101 citations · 155 across the 7 of their papers we have counts for
7 papers
Toward Co-adapting Machine Learning Job Shape and Cluster Topology
Shawn Shuoshuo Chen, Daiyaan Arfeen, Minlan Yu +2
Allocating resources to distributed machine learning jobs in multi-tenant torus-topology clusters must meet each job's specific placement and communication requirements, which are…
Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training
Daiyaan Arfeen, Dheevatsa Mudigere, Ankit More +3
LLM training is scaled up to 10Ks of GPUs by a mix of data-(DP) and model-parallel (MP) execution. Critical to achieving efficiency is tensor-parallel (TP; a form of MP) execution…
PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
Daiyaan Arfeen, Zhen Zhang, Xinwei Fu +2
Training Deep Neural Networks (DNNs) with billions of parameters generally involves pipeline-parallel (PP) execution. Unfortunately, PP model training can use GPUs inefficiently, e…
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
Byungsoo Jeon, Mengdi Wu, Shiyi Cao +11
Deep neural networks (DNNs) continue to grow rapidly in size, making them infeasible to train on a single device. Pipeline parallelism is commonly used in existing DNN systems to s…
SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification
Xupeng Miao, Gabriele Oliaro, Zhihao Zhang +12
This paper introduces SpecInfer, a system that accelerates generative large language model (LLM) serving with tree-based speculative inference and verification. The key idea behind…
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
Zhen Dong, Zhewei Yao, Yaohui Cai +4
Quantization is an effective method for reducing memory footprint and inference time of Neural Networks, e.g., for efficient inference in the cloud, especially at the edge. However…
Unsupervised Projection Networks for Generative Adversarial Networks
Daiyaan Arfeen, Jesse Zhang
We propose the use of unsupervised learning to train projection networks that project onto the latent space of an already trained generator. We apply our method to a trained StyleG…