This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training ...

Dec 1, 2021 · This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training ...

This paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training ...

Efficient Parameter Server Placement for Distributed Deep Learning in Edge Computing. Yalan Wu, Jiaquan Yan, Long Chen 0006, Jigang Wu, Yidong Li.

Deep learning has acquired great success in many fields, including computer vision, natural language processing, autonomous driving, speech recognition, ...

Yalan Wu, Jiaquan Yan, Long Chen, Jigang Wu and Yidong Li. Efficient Parameter Server Placement for Distributed Deep Learning in Edge Computing, ...

In this paper, we consider distributed data-parallel training of DL models using a centralized parameter server architec- ture with asynchronous training loops.

An illustration of the dynamic service placement in mobile edge computing. is obvious that the available storage capacity of m2 cannot satisfy the requirements ...

"More effective distributed ml via a stale synchronous parallel parameter server." In Advances in neural information processing systems, pp. 1223-. 1231 ...

This distributed setup is crucial for overcoming the computational bottlenecks of large-scale deep learning, ensuring that both training and inference are.