in Multiple GPUs
Zhengmao Ye, Dengchun Li, Zetao Hu +8
Transformer-based, pre-trained large language models (LLMs) have demonstrated outstanding performance across diverse domains, particularly in the emerging {\em pretrain-then-finetu…
DLRover-RM: Resource Optimization for Deep Recommendation Models Training in the Cloud
Qinlong Wang, Tingfeng Lan, Yinghao Tang +8
Deep learning recommendation models (DLRM) rely on large embedding tables to manage categorical sparse features. Expanding such embedding tables can significantly enhance model per…