Using NVIDIA GPU Time-slicing in Cloud Kubernetes Clusters with the Luna Smart Cluster Autoscaler

Introduction

Kubernetes (K8s) workloads are given exclusive access to their allocated GPUs by default.  With NVIDIA GPU time-slicing, GPUs can be shared among K8s workloads by interleaving their GPU use.  For cloud K8s clusters running non-demanding GPU workloads, configuring NVIDIA GPU time-slicing can significantly reduce GPU costs. Note that NVIDIA GPU time-slicing is intended for non-production test/dev workloads, as it does not enforce memory and fault isolation.

Using NVIDIA GPU time-slicing in a cloud Kubernetes cluster with a cluster autoscaler (CA) that is aware of the time-slicing configuration can significantly reduce costs. A time-slice aware “smart” CA prevents initial over-allocation of instances and optimizes instance selection, and reduces the risk of exceeding quotas and capacity limits.  Also, on GKE, where GPU time-slicing is expected to be configured at the control plane level, a smart CA facilitates using time-slicing on GPU resources that are dynamically allocated.

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