[doc] added reference to related works (#2994)

* [doc] added reference to related works

* polish code

README-zh-Hans.md

@@ -376,6 +376,10 @@ docker run -ti --gpus all --rm --ipc=host colossalai bash
 
 ## 引用我们
 
+Colossal-AI项目受一些相关的项目启发而成立，一些项目是我们的开发者的科研项目，另一些来自于其他组织的科研工作。我们希望. 我们希望在[参考文献列表](./REFERENCE.md)中列出这些令人称赞的项目，以向开源社区和研究项目致谢。
+
+你可以通过以下格式引用这个项目。
+
 ```
 @article{bian2021colossal,
   title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},

README.md

@@ -378,6 +378,10 @@ We leverage the power of [GitHub Actions](https://github.com/features/actions) t
 
 ## Cite Us
 
+This project is inspired by some related projects (some by our team and some by other organizations). We would like to credit these amazing projects as listed in the [Reference List](./REFERENCE.md).
+
+To cite this project, you can use the following BibTeX citation.
+
 ```
 @article{bian2021colossal,
   title={Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training},

REFERENCE.md

+# References
+
+The Colossal-AI project aims to provide a wide array of parallelism techniques for the machine learning community in the big-model era. This project is inspired by quite a few reserach works, some are conducted by some of our developers and the others are research projects open-sourced by other organizations. We would like to credit these amazing projects below in the IEEE citation format.
+
+## By Our Team
+
+- Q. Xu, S. Li, C. Gong, and Y. You, ‘An Efficient 2D Method for Training Super-Large Deep Learning Models’. arXiv, 2021.
+
+- Z. Bian, Q. Xu, B. Wang, and Y. You, ‘Maximizing Parallelism in Distributed Training for Huge Neural Networks’. arXiv, 2021.
+
+- S. Li, F. Xue, C. Baranwal, Y. Li, and Y. You, ‘Sequence Parallelism: Long Sequence Training from System Perspective’. arXiv, 2021.
+
+- S. Li et al., ‘Colossal-AI: A Unified Deep Learning System For Large-Scale Parallel Training’. arXiv, 2021.
+
+- B. Wang, Q. Xu, Z. Bian, and Y. You, ‘Tesseract: Parallelize the Tensor Parallelism Efficiently’, in Proceedings of the 51th International Conference on Parallel Processing, 2022.
+
+- J. Fang et al., ‘A Frequency-aware Software Cache for Large Recommendation System Embeddings’. arXiv, 2022.
+
+- J. Fang et al., ‘Parallel Training of Pre-Trained Models via Chunk-Based Dynamic Memory Management’, IEEE Transactions on Parallel and Distributed Systems, vol. 34, no. 1, pp. 304–315, 2023.
+
+- Y. Liu, S. Li, J. Fang, Y. Shao, B. Yao, and Y. You, ‘Colossal-Auto: Unified Automation of Parallelization and Activation Checkpoint for Large-scale Models’. arXiv, 2023.
+
+
+## By Other Organizations
+
+- M. Shoeybi, M. Patwary, R. Puri, P. LeGresley, J. Casper, and B. Catanzaro, ‘Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism’. arXiv, 2019.
+
+- S. Rajbhandari, J. Rasley, O. Ruwase, and Y. He, ‘ZeRO: Memory Optimizations toward Training Trillion Parameter Models’, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, 2020.
+
+- J. Rasley, S. Rajbhandari, O. Ruwase, and Y. He, ‘DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters’, in Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA, 2020, pp. 3505–3506.
+
+- D. Narayanan et al., ‘Efficient Large-Scale Language Model Training on GPU Clusters Using Megatron-LM’, in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, St. Louis, Missouri, 2021.
+
+- Jie Ren, Samyam Rajbhandari, Reza Yazdani Aminabadi, Olatunji Ruwase, Shuangyan Yang, Minjia Zhang, Dong Li, Yuxiong He. 2021. ZeRO-Offload: Democratizing Billion-Scale Model Training. arXiv:2101.06840 and USENIX ATC 2021.
+
+- S. Rajbhandari, O. Ruwase, J. Rasley, S. Smith, and Y. He, ‘ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning’. in Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis, St. Louis, Missouri, 2021.
+
+- L. Zheng et al., ‘Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning’, in 16th USENIX Symposium on Operating Systems Design and Implementation (OSDI 22), 2022, pp. 559–578.

docs/source/en/concepts/paradigms_of_parallelism.md

@@ -119,5 +119,6 @@ model on a single machine.
 </figure>
 
 Related paper:
+- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
 - [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
 - [PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management](https://arxiv.org/abs/2108.05818)

docs/source/en/features/nvme_offload.md

@@ -5,6 +5,11 @@ Author: Hongxin Liu
 **Prerequisite:**
 - [Zero Redundancy Optimizer with chunk-based memory management](../features/zero_with_chunk.md)
 
+**Related Paper**
+
+- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
+- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
+
 ## Introduction
 
 If a model has `N` parameters, when using Adam, it has `8N` optimizer states. For billion-scale models, optimizer states take at least 32 GB memory. GPU memory limits the model scale we can train, which is called GPU memory wall. If we offload optimizer states to the disk, we can break through GPU memory wall.

docs/source/en/features/zero_with_chunk.md

 # Zero Redundancy Optimizer with chunk-based memory management
 
 Author: [Hongxiu Liu](https://github.com/ver217), [Jiarui Fang](https://github.com/feifeibear), [Zijian Ye](https://github.com/ZijianYY)
+
 **Prerequisite:**
 - [Define Your Configuration](../basics/define_your_config.md)
 
@@ -9,9 +10,11 @@ Author: [Hongxiu Liu](https://github.com/ver217), [Jiarui Fang](https://github.c
 - [Train GPT with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt)
 
 **Related Paper**
+
 - [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/abs/1910.02054)
 - [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
 - [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
+- [DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters](https://dl.acm.org/doi/10.1145/3394486.3406703)
 - [PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management](https://arxiv.org/abs/2108.05818)
 
 ## Introduction

docs/source/zh-Hans/concepts/paradigms_of_parallelism.md

@@ -87,5 +87,6 @@
 </figure>
 
 相关文章:
+- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
 - [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
 - [PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management](https://arxiv.org/abs/2108.05818)

docs/source/zh-Hans/features/nvme_offload.md

@@ -5,6 +5,10 @@
 **前置教程:**
 - [基于Chunk内存管理的零冗余优化器 (ZeRO)](../features/zero_with_chunk.md)
 
+**相关论文**
+
+- [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
+- [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
 ## 引言
 
 如果模型具有`N`个参数，在使用 Adam 时，优化器状态具有`8N`个参数。对于十亿规模的模型，优化器状态至少需要 32 GB 内存。 GPU显存限制了我们可以训练的模型规模，这称为GPU显存墙。如果我们将优化器状态 offload 到磁盘，我们可以突破 GPU 内存墙。

docs/source/zh-Hans/features/zero_with_chunk.md

@@ -3,9 +3,11 @@
 作者: [Hongxiu Liu](https://github.com/ver217), [Jiarui Fang](https://github.com/feifeibear), [Zijian Ye](https://github.com/ZijianYY)
 
 **前置教程:**
+
 - [定义配置文件](../basics/define_your_config.md)
 
 **示例代码**
+
 - [Train GPT with Colossal-AI](https://github.com/hpcaitech/ColossalAI/tree/main/examples/language/gpt)
 
 **相关论文**
@@ -13,8 +15,10 @@
 - [ZeRO: Memory Optimizations Toward Training Trillion Parameter Models](https://arxiv.org/abs/1910.02054)
 - [ZeRO-Offload: Democratizing Billion-Scale Model Training](https://arxiv.org/abs/2101.06840)
 - [ZeRO-Infinity: Breaking the GPU Memory Wall for Extreme Scale Deep Learning](https://arxiv.org/abs/2104.07857)
+- [DeepSpeed: System Optimizations Enable Training Deep Learning Models with Over 100 Billion Parameters](https://dl.acm.org/doi/10.1145/3394486.3406703)
 - [PatrickStar: Parallel Training of Pre-trained Models via Chunk-based Memory Management](https://arxiv.org/abs/2108.05818)
 
+
 ## 引言
 
 零冗余优化器 (ZeRO) 通过对三个模型状态（优化器状态、梯度和参数）进行划分而不是复制他们，消除了数据并行进程中的内存冗余。该方法与传统的数据并行相比，内存效率得到了极大的提高，而计算粒度和通信效率得到了保留。