The computing demand for massive applications has led to the ubiquitous deployment of
computing power. This trend results in the urgent need for higher-level computing resource …

Deep learning tasks (DLT) include training and inference tasks, where training DLTs have
requirements on minimizing average job completion time (JCT) and inference tasks need …

Distributed training (DT) in shared clusters usually deploys a scheduler for resource
allocation to multiple concurrent jobs. Meanwhile, a recent acceleration primitive, In-Network …

The scale of deep learning models has grown tremendously in recent years. State-of-the-art
models have reached billions of parameters and terabyte-scale model sizes. Training of …

With the proliferation of deep learning, there exists a strong need to efficiently operate GPU
clusters for deep learning production in giant AI companies, as well as for research and …

Public cloud GPU clusters are becoming emerging platforms for training distributed deep
learning jobs. Under this training paradigm, the job scheduler is a crucial component to …

With rapidly increasing distributed deep learning workloads in large-scale data centers,
efficient distributed deep learning framework strategies for resource allocation and workload …

The growth of deep learning training (DLT) jobs in modern GPU clusters calls for efficient
deep learning (DL) scheduler designs. Due to the extensive applications of DL technology …

As large language models (LLMs) become widespread in various application domains, a
critical challenge the AI community is facing is how to train these large AI models in a cost …

Deep learning (DL) has become a dominating type of workloads on AI computing platforms.
The performance of such platforms highly depends on how distributed DL jobs are …