Part 2: How to Measure Your GPU Utilization

This five-part series explores the under-utilization of GPU clusters, methods to measure and improve utilization, and related security and optimization practices within Kubernetes environments. Traditional GPU monitoring tools like nvidia-smi offer only snapshot views of utilization, lacking the strategic insights necessary for optimization; thus, a multidimensional approach that integrates with Kubernetes orchestration is recommended. The NVIDIA Data Center GPU Manager (DCGM), when combined with cAdvisor and Kubernetes metrics, provides comprehensive monitoring capabilities, offering insights into GPU utilization patterns across workloads. The NVIDIA GPU Operator facilitates the deployment and management of DCGM, ensuring consistent monitoring and integration with Kubernetes infrastructure. Effective GPU optimization involves understanding the interplay between compute and memory utilization, enabling strategic decisions on workload placement and resource sharing. Additionally, strategic GPU monitoring should encompass cluster-wide trends, utilization patterns, and cost attributions to identify optimization opportunities and improve scheduling. A workshop with NVIDIA is available for further learning on GPU utilization in Kubernetes.

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