Title: Xiaonan Nie’s Home
Open Graph Title: Xiaonan Nie’s Home
Open Graph Description: About me
Opengraph URL: https://codecaution.github.io/
Domain: codecaution.github.io
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Links:
| Xiaonan Nie's Home | https://codecaution.github.io/ |
| Github | https://github.com/codecaution |
| Google Scholar | https://scholar.google.com/citations?user=99LfmxYAAAAJ&hl=zh-CN&oi=ao |
| Prof. Bin Cui | http://net.pku.edu.cn/~cuibin/ |
| the 1st Google MoE workshop | https://rsvp.withgoogle.com/events/googleworkshopsparsityadaptivecomputation-2022/agenda |
| NVIDIA’s GPU technology conference (GTC) 2024 | https://www.nvidia.com/en-us/on-demand/session/gtc24-s61691/ |
| HETU | https://github.com/PKU-DAIR/Hetu |
| Angel-PTM | https://cloud.tencent.com/developer/article/2245528 |
| Baichuan-AI | https://www.baichuan-ai.com/home |
| GTC 2024 | https://www.nvidia.com/en-us/on-demand/session/gtc24-s61691/ |
| 2021 Synced Machine Intelligence TOP-10 Open Source Awards. | https://www.jiqizhixin.com/awards/2021/events |
| Pop SOTA！List for AI Developers 2021. | https://mp.weixin.qq.com/s/jHkF9UpgEn1MLZpRH2FOaA |
| [2021 CCF BDCI Contest] | https://mp.weixin.qq.com/s/hSoDMVMZApQxaiNqh2jUSg |
| HunYuan-NLP 1T, Top-1 model in CLUE | https://cluebenchmarks.com/rank.html |
| https://arxiv.org/abs/2509.20427 | |
| https://arxiv.org/abs/2506.09113 | |
| https://arxiv.org/abs/2505.14683 | |
| https://arxiv.org/abs/2309.10305 | |
| Efficiently Training 7B LLM with 1 Million Sequence Length on 8 GPUs. | https://arxiv.org/abs/2407.12117 |
| Malleus: Straggler-Resilient Hybrid Parallel Training of Large-scale Models via Malleable Data and Model Parallelization. | https://arxiv.org/abs/2410.13333 |
| PQCache: Product Quantization-based KVCache for Long Context LLM Inference. | https://arxiv.org/abs/2407.12820 |
| ByteScale: Efficient Scaling of LLM Training with a 2048K Context Length on More Than 12,000 GPUs. | https://arxiv.org/abs/2502.21231 |
| NetMoE: Accelerating MoE Training through Dynamic Sample Placement. | https://openreview.net/forum?id=1qP3lsatCR |
| DataSculpt: A Holistic Data Management Framework for Long-Context LLMs Training. | https://arxiv.org/abs/2409.00997 |
| LSH-MoE: Communication-efficient MoE Training via Locality-Sensitive Hashing. | https://arxiv.org/abs/2411.08446 |
| Improving Automatic Parallel Training via Balanced Memory Workload Optimization | https://arxiv.org/abs/2307.02031 |
| FlexMoE: Scaling Large-scale Sparse Pre-trained Model Training via Dynamic Device Placement | https://arxiv.org/abs/2304.03946 |
| Angel-PTM: A Scalable and Economical Large-scale Pre-training System in Tencent | https://arxiv.org/pdf/2303.02868.pdf |
| Galvatron: Efficient Transformer Training over Multiple GPUs Using Automatic Parallelism | https://www.vldb.org/pvldb/vol16/p470-miao.pdf |
| OSDP: Optimal Sharded Data Parallel for Distributed Deep Learning | https://arxiv.org/abs/2209.13258 |
| TSPLIT: Fine-grained GPU Memory Management for Efficient DNN Training via Tensor Splitting | https://ieeexplore.ieee.org/document/9835178 |
| EvoMoE: An Evolutional Mixture-of-Experts Training Framework via Dense-To-Sparse Gate | https://codecaution.github.io/(https:/arxiv.org/abs/2112.14397) |
| Hetu: A highly efficient automatic parallel distributed deep learning system | http://scis.scichina.com/en/2023/117101.pdf |
| HET: Scaling out Huge Embedding Model Training via Cache-enabled Distributed Framework | https://dl.acm.org/doi/10.14778/3489496.3489511 |
| HET-GMP: A Graph-based System Approach to Scaling Large Embedding Model Training | https://dl.acm.org/doi/10.1145/3514221.3517902 |
| Heterogeneity-Aware Distributed Machine Learning Training via Partial Reduce | https://dl.acm.org/doi/10.1145/3448016.3452773 |
| Sitemap | https://codecaution.github.io/sitemap/ |
| GitHub | http://github.com/codecaution |
| Feed | https://codecaution.github.io/feed.xml |
| Jekyll | http://jekyllrb.com |
| AcademicPages | https://github.com/academicpages/academicpages.github.io |
| Minimal Mistakes | https://mademistakes.com/work/minimal-mistakes-jekyll-theme/ |
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