29 citations · 51 across the 4 of their papers we have counts for
6 papers · 1 filter
Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool
Yan Wang, Xiulong Yuan, Kaiming Yang +16
Long-context LLM training suffers from a load-balancing problem that sequence packing does not solve. Packing samples into fixed-token sequences balances memory and linear-cost ope…
HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis
Shiwei Zhang, Lansong Diao, Chuan Wu +3
Single-Program-Multiple-Data (SPMD) parallelism has recently been adopted to train large deep neural networks (DNNs). Few studies have explored its applicability on heterogeneous c…
Ada-Grouper: Accelerating Pipeline Parallelism in Preempted Network by Adaptive Group-Scheduling for Micro-Batches
Siyu Wang, Zongyan Cao, Chang Si +3
Pipeline parallelism has been demonstrated to be a remarkable approach to improve throughput for training deep neural networks with billions of parameters over heterogeneous cluste…
Auto-Parallelizing Large Models with Rhino: A Systematic Approach on Production AI Platform
Shiwei Zhang, Lansong Diao, Siyu Wang +7
We present Rhino, a system for accelerating tensor programs with automatic parallelization on AI platform for real production environment. It transforms a tensor program written fo…
Optimizing DNN Compilation for Distributed Training with Joint OP and Tensor Fusion
Xiaodong Yi, Shiwei Zhang, Lansong Diao +6
This paper proposes DisCo, an automatic deep learning compilation module for data-parallel distributed training. Unlike most deep learning compilers that focus on training or infer…
DAPPLE: A Pipelined Data Parallel Approach for Training Large Models
Shiqing Fan, Yi Rong, Chen Meng +10
It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an e…