JanusPipe: Efficient Pipeline Parallel Training for Machine Learning Interatomic Potentials
Fuente:
arXiv
Saved in:
| Main Authors: | Wang, Hongyu, Liu, Weijian, Xu, Hongtao, Wang, Yan, Li, Mingzhen, Jia, Weile, Tan, Guangming |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| 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)
by: Zhou, Yuanchang, et al.
Published: (2026)
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs
by: Zhou, Yuanchang, et al.
Published: (2024)
by: Zhou, Yuanchang, et al.
Published: (2024)
Large-scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku
by: Xu, Hongtao, et al.
Published: (2025)
by: Xu, Hongtao, et al.
Published: (2025)
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
by: Tian, Ye, et al.
Published: (2024)
by: Tian, Ye, et al.
Published: (2024)
InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
by: Wang, Shiju, et al.
Published: (2025)
by: Wang, Shiju, et al.
Published: (2025)
HelixPipe: Efficient Distributed Training of Long Sequence Transformers with Attention Parallel Pipeline Parallelism
by: Zhang, Geng, et al.
Published: (2025)
by: Zhang, Geng, et al.
Published: (2025)
PipeLive: Efficient Live In-place Pipeline Parallelism Reconfiguration for Dynamic LLM Serving
by: Bai, Xu, et al.
Published: (2026)
by: Bai, Xu, et al.
Published: (2026)
SiPipe: Bridging the CPU-GPU Utilization Gap for Efficient Pipeline-Parallel LLM Inference
by: He, Yongchao, et al.
Published: (2025)
by: He, Yongchao, et al.
Published: (2025)
Efficient Long Context Fine-tuning with Chunk Flow
by: Yuan, Xiulong, et al.
Published: (2025)
by: Yuan, Xiulong, et al.
Published: (2025)
Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
by: Guo, Zhuoqiang, et al.
Published: (2025)
by: Guo, Zhuoqiang, et al.
Published: (2025)
TD-Pipe: Temporally-Disaggregated Pipeline Parallelism Architecture for High-Throughput LLM Inference
by: Zhang, Hongbin, et al.
Published: (2025)
by: Zhang, Hongbin, et al.
Published: (2025)
CrossPipe: Towards Optimal Pipeline Schedules for Cross-Datacenter Training
by: Chen, Tiancheng, et al.
Published: (2025)
by: Chen, Tiancheng, et al.
Published: (2025)
Bandwidth-Aware and Cost-Efficient Pipeline Parallel Scheduling in Geo-Distributed LLM Training
by: Zhang, Han, et al.
Published: (2026)
by: Zhang, Han, et al.
Published: (2026)
A Flexible Programmable Pipeline Parallelism Framework for Efficient DNN Training
by: Jiang, Lijuan, et al.
Published: (2025)
by: Jiang, Lijuan, et al.
Published: (2025)
Adaptra: Straggler-Resilient Hybrid-Parallel Training with Pipeline Adaptation
by: Wu, Tianyuan, et al.
Published: (2025)
by: Wu, Tianyuan, et al.
Published: (2025)
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
by: Jeon, Byungsoo, et al.
Published: (2024)
by: Jeon, Byungsoo, et al.
Published: (2024)
OptPipe: Memory- and Scheduling-Optimized Pipeline Parallelism for LLM Training
by: Li, Hongpei, et al.
Published: (2025)
by: Li, Hongpei, et al.
Published: (2025)
PipeSD: An Efficient Cloud-Edge Collaborative Pipeline Inference Framework with Speculative Decoding
by: Han, Yunhe, et al.
Published: (2026)
by: Han, Yunhe, et al.
Published: (2026)
Seq1F1B: Efficient Sequence-Level Pipeline Parallelism for Large Language Model Training
by: Sun, Ao, et al.
Published: (2024)
by: Sun, Ao, et al.
Published: (2024)
SPPO:Efficient Long-sequence LLM Training via Adaptive Sequence Pipeline Parallel Offloading
by: Chen, Qiaoling, et al.
Published: (2025)
by: Chen, Qiaoling, et al.
Published: (2025)
Memory Efficient and Staleness Free Pipeline Parallel DNN Training Framework with Improved Convergence Speed
by: Dutta, Ankita, et al.
Published: (2025)
by: Dutta, Ankita, et al.
Published: (2025)
PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
by: Arfeen, Daiyaan, et al.
Published: (2024)
by: Arfeen, Daiyaan, et al.
Published: (2024)
BitPipe: Bidirectional Interleaved Pipeline Parallelism for Accelerating Large Models Training
by: Wu, Houming, et al.
Published: (2024)
by: Wu, Houming, et al.
Published: (2024)
MSPipe: Efficient Temporal GNN Training via Staleness-Aware Pipeline
by: Sheng, Guangming, et al.
Published: (2024)
by: Sheng, Guangming, et al.
Published: (2024)
TiMePReSt: Time and Memory Efficient Pipeline Parallel DNN Training with Removed Staleness
by: Dutta, Ankita, et al.
Published: (2024)
by: Dutta, Ankita, et al.
Published: (2024)
Balancing Pipeline Parallelism with Vocabulary Parallelism
by: Yeung, Man Tsung, et al.
Published: (2024)
by: Yeung, Man Tsung, et al.
Published: (2024)
DawnPiper: A Memory-scablable Pipeline Parallel Training Framework
by: Peng, Xuan, et al.
Published: (2025)
by: Peng, Xuan, et al.
Published: (2025)
PipeBoost: Resilient Pipelined Architecture for Fast Serverless LLM Scaling
by: Liu, Chongpeng, et al.
Published: (2025)
by: Liu, Chongpeng, et al.
Published: (2025)
Enhancing Memory Efficiency in Large Language Model Training Through Chronos-aware Pipeline Parallelism
by: Lin, Xinyuan, et al.
Published: (2025)
by: Lin, Xinyuan, et al.
Published: (2025)
FlexPipe: Adapting Dynamic LLM Serving Through Inflight Pipeline Refactoring in Fragmented Serverless Clusters
by: Lin, Yanying, et al.
Published: (2025)
by: Lin, Yanying, et al.
Published: (2025)
PiPar: Pipeline Parallelism for Collaborative Machine Learning
by: Zhang, Zihan, et al.
Published: (2022)
by: Zhang, Zihan, et al.
Published: (2022)
Synergistic Tensor and Pipeline Parallelism
by: Qi, Mengshi, et al.
Published: (2025)
by: Qi, Mengshi, et al.
Published: (2025)
PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption
by: Tan, Yifan, et al.
Published: (2024)
by: Tan, Yifan, et al.
Published: (2024)
PPipe: Efficient Video Analytics Serving on Heterogeneous GPU Clusters via Pool-Based Pipeline Parallelism
by: Kong, Z. Jonny, et al.
Published: (2025)
by: Kong, Z. Jonny, et al.
Published: (2025)
SkipPipe: Partial and Reordered Pipelining Framework for Training LLMs in Heterogeneous Networks
by: Blagoev, Nikolay, et al.
Published: (2025)
by: Blagoev, Nikolay, et al.
Published: (2025)
PipeOffload: Improving Scalability of Pipeline Parallelism with Memory Optimization
by: Wan, Xinyi, et al.
Published: (2025)
by: Wan, Xinyi, et al.
Published: (2025)
Heimdall++: Optimizing GPU Utilization and Pipeline Parallelism for Efficient Single-Pulse Detection
by: Xia, Bingzheng, et al.
Published: (2025)
by: Xia, Bingzheng, et al.
Published: (2025)
NestPipe: Large-Scale Recommendation Training on 1,500+ Accelerators via Nested Pipelining
by: Jiang, Zhida, et al.
Published: (2026)
by: Jiang, Zhida, et al.
Published: (2026)
LoongTrain: Efficient Training of Long-Sequence LLMs with Head-Context Parallelism
by: Gu, Diandian, et al.
Published: (2024)
by: Gu, Diandian, et al.
Published: (2024)
ElasticMM: Efficient Multimodal LLMs Serving with Elastic Multimodal Parallelism
by: Liu, Zedong, et al.
Published: (2025)
by: Liu, Zedong, et al.
Published: (2025)
Similar Items
-
Breaking the Training Barrier of Billion-Parameter Universal Machine Learning Interatomic Potentials
by: Zhou, Yuanchang, et al.
Published: (2026) -
FastCHGNet: Training one Universal Interatomic Potential to 1.5 Hours with 32 GPUs
by: Zhou, Yuanchang, et al.
Published: (2024) -
Large-scale Neural Network Quantum States for ab initio Quantum Chemistry Simulations on Fugaku
by: Xu, Hongtao, et al.
Published: (2025) -
DiffusionPipe: Training Large Diffusion Models with Efficient Pipelines
by: Tian, Ye, et al.
Published: (2024) -
InfiniPipe: Elastic Pipeline Parallelism for Efficient Variable-Length Long-Context LLM Training
by: Wang, Shiju, et al.
Published: (2025)