Abstract: Edge deployment of large language models (LLMs) is increasingly attractive due to its advantages in privacy, customization, and availability. However, edge environments face significant ...

Abstract: Distributed learning is commonly used for training deep learning models, especially large models. In distributed learning, manual parallelism (MP) methods demand considerable human effort ...

Determining the degree of parallelism (DOP) for query execution is of great importance to both performance and resource provisioning. However, recent work that applies machine learning (ML) to query ...