Cover Image

FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Zhou, Yuanchang, Hu, Siyu, Wang, Chen, Wang, Lin-Wang, Tan, Guangming, Jia, Weile
Format:	Preprint
Published:	2024
Subjects:	Distributed, Parallel, and Cluster Computing Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
by: Zhou, Yuanchang, et al.
Published: (2026)

JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
by: Wang, Hongyu, et al.
Published: (2026)

Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
by: Guo, Zhuoqiang, et al.
Published: (2025)

Fast Kronecker Matrix-Matrix Multiplication on GPUs
by: Jangda, Abhinav, et al.
Published: (2024)

Joint Training on AMD and NVIDIA GPUs
by: Hu, Jon, et al.
Published: (2026)

Large-scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku
by: Xu, Hongtao, et al.
Published: (2025)

Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
by: He, Guoliang, et al.
Published: (2025)

Straggler Tolerant and Resilient DL Training on Homogeneous GPUs
by: Zhang, Zeyu, et al.
Published: (2025)

Large Scale Finite-Temperature Real-time Time Dependent Density Functional Theory Calculation with Hybrid Functional on ARM and GPU Systems
by: Liu, Rongrong, et al.
Published: (2025)

ROSE: Rollout On Serving GPUs via Cooperative Elasticity for Agentic RL
by: Gao, Wei, et al.
Published: (2026)

FlashSketch: Sketch-Kernel Co-Design for Fast Sparse Sketching on GPUs
by: Dwaraknath, Rajat Vadiraj, et al.
Published: (2026)

Optimizing sDTW for AMD GPUs
by: Latta-Lin, Daniel, et al.
Published: (2024)

HAP: SPMD DNN Training on Heterogeneous GPU Clusters with Automated Program Synthesis
by: Zhang, Shiwei, et al.
Published: (2024)

FlashMP: Fast Discrete Transform-Based Solver for Preconditioning Maxwell's Equations on GPUs
by: Zhang, Haoyuan, et al.
Published: (2025)

LuWu: An End-to-End In-Network Out-of-Core Optimizer for 100B-Scale Model-in-Network Data-Parallel Training on Distributed GPUs
by: Sun, Mo, et al.
Published: (2024)

TurboFFT: Co-Designed High-Performance and Fault-Tolerant Fast Fourier Transform on GPUs
by: Wu, Shixun, et al.
Published: (2024)

RTop-K: Ultra-Fast Row-Wise Top-K Selection for Neural Network Acceleration on GPUs
by: Xie, Xi, et al.
Published: (2024)

SDSL-Solver: Scalable Distributed Sparse Linear Solvers for Large-Scale Interior Point Methods
by: Yang, Shaofeng, et al.
Published: (2026)

PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2024)

Scaling to 32 GPUs on a Novel Composable System Architecture
by: Ihnotic, John
Published: (2024)

Tessera: Unlocking Heterogeneous GPUs through Kernel-Granularity Disaggregation
by: Hu, Tiancheng, et al.
Published: (2026)

An Adaptive Distributed Stencil Abstraction for GPUs
by: Bhosale, Aditya, et al.
Published: (2025)

Accelerating Maximal Biclique Enumeration on GPUs
by: Hsieh, Chou-Ying, et al.
Published: (2024)

Parallelizing Maximal Clique Enumeration on GPUs
by: Almasri, Mohammad, et al.
Published: (2022)

DynaTrain: Fast Online Parallelism Switching for Elastic LLM Training
by: Wang, Yuanqing, et al.
Published: (2026)

Efficient Long Context Fine-tuning with Chunk Flow
by: Yuan, Xiulong, et al.
Published: (2025)

Disaggregated Prefill and Decoding Inference System for Large Language Model Serving on Multi-Vendor GPUs
by: Chen, Xing, et al.
Published: (2025)

SWIFT: Expedited Failure Recovery for Large-scale DNN Training
by: Zhong, Yuchen, et al.
Published: (2023)

Training LLMs with Fault Tolerant HSDP on 100,000 GPUs
by: Salpekar, Omkar, et al.
Published: (2026)

Serving Compound Inference Systems on Datacenter GPUs
by: Devata, Sriram, et al.
Published: (2026)

Optimal Workload Placement on Multi-Instance GPUs
by: Turkkan, Bekir, et al.
Published: (2024)

AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
by: Yan, Ran, et al.
Published: (2025)

FLUX: Fast Software-based Communication Overlap On GPUs Through Kernel Fusion
by: Chang, Li-Wen, et al.
Published: (2024)

RDMA-Based Algorithms for Sparse Matrix Multiplication on GPUs
by: Brock, Benjamin, et al.
Published: (2023)

Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs
by: Plesner, Andreas, et al.
Published: (2024)

FFTrainer: Fast Failover in Large-Language Model Training with Almost-Free State Management
by: Zhao, Bohan, et al.
Published: (2025)

HetCCL: Accelerating LLM Training with Heterogeneous GPUs
by: Kim, Heehoon, et al.
Published: (2026)

λScale: Enabling Fast Scaling for Serverless Large Language Model Inference
by: Yu, Minchen, et al.
Published: (2025)

DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
by: Tian, Ye, et al.
Published: (2024)

Optimizing Frequent Checkpointing via Low-Cost Differential for Distributed Training Systems
by: Yao, Chenxuan, et al.
Published: (2025)