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      "title": "Explore in Atlas",
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      "title": "SchedMD — Slurm Generic Resource Scheduling",
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      "url": "https://semiaifoundry.com/learn/atlas/rack/"
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  "content": "System operations · Atlas v1.3.0\n\n# Cluster scheduler and resource allocation\n\nMatches queued workloads to available compute resources under operational policies.\n\n[Explore in Atlas](https://semiaifoundry.com/ai-atlas/#scheduler)Cluster-wide logical control plane\n\nLearning objectiveUse Little’s law with a declared queue boundary.\n\n## The mechanism\n\nA cluster scheduler turns shared hardware into an organized service. Jobs request resources such as CPUs, GPUs, memory, and time, and the scheduler allocates a compatible set when policies and availability permit. Slurm, one common example, represents GPUs as schedulable resources and exposes assigned devices to job steps. The atlas shows requests entering a queue, resource groups being reserved, and workers launching on those allocations. It distinguishes this job-level decision from the GPU hardware scheduler that issues instructions and from the serving scheduler that batches inference requests. Good placement can improve communication locality, but a scheduler cannot remove the algorithm's communication requirements or manufacture capacity that the cluster does not have.\n\n1.  Read a job’s resource request and policy constraints.\n2.  Choose an available compatible allocation.\n3.  Launch and track the job while releasing resources when it finishes.\n\n## Science and assumptions\n\n### Little’s law\n\n$$\nL=\\lambda W\n$$\n\nLittle’s law states that mean in-system jobs equal the admitted job rate times mean time in the system.\n\nSymbols and units\n\nL\n\nMean admitted jobs inside the selected system\n\njobs\n\nλ\n\nStable admitted arrival/completion rate\n\njobs/s\n\nW\n\nMean time each admitted job spends in the system\n\ns\n\nAverage in-system job count L equals admitted arrival rate λ times average time W spent within the chosen system boundary. It links workload concurrency to waiting and service time.\n\nAssumptions Stable long-run averages with consistent arrival and population definitions; not a prediction of each job’s latency or an overloaded transient queue.\n\n## Engineering example\n\nAt 100 requests/s and 200 ms average latency, average in-flight work is about 20 requests.\n\n## Distinctions and tradeoffs\n\nCluster scheduling and GPU instruction scheduling occur at different levels of the machinery.\n\nPacking improves utilization. Queue fairness, locality, and job constraints can compete.\n\nLarge training jobs need coordinated resource reservations. Allocation decisions influence waiting time and communication placement.\n\n## Specifications and interfaces\n\nExample system\n\nSlurm GRES scheduling\n\nGPU-aware resource allocation.\n\nAllocation inputs\n\nResource request + policy + availability\n\nActual rules depend on the installation.\n\nBoundary\n\nJob placement\n\nDistinct from kernel instruction scheduling.\n\n### Interfaces\n\nJob requests and cluster state Input\n\nResource requirements and availability\n\nWorker allocation Output\n\nAssigned execution resources\n\n## Check your understanding\n\n### What does a cluster scheduler primarily decide?\n\n1.  Which available resources a job receives\n2.  The position of every electron in a GPU\n3.  The next token probability inside a model\n\nAnswer and reasoning\n\nWhich available resources a job receives\n\nIt allocates and manages execution resources; lower software and hardware layers execute the assigned workload.\n\n## Evidence and scope\n\nStationarity and a well-defined system boundary are required.\n\nNo current calculator directly models this lesson’s main mechanism. Use the library for related bounds while retaining their stated omissions.\n\n## Sources\n\n-   [SchedMD — Slurm Generic Resource Scheduling](https://slurm.schedmd.com/gres.html)\n-   [MIT — Queueing Models and Little’s Law](https://web.mit.edu/1.041/www/lectures/L8-queuing-models-2024sp.pdf)\n\n[Published lesson data](https://semiaifoundry.com/ai-atlas/lesson-data/scheduler-de53c3d9a7b4f4a0.json) · [Atlas releases and licensing](https://semiaifoundry.com/ai-atlas/licenses.html)\n\n## Continue exploring\n\n### Foundations\n\n-   [Host CPU](https://semiaifoundry.com/learn/atlas/host-cpu/)\n\n### Related lessons\n\n-   [Integrated accelerator rack: GB200 NVL72 example](https://semiaifoundry.com/learn/atlas/rack/)\n-   [Data, tensor, pipeline, and state parallelism](https://semiaifoundry.com/learn/atlas/parallelism/)\n-   [Dataset and checkpoint storage hierarchy](https://semiaifoundry.com/learn/atlas/storage/)\n-   [Distributed checkpoint and recovery state](https://semiaifoundry.com/learn/atlas/checkpoint/)\n-   [Inference serving and request scheduling](https://semiaifoundry.com/learn/atlas/serving/)"
}