5 papers
TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training
Man Liu, Xingchen Liu, Xingjian Tian +8
Handling communication overhead in large-scale tensor-parallel training remains a critical challenge due to the dense, near-zero distributions of intermediate tensors, which exacer…
A Fully GPU-Accelerated Framework for High-Performance Configuration Interaction Selection with Neural Network Quantum States
Daran Sun, Bowen Kan, Haoquan Long +13
AI-driven methods have demonstrated considerable success in tackling the central challenge of accurately solving the Schrödinger equation for complex many-body systems. Among neura…
ENEC: A Lossless AI Model Compression Method Enabling Fast Inference on Ascend NPUs
Jinwu Yang, Jiaan Wu, Zedong Liu +17
The rapid scaling of Large Language Models presents significant challenges for their deployment and inference, particularly on resource-constrained specialized AI hardware accelera…
TSUE: A Two-Stage Data Update Method for an Erasure Coded Cluster File System
Zheng Wei, Jing Xing, Yida Gu +4
Compared to replication-based storage systems, erasure-coded storage incurs significantly higher overhead during data updates. To address this issue, various parity logging methods…
SolarZip: An Efficient and Adaptive Compression Framework for Solar EUV Imaging Data
Zedong Liu, Song Tan, Alexander Warmuth +7
Context: With the advancement of solar physics research, next-generation solar space missions and ground-based telescopes face significant challenges in efficiently transmitting an…