Graph neural networks (GNNs) suffer from low GPU utilization due to frequent memory
accesses. Existing concurrent training mechanisms cannot be directly adapted to GNNs …

Recent innovation in large language models (LLMs), and their myriad use cases have
rapidly driven up the compute demand for datacenter GPUs. Several cloud providers and …

Training deep learning (DL) models in the cloud has become a norm. With the emergence of
serverless computing and its benefits of true pay-as-you-go pricing and scalability, systems …

Modern cluster management systems, such as Kubernetes, support heterogeneous
workloads and resources. However, existing resource schedulers in these systems do not …

Modern GPU clusters are designed to support distributed Deep Learning jobs from multiple
tenants concurrently. Each tenant may have varied and dynamic resource demands …

With the rapid development of cloud systems, an increasing number of service workloads
are deployed in the private cloud and/or public cloud. Although large cloud providers such …

As machine learning (ML) becomes more and more popular, datacenter operators use
hardware accelerators such as GPUs to tackle the high computation demand of ML …

With the rapid proliferation of deep learning (DL) jobs running on heterogeneous GPUs,
scheduling DL jobs to meet various scheduling requirements, such as meeting deadlines …

Machine learning (ML) tasks are one of the major workloads in today's edge computing
networks. Existing edge-cloud schedulers allocate the requested amounts of resources to …

Modern GPU clusters inherently exhibit heterogeneity, encompassing various aspects such
as computation and communication. This heterogeneity poses a significant challenge for the …