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…
DisasterBench: Benchmarking LLM Planning under Typed Tool Interface Constraints
Zhitong Chen, Kai Yin, Weifeng Zhang +7
Disasters cause severe societal impacts, demanding rapid coordination of heterogeneous AI tools, from satellite analysis to flood prediction and damage assessment, into coherent mu…
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…