# Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems

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## Abstract

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## 1. Introduction

- First, we propose a new layer dropping scheme to reduce communication data. In the case of large-scale deep learning models, the number of model parameters is large, so the number of gradients computed by workers is also large. Therefore, it is inefficient to compare the threshold value with the value of each gradient computed by the worker. Our proposed scheme efficiently compresses communication data by comparing the representative value of each hidden layer of the deep learning model and a threshold. We used the average of the absolute values of the gradients as the representative value of each hidden layer. The worker converts the hidden layer for which the representative value is less than the threshold value into a list type of size oneand sends it to the parameter server. Furthermore, the parameter server converts the hidden layers that have not been updated into a list of size one and sends them to the worker. Hidden layers that are not sent to the parameter server are accumulated in the worker’s local cache. When the accumulated value is greater than the threshold, the hidden layer stored in the worker’s local cache is sent to the parameter server to ensure training accuracy.
- Second, we propose an efficient method to pick a threshold value. The threshold is a value that can reduce the size of the gradients that the worker will send to the parameter server by a ratio R from the size of the total gradients. It takes much time to calculate a threshold by reflecting all the gradients. Therefore, in this paper, we compute the threshold by replacing the value of each gradient with the L1 norm of the gradients included in each hidden layer. Since the L1 norm is the sum of the absolute values of each element of the vector, the L1 norm of the gradients of each hidden layer shows the effect of the gradients on the model parameters. By using the L1 norm of each hidden layer, a large number of gradients to be used for threshold calculation can be reduced to the number of hidden layers in the deep learning model. The threshold can be fixed at the value calculated at the beginning of training or can be calculated again at specific cycles. In our experiments, we calculated a new threshold every 100 steps.

## 2. Related Works

## 3. Distributed Deep Learning Architecture

## 4. Communication Optimization Schemes

#### 4.1. Operation of the Worker for Communication Optimization

#### 4.2. Operation of the Parameter Server for Communication Optimization

#### 4.3. Gradient Accumulation

## 5. How to Calculate the Threshold

Algorithm 1 Threshold searching algorithm. |

## 6. Evaluations

#### 6.1. Performance of Layer Dropping in a 1 Gbit Network Environment

#### 6.2. Performance of Layer Dropping in a 56 Gbit Network Environment

#### 6.3. Summary of the Experimental Results

## 7. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## References

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Network | Layer Dropping | Worker Compute (sec) | Parameter Server Compute (sec) | Communication (sec) | Total (sec) |
---|---|---|---|---|---|

1 Gb/s | No | 0.388 | 0.073 | 3.457 | 3.918 |

Yes | 0.408 | 0.085 | 0.182 | 0.675 | |

56 Gb/s | No | 0.222 | 0.048 | 1.951 | 2.222 |

Yes | 0.230 | 0.060 | 0.375 | 0.665 |

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**MDPI and ACS Style**

Lee, J.; Choi, H.; Jeong, H.; Noh, B.; Shin, J.S. Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems. *Appl. Sci.* **2020**, *10*, 8846.
https://doi.org/10.3390/app10248846

**AMA Style**

Lee J, Choi H, Jeong H, Noh B, Shin JS. Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems. *Applied Sciences*. 2020; 10(24):8846.
https://doi.org/10.3390/app10248846

**Chicago/Turabian Style**

Lee, Jaehwan, Hyeonseong Choi, Hyeonwoo Jeong, Baekhyeon Noh, and Ji Sun Shin. 2020. "Communication Optimization Schemes for Accelerating Distributed Deep Learning Systems" *Applied Sciences* 10, no. 24: 8846.
https://doi.org/10.3390/app10248846