Researchers run long batch training jobs in a shared HPC-style cluster. Which scheduler is common here?
Why C is the answer
Slurm
Slurm is the standard batch scheduler for HPC and is widely used for training.
In detail
- Slurm is a common scheduler for HPC-style batch work.
- Kubernetes suits long-running services.
Use case
An illustrative scenario, not a real incident.
Researchers submit long training jobs, and Slurm queues them for shared GPUs.
See it in different ways
flowchart TD
A{"What does the workload look like?"} -->|"Batch jobs"| B["Slurm"]
A -->|"Containers and services"| C["Kubernetes"]mindmap
root((Operations))
Monitoring
nvidia-smi
DCGM
Scheduling
Slurm
Kubernetes
Provisioning
Base Command Manager
Sharing
MIG
vGPUsequenceDiagram participant P0 as Researcher participant P1 as Slurm participant P2 as GPU nodes P0->>P1: Submit job P1->>P2: Allocate when free P2-->>P0: Results
Key point: Slurm schedules batch jobs.
Why the other options are wrong
- A. Docker Compose
- Compose runs local containers.
- B. Ansible
- Ansible configures systems.
- D. Apache Airflow
- Airflow orchestrates data workflows.