34 citations · 79 across the 27 of their papers we have counts for
8 papers · 1 filter
Equivariant Diffusion for Crystal Structure Prediction
Peijia Lin, Pin Chen, Rui Jiao +6
In addressing the challenge of Crystal Structure Prediction (CSP), symmetry-aware deep learning models, particularly diffusion models, have been extensively studied, which treat CS…
Partial Cross-Compilation and Mixed Execution for Accelerating Dynamic Binary Translation
Yuhao Gu, Zhongchun Zheng, Nong Xiao +2
With the growing diversity of instruction set architectures (ISAs), cross-ISA program execution has become common. Dynamic binary translation (DBT) is the main solution but suffers…
PolyKAN: Efficient Fused GPU Operators for Polynomial Kolmogorov-Arnold Network Variants
Mingkun Yu, Heming Zhong, Dan Huang +2
Kolmogorov-Arnold Networks (KANs) promise higher expressive capability and stronger interpretability than Multi-Layer Perceptron, particularly in the domain of AI for Science. Howe…
HPC-AI Coupling Methodology for Scientific Applications
Yutong Lu, Dan Huang, Pin Chen
Artificial intelligence (AI) technologies have fundamentally transformed numerical-based high-performance computing (HPC) applications with data-driven approaches and endeavored to…
TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference
Hongbin Zhang, Taosheng Wei, Zhenyi Zheng +3
As the model size continuously increases, pipeline parallelism shows great promise in throughput-oriented LLM inference due to its low demand on communications. However, imbalanced…
gLLM: Global Balanced Pipeline Parallelism System for Distributed LLM Serving with Token Throttling
Tianyu Guo, Xianwei Zhang, Jiangsu Du +3
Pipeline parallelism has emerged as a predominant approach for deploying large language models (LLMs) across distributed nodes, owing to its lower communication overhead compared t…
EcoServe: Enabling Cost-effective LLM Serving with Proactive Intra- and Inter-Instance Orchestration
Jiangsu Du, Hongbin Zhang, Taosheng Wei +4
Existing LLM serving strategies can be categorized based on whether prefill and decode phases are disaggregated: non-disaggregated (NoDG) or fully disaggregated (FuDG). However, th…
Boosting LLM Serving through Spatial-Temporal GPU Resource Sharing
Zejia Lin, Hongxin Xu, Guanyi Chen +3
Modern LLM serving systems confront inefficient GPU utilization due to the fundamental mismatch between compute-intensive prefill and memory-bound decode phases. While current prac…