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Topology-Aware Scheduling

Topology-aware scheduling places jobs with the communication network in mind. The right GPU count is not enough when ranks share slow or congested paths.

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RELATED CONCEPTS

Topology-Aware Communication Gang Scheduling with Kueue and Volcano Rail-Optimized and Fat-Tree Fabrics NVLink, NVSwitch and PCIe Kubernetes GPU Scheduling Training Cluster Design at 10k GPUs

LESSONS THAT TEACH THIS

Fleet and ReliabilityAllocated, active and placeable are different capacity measurements14 min→Fleet and ReliabilityPlace communication groups against measured topology14 min→

PRACTICE THIS IN REAL QUESTIONS

Kubernetes, Slurm & GPU SchedulingTwo identical 8-GPU jobs get 8 GPUs each. One runs at half the speed of the other. What did the scheduler do, and how do you stop it?→Coding for InfraPlace GPU jobs onto nodes. Compare first fit, best fit and worst fit, and say which one a training cluster wants.→Kubernetes, Slurm & GPU SchedulingWhat is gang scheduling, and what goes wrong on a Kubernetes cluster that does not have it?→Kubernetes, Slurm & GPU SchedulingKubernetes device plugins versus Dynamic Resource Allocation: what changes for GPU scheduling, and what would you adopt in 2026?→Kubernetes, Slurm & GPU SchedulingEight research teams share 1,024 GPUs. Design the quota and fairness policy, and tell me how they will game it.→Hardware, Cabling & Cluster Build-OutFor 64 to 72 GPUs, is one GB300 NVL72 rack better than eight HGX B300 nodes?→