4 papers
EPIC: Abstraction and Polymorphism of In-Network Collectives on Ethernet
Yitao Yuan, Jianglong Nie, Tianyu Bai +28
In-Network Collective (INC) acceleration holds immense potential for optimizing AI training and inference; however, its cross-layer nature has historically hindered investment and…
NasZip: Software and Hardware Co-Design to Accelerate Approximate Nearest Neighbor Search with DIMM-Based Near-Data Processing
Cheng Zou, Shuo Yang, Chen Nie +6
As large language models (LLMs) continue to advance, retrieval-augmented generation (RAG) has become the key mechanism for expanding model knowledge and reducing hallucinations. Ce…
Arena: Efficiently Training Large Models via Dynamic Scheduling and Adaptive Parallelism Co-Design
Chunyu Xue, Weihao Cui, Quan Chen +10
Efficiently training large-scale models (LMs) in GPU clusters involves two separate avenues: inter-job dynamic scheduling and intra-job adaptive parallelism (AP). However, existing…
Exploiting Block Coordinate Descent for Cost-Effective LLM Model Training
Zeyu Liu, Yan Li, Yunquan Zhang +6
Training large language models typically demands extensive GPU memory and substantial financial investment, which poses a barrier for many small- to medium-sized teams. In this pap…