6 citations · 6 across the 9 of their papers we have counts for
11 papers
TrimSO(2): Fast and Memory-Efficient Training of Equivariant Interatomic Potentials
Chen Wang, Yuanchang Zhou, Guangming Tan +2
Equivariant interatomic potentials enable accurate and efficient simulations of molecules and materials. Among them, models based on SO(2) convolutions reduce the cost of tensor pr…
LunaTP: Enabling Efficient and Scalable Tensor Product for Equivariant MLIPs
Yuanchang Zhou, Mingzhen Li, Chen Wang +3
Equivariant machine learning interatomic potentials (MLIPs) promise atomistic simulations with accuracy approaching that of quantum-mechanical calculations. However, the high compu…
Scalable High-Fidelity Macromolecular Docking for GPU-Accelerated Supercomputers
Xiangyu Meng, Peng Chen, Mingzhen Li +7
Flexible macromolecular docking offers high-fidelity predictions of biomolecular interactions, but remains prohibitively expensive at scale. Among existing approaches, LightDock le…
Libra: Taming Attention Workload Skew in Long-Context LLM Training with Bounded Sequence Pool
Yan Wang, Xiulong Yuan, Kaiming Yang +16
Long-context LLM training suffers from a load-balancing problem that sequence packing does not solve. Packing samples into fixed-token sequences balances memory and linear-cost ope…
Direct Model State Migration for Elastic Training of Large Language Models
Weijian Liu, Mingzhen Li, Rui Kang +3
Large language model (LLM) training shall adapt to dynamic resources in shared clusters to tackle the elasticity, including passive preemption and optimistic scaling. State migrati…
JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
Hongyu Wang, Weijian Liu, Hongtao Xu +4
Discovering atom-level phenomena requires molecular dynamics (MD) simulations with ab initio accuracy. Machine learning interatomic potentials (MLIPs) enable stable, high-accuracy…
SparseBalance: Load-Balanced Long Context Training with Dynamic Sparse Attention
Hongtao Xu, Jianchao Tan, Yuxuan Hu +8
While sparse attention mitigates the computational bottleneck of long-context LLM training, its distributed training process exhibits extreme heterogeneity in both \textit{1)} sequ…
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
Yuanchang Zhou, Hongyu Wang, Yiming Du +12
Universal Machine Learning Interatomic Potentials (uMLIPs), pre-trained on massively diverse datasets encompassing inorganic materials and organic molecules across the entire perio…
Large-scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku
Hongtao Xu, Zibo Wu, Mingzhen Li +1
Solving quantum many-body problems is one of the fundamental challenges in quantum chemistry. While neural network quantum states (NQS) have emerged as a promising computational to…
Scaling Neural-Network-Based Molecular Dynamics with Long-Range Electrostatic Interactions to 51 Nanoseconds per Day
Jianxiong Li, Beining Zhang, Mingzhen Li +7
Neural network-based molecular dynamics (NNMD) simulations incorporating long-range electrostatic interactions have significantly extended the applicability to heterogeneous and io…
Scaling Molecular Dynamics with ab initio Accuracy to 149 Nanoseconds per Day
Jianxiong Li, Boyang Li, Zhuoqiang Guo +7
Physical phenomena such as chemical reactions, bond breaking, and phase transition require molecular dynamics (MD) simulation with ab initio accuracy ranging from milliseconds to m…