System for deep learning training.
- Class materials for a distributed systems lecture series [GitHub]
- bytedance/byteps: A high performance and general PS framework for distributed training [GitHub]
- PipeDream: Generalized Pipeline Parallelism for DNN Training (SOSP2019) [Paper] [Github]
- Exploring Hidden Dimensions in Parallelizing Convolutional Neural Networks. [Paper] [GitHub]
- Zhihao Jia, Sina Lin, Charles R. Qi, and Alex Aiken. (ICML 2018)
- Mesh-TensorFlow: Deep Learning for Supercomputers [Paper] [GitHub]
- Shazeer, Noam, Youlong Cheng, Niki Parmar, Dustin Tran, et al. (NIPS 2018)
- Summary: Data parallelism for language model
- PyTorch-BigGraph: A Large-scale Graph Embedding System [Paper] [GitHub]
- Lerer, Adam and Wu, Ledell and Shen, Jiajun and Lacroix, Timothee and Wehrstedt, Luca and Bose, Abhijit and Peysakhovich, Alex (SysML 2019)
- Beyond data and model parallelism for deep neural networks [Paper] [GitHub]
- Jia, Zhihao, Matei Zaharia, and Alex Aiken. (SysML 2019)
- Summary: SOAP (sample, operation, attribution and parameter) parallelism. Operator graph, device topology and extution optimizer. MCMC search algorithm and excution simulator.
- Device placement optimization with reinforcement learning [Paper]
- Mirhoseini, Azalia, Hieu Pham, Quoc V. Le, Benoit Steiner, Rasmus Larsen, Yuefeng Zhou, Naveen Kumar, Mohammad Norouzi, Samy Bengio, and Jeff Dean. (ICML 17)
- Summary: Using REINFORCE learn a device placement policy. Group operations to excute. Need a lot of GPUs.
- Spotlight: Optimizing device placement for training deep neural networks [Paper]
- Gao, Yuanxiang, Li Chen, and Baochun Li (ICML 18)
- GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism [Paper][GitHub] [News]
- Huang, Yanping, et al. (arXiv preprint arXiv:1811.06965 (2018))
- Horovod: Distributed training framework for TensorFlow, Keras, and PyTorch. [GitHub]
- Distributed machine learning infrastructure for large-scale robotics research [GitHub] [Blog]
- A Generic Communication Scheduler for Distributed DNN Training Acceleration [Paper] [BytePS]
- PENG, Y., Zhu, Y., CHEN, Y., BAO, Y., Yi, B., Lan, C., Wu, C. and Guo, (SOSP 2019)
- Summary: communication schedular
- Gandiva: Introspective cluster scheduling for deep learning. [Paper]
- Xiao, Wencong, et al. (OSDI 2018)
- Summary: Improvet the efficency of hyper-parameter in cluster. Aware of hardware utilization.
- Optimus: an efficient dynamic resource scheduler for deep learning clusters [Paper]
- Peng, Yanghua, et al. (EuroSys 2018)
- Summary: Job scheduling on clusters. Total complete time as the metric.
- Multi-tenant GPU clusters for deep learning workloads: Analysis and implications. [Paper] [dataset]
- Jeon, Myeongjae, Shivaram Venkataraman, Junjie Qian, Amar Phanishayee, Wencong Xiao, and Fan Yang
- Slurm: A Highly Scalable Workload Manager [GitHub]