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GPU Glossary/Cluster Management
Cluster Management

Job Scheduling

Allocating GPU cluster resources using FIFO, fair-share, or priority-based policies.

What it is

Job scheduling maps GPU workloads -- training jobs, inference services, and batch processing -- to available cluster resources based on scheduling policies, resource requirements, and hardware topology constraints. The three dominant algorithms are FIFO (simple but causes head-of-line blocking), fair-share (allocates proportional to configured team shares), and priority-based backfill (numeric priorities with opportunistic smaller-job filling to maximize utilization). Modern GPU schedulers like Slurm, Kubernetes with Volcano or Kueue, and Run:ai implement GPU-aware constraints including GRES types, topology affinity, and anti-affinity rules.

Why it matters

Scheduling decisions determine placement quality: poor placement across congested switch tiers can degrade distributed training throughput by 30% or more compared to topology-optimal placement. Scheduling a job onto GPUs with early ECC degradation guarantees a mid-run failure. Queue wait time and cluster utilization efficiency are directly controlled by scheduling policy choices, with significant financial impact at scale.

How to monitor

Monitor queue depth, wait times, and cluster utilization via squeue and sinfo in Slurm or Kubernetes queue APIs. Track job placement quality by correlating NCCL throughput with network topology locality. Factryze feeds real-time GPU health scores into scheduler decisions, ensuring jobs are never placed on GPUs showing early degradation signals, and provides cluster-wide visibility into scheduling efficiency and resource fragmentation.

Job Scheduling - GPU Cluster Scheduling PipelineJob Scheduling - GPU Cluster Scheduling Pipeline
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Job Scheduling - GPU Cluster Scheduling PipelineJob Scheduling - GPU Cluster Scheduling Pipeline

Related terms

Slurm (Cluster Management)

Open-source HPC workload manager scheduling GPU cluster jobs via srun, sbatch, and squeue.

Gang Scheduling (Cluster Management)

Atomic co-scheduling of all GPUs for distributed training requiring synchronized start.

Topology-Aware Placement (Cluster Management)

Scheduling GPU jobs by NVLink domain, NUMA affinity, and network switch locality.

Monitor this automatically

Factryze correlates GPU signals in real time: errors, clocks, and fabric health.

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