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10 layers580 nodes2,376 dependencies9 chokepoints112 bottlenecks6,500+ companiesnode size = companies identified

HPC batch schedulers (Slurm and successors)

BOTTLENECK

SchedMD's Slurm dominates academic clusters but cannot scale elastically, forcing reliance on rigid queue-based architectures.

Queue-based batch systems like Slurm, inherited from supercomputing, still rule academic and national-lab training clusters. They handle large-node jobs and priority queues well but cannot scale elastically like cloud-native tools. SchedMD and HPC integrators earn licensing and support revenue.

Queue-based workload managers from the HPC tradition, dominant in large-scale training and research clusters.

What the evidence shows

Many places are coalescing around Slurm, making it the most popular batch scheduler.

reddit.com

Slurm is now the most popular batch scheduler, but 10 years ago it was PBS/TORQUE.

mattermodeling.stackexchange.com

Slurm has overtaken PBS in research and industry, leading to declining adoption of PBS.

vantagecompute.ai
RESCORED JUL 2026near-monopolyscaling3 companies

Who controls it

No named supplier is publicly confirmed for this node yet.

No independently verified market-size figure is published for this node yet.

Where it sits in the stack

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Related nodes

Container orchestration (Kubernetes and AI extensions)AI-native cluster and fabric managers

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