Gke Batch Hpc

GKE Batch & HPC Workloads

This reference covers running batch processing and high-performance computing (HPC) workloads on GKE.

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When to Use

• Running batch data processing pipelines
• HPC simulations (CFD, molecular dynamics, financial modeling)
• Large-scale parallel computation (MPI, MapReduce)
• ML training jobs
• CI/CD build farms

Batch Processing on GKE

Kubernetes Jobs

apiVersion: batch/v1
kind: Job
metadata:
  name: batch-job
spec:
  parallelism: 10
  completions: 100
  backoffLimit: 3
  template:
    spec:
      containers:
      - name: worker
        image: 
        resources:
          requests:
            cpu: "1"
            memory: "2Gi"
      restartPolicy: Never

JobSet (for Complex Multi-Job Workflows)

The golden path enables JobSet monitoring (JOBSET in monitoringConfig).

apiVersion: jobset.x-k8s.io/v1alpha2
kind: JobSet
metadata:
  name: training-job
spec:
  replicatedJobs:
  - name: workers
    replicas: 4
    template:
      spec:
        parallelism: 1
        completions: 1
        template:
          spec:
            containers:
            - name: worker
              image: 
              resources:
                requests:
                  cpu: "4"
                  memory: "8Gi"

Kueue (Job Queuing)

Kueue manages job scheduling and resource allocation for batch workloads:

# Install Kueue
kubectl apply --server-side -f https://github.com/kubernetes-sigs/kueue/releases/latest/download/manifests.yaml

# Define a ClusterQueue
apiVersion: kueue.x-k8s.io/v1beta1
kind: ClusterQueue
metadata:
  name: batch-queue
spec:
  namespaceSelector: {}
  resourceGroups:
  - coveredResources: ["cpu", "memory"]
    flavors:
    - name: default
      resources:
      - name: "cpu"
        nominalQuota: 100
      - name: "memory"
        nominalQuota: "200Gi"
---
# Allow a namespace to use the queue
apiVersion: kueue.x-k8s.io/v1beta1
kind: LocalQueue
metadata:
  name: batch-local
  namespace: batch-jobs
spec:
  clusterQueue: batch-queue

HPC on GKE

Compact Placement (Low-Latency Networking)

For tightly-coupled HPC workloads that need low-latency inter-node communication:

# Standard clusters: create node pool with compact placement
gcloud container node-pools create hpc-pool \
  --cluster  --region  \
  --machine-type c3-standard-44 \
  --placement-type COMPACT \
  --num-nodes 8 \
  --enable-autoscaling --min-nodes 0 --max-nodes 16 \
  --quiet

MPI Workloads

Use the MPI Operator for MPI-based HPC applications:

# Install MPI Operator
kubectl apply -f https://raw.githubusercontent.com/kubeflow/mpi-operator/master/deploy/v2beta1/mpi-operator.yaml

apiVersion: kubeflow.org/v2beta1
kind: MPIJob
metadata:
  name: hpc-simulation
spec:
  slotsPerWorker: 4
  mpiReplicaSpecs:
    Launcher:
      replicas: 1
      template:
        spec:
          containers:
          - name: launcher
            image: 
            command: ["mpirun", "-np", "32", "./simulation"]
            resources:
              requests:
                cpu: "1"
                memory: "2Gi"
              limits:
                cpu: "2"
                memory: "4Gi"
    Worker:
      replicas: 8
      template:
        spec:
          containers:
          - name: worker
            image: 
            resources:
              requests:
                cpu: "4"
                memory: "8Gi"
              limits:
                cpu: "8"
                memory: "16Gi"

Cost Optimization for Batch/HPC

Spot VMs for Batch

Batch workloads are ideal Spot VM candidates (interruptible, can checkpoint). Use a ComputeClass with Spot-first priority and activeMigration to return to Spot when available. See the gke-compute-classes skill for the Spot-with-fallback pattern.

Scale-to-Zero

For batch clusters, allow node pools to scale to zero when no jobs are running:

• Autopilot (golden path): Automatic, nodes scale to zero when no pods are

scheduled

• Standard: Set --min-nodes 0 on batch node pools

Best Practices & Production Guidelines

• Resource Quotas: Always specify resource requests and limits (CPU,

memory, and optionally GPU/TPU) for all batch/HPC manifests. This is critical for Kueue admission, autoscaling, and preventing resource starvation in the cluster.

• TPU/Spot Cluster Maintenance: For long-running AI training runs on Spot

VMs/TPUs, advise using GKE maintenance exclusions to block automatic cluster upgrades/reboots during the active training window to minimize unnecessary preemption.

• MPI Workloads: Use the Kubeflow Training Operator to orchestrate

distributed MPI applications via the MPIJob custom resource.

• Kueue & JobSet: Use Kueue for multi-tenant job queueing and fair

sharing; use JobSet for multi-component tightly coupled workloads.

• Resilience: Always set a backoffLimit on Jobs, and implement

application-level checkpointing (e.g., using Orbax or PyTorch checkpointing) to survive Spot VM preemption.

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: google
• Source: google/skills
• License: Apache-2.0

Install and usage instructions live in the source repository linked above.