Co-Location of CPU and GPU Workloads for High Resource Efficiency in Kubernetes

Co-Location of CPU and GPU Workloads for High Resource Efficiency in Kubernetes

Explore strategies for optimizing resource utilization in Kubernetes clusters by co-locating CPU and GPU workloads. Learn how Ant Financial and Alibaba achieved a 10% increase in utilization through innovative approaches. Discover the creation of a new QoS class, implementation of node-level cgroups for batch jobs, and use of PodGroup CRD for gang scheduling. Gain insights into building and managing a co-location cluster with over 100 GPU and 500 CPU nodes, effectively combining long-running services and AI batch jobs. This 37-minute conference talk from the Linux Foundation provides valuable experience and practices for maximizing resource efficiency in Kubernetes environments.