Name
Title | ||
|---|---|---|
PipePar: A Pipelined Hybrid Parallel Approach for Accelerating Distributed DNN Training |
Abstract | ||
|---|---|---|
Large scale DNN training tasks are exceedingly compute-intensive and time-consuming, which are usually executed on highly-parallel platforms. Data and model parallelization is a common way to speed up the training progress across devices. However, they tend to achieve sub-optimal performance due to the communication overheads and unbalanced load among servers. Recent emerging pipelining solutions ... |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CSCWD49262.2021.9437625 | 2021 IEEE 24th International Conference on Computer Supported Cooperative Work in Design (CSCWD) |
Keywords | DocType | ISBN |
Training,Performance evaluation,Tensors,Computational modeling,Graphics processing units,Data models,Servers | Conference | 978-1-7281-6597-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jiange Li | 1 | 0 | 0.34 |
| Yu-Chen Wang | 2 | 34 | 27.05 |
| Zhang Jinghui | 3 | 33 | 7.47 |
| Jin Jiahui | 4 | 88 | 16.84 |
| Fang Dong | 5 | 202 | 35.44 |
| Lei Qian | 6 | 0 | 0.34 |