34 citations · 79 across the 27 of their papers we have counts for
29 papers
OACM: Optimistic Asynchronous Communication Model for Large-Scale SNN Simulation
Guangnan Feng, Yangle Zeng, Honghui Liang +2
Spiking Neural Network (SNN) simulation serves as a crucial tool for understanding brain dynamics and advancing neuromorphic computing, but faces significant scalability challenges…
Evaluating Predicted Densities, Hamiltonians, and Density Matrices as Periodic SCF Initializers
Pin Chen, Jiang Li, Yutong Lu
Learned electronic states are usually evaluated by prediction error, even though their intended use is to accelerate the self-consistent-field (SCF) loop of density functional theo…
Communication-Aware Placement and Pruning for Efficient Mixture-of-Experts Inference
Xiao Shi, Yingying Sun, Jiangsu Du +2
As MoE models scale to hundreds of experts, placement and pruning decisions increasingly dictate communication volume, affecting the performance of distributed inference across GPU…
Extreme-Scale Atomistic Simulation of Real-Temperature Magnetic Skyrmion Dynamics by Coupled Spin-Lattice Modeling
Pin Chen, Cheng-bing Chen, Hai Liu +9
Real-temperature topological magnetic dynamics in functional materials is governed by coupled lattice and spin evolution, yet remains inaccessible to predictive simulation at devic…
CodegenBench: Can LLMs Write Efficient Code Across Architectures?
Jie Li, Wenzhao Wu, Junqi Hu +5
While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.g., PyTorch, CUDA),…
AlignedServe: Orchestrating Prefix-aware Batching to Build a High-throughput and Computing-efficient LLM Serving System
Fengyao Bai, Hongbin Zhang, Zhitao Chen +3
High-throughput inference serving is essential for applications built on large language models (LLMs). Existing serving frameworks reduce request-level and batch-level bubbles thro…
POLAR-PIC: A Holistic Framework for Matrixized PIC with Co-Designed Compute, Layout, and Communication
Yizhuo Rao, Xingjian Cui, Shangzhi Pang +8
Particle-in-Cell (PIC) simulations are fundamental to plasma physics but often suffer from limited scalability due to particle-grid interaction bottlenecks and particle redistribut…
Beyond Few-Step Inference: Accelerating Video Diffusion Transformer Model Serving with Inter-Request Caching Reuse
Hao Liu, Ye Huang, Chenghuan Huang +5
Video Diffusion Transformer (DiT) models are a dominant approach for high-quality video generation but suffer from high inference cost due to iterative denoising. Existing caching…
Clinical-Injection Transformer with Domain-Adapted MAE for Lupus Nephritis Prognosis Prediction
Yuewen Huang, Zhitao Ye, Guangnan Feng +3
Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus that affects pediatric patients with significantly greater severity and worse renal outcomes compared…
Matrix-PIC: Harnessing Matrix Outer-product for High-Performance Particle-in-Cell Simulations
Yizhuo Rao, Xingjian Cui, Jiabin Xie +5
Particle-in-Cell (PIC) simulations spend most of their execution time on particle--grid interactions, where fine-grained atomic updates become a major bottleneck on traditional man…
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…
UNR: Unified Notifiable RMA Library for HPC
Guangnan Feng, Jiabin Xie, Dezun Dong +1
Remote Memory Access (RMA) enables direct access to remote memory to achieve high performance for HPC applications. However, most modern parallel programming models lack schemes fo…
Galaxy: A Resource-Efficient Collaborative Edge AI System for In-situ Transformer Inference
Shengyuan Ye, Jiangsu Du, Liekang Zeng +4
Transformer-based models have unlocked a plethora of powerful intelligent applications at the edge, such as voice assistant in smart home. Traditional deployment approaches offload…
Exploring Low-Resource Medical Image Classification with Weakly Supervised Prompt Learning
Fudan Zheng, Jindong Cao, Weijiang Yu +3
Most advances in medical image recognition supporting clinical auxiliary diagnosis meet challenges due to the low-resource situation in the medical field, where annotations are hig…
Intensive Vision-guided Network for Radiology Report Generation
Fudan Zheng, Mengfei Li, Ying Wang +5
Automatic radiology report generation is booming due to its huge application potential for the healthcare industry. However, existing computer vision and natural language processin…
Crystal Structure Prediction by Joint Equivariant Diffusion
Rui Jiao, Wenbing Huang, Peijia Lin +4
Crystal Structure Prediction (CSP) is crucial in various scientific disciplines. While CSP can be addressed by employing currently-prevailing generative models (e.g. diffusion mode…
Searching High Temperature Superconductors with the assistance of Graph Neural Networks
Liang Gu, Yang Liu, Pin Chen +5
Predicting high temperature superconductors has long been a great challenge. A major difficulty is how to predict the transition temperature Tc of superconductors. Recently, progre…