9 papers
KVServe: Service-Aware KV Cache Compression for Communication-Efficient Disaggregated LLM Serving
Zedong Liu, Xinyang Ma, Dejun Luo +9
LLMs are widely adopted in production, pushing inference systems to their limits. Disaggregated LLM serving (e.g., PD separation and KV state disaggregation) improves scalability a…
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 neur…
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…
Research Paradigm of Materials Science Tetrahedra with Artificial Intelligence
Shiyun Zhang, Yibo Yao, Haoquan Long +4
The classical material tetrahedron that represents the Structure-Property-Processing-Performance-Characterization relationship is the most important research paradigm in materials…
Pipelined Dense Symmetric Eigenvalue Decomposition on Multi-GPU Architectures
Hansheng Wang, Ruiyi Zhan, Dajun Huang +6
Large symmetric eigenvalue problems are commonly observed in many disciplines such as Chemistry and Physics, and several libraries including cuSOLVERMp, MAGMA and ELPA support comp…