In the last few years, the deep learning (DL) computing paradigm has been deemed the
Gold Standard in the machine learning (ML) community. Moreover, it has gradually become …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …

The rise of large language models (LLMs) has enabled LLM-based applications (aka AI
agents or co-pilots), a new software paradigm that combines the strength of LLM and …

Modern GPU datacenters are critical for delivering Deep Learning (DL) models and services
in both the research community and industry. When operating a datacenter, optimization of …

In public clouds, users must manually select a datacenter region to upload their ML training
data and launch ML training workloads in the same region to ensure data and computation …

Large tech companies are piling up a massive number of GPUs in their server fleets to run
diverse machine learning (ML) workloads. However, these expensive devices often suffer …

Containers are widely used for resource management in datacenters. A common practice to
support deep learning (DL) training in container clouds is to statically bind GPUs to …

Since its release in 2014, Kubernetes has become a popular choice for orchestrating
containerized workloads at scale. To determine the most appropriate node to host a given …

Deep learning (DL) is becoming increasingly popular in many domains, including computer
vision, speech recognition, self-driving automobiles, etc. GPU can train DL models efficiently …