Learning Efficient Parameter Server Synchronization Policies for Distributed SGD
Authors: Rong Zhu, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou
ICLR 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present extensive numerical results obtained from experiments performed in simulated cluster environments. In our experiments training time is reduced by 44% on average and learned policies generalize to multiple unseen circumstances. |
| Researcher Affiliation | Industry | Rong Zhu*, Sheng Yang, Andreas Pfadler, Zhengping Qian, Jingren Zhou Alibaba Group |
| Pseudocode | Yes | Algorithm 1: Unified Synchronization Policy Framework |
| Open Source Code | No | The paper does not provide a direct link or explicit statement about the availability of the source code for its methodology. |
| Open Datasets | Yes | In each instance, we randomly sample 50% data from the MNIST dataset and run the standard SGD for training. |
| Dataset Splits | No | The paper mentions "88% validation accuracy" as a termination criterion but does not specify the size or split methodology for a validation dataset. |
| Hardware Specification | No | The paper states, "We implement RLP in a simulated cluster/PS environment." As experiments are conducted in a simulated environment, no specific physical hardware specifications are mentioned for running the experiments. |
| Software Dependencies | No | The paper mentions using "standard off-the-shelf deep Q-learning algorithm" and "two-layer neural networks" but does not specify any software names with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The hyper-parameters for RLP are set as follows: historical size H = 10, replay pool size N = 50, mini-batch size |B| = 32, copy rate c = 5, discount factor γ = 0.8, exploration probability ϵ = 0.1 and learning rate to be 0.01. For the underlying DNN model, we set its batch size to 16 and learning rate to 0.01. |