The mechanism
A 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.
- Read a job’s resource request and policy constraints.
- Choose an available compatible allocation.
- Launch and track the job while releasing resources when it finishes.
Science and assumptions
Little’s law
Little’s law states that mean in-system jobs equal the admitted job rate times mean time in the system.
Symbols and units
- L
- Mean admitted jobs inside the selected system
jobs
- λ
- Stable admitted arrival/completion rate
jobs/s
- W
- Mean time each admitted job spends in the system
s
Average 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.
Assumptions Stable long-run averages with consistent arrival and population definitions; not a prediction of each job’s latency or an overloaded transient queue.
Engineering example
At 100 requests/s and 200 ms average latency, average in-flight work is about 20 requests.
Distinctions and tradeoffs
Cluster scheduling and GPU instruction scheduling occur at different levels of the machinery.
Packing improves utilization. Queue fairness, locality, and job constraints can compete.
Large training jobs need coordinated resource reservations. Allocation decisions influence waiting time and communication placement.
Specifications and interfaces
- Example system
- Slurm GRES scheduling
GPU-aware resource allocation.
- Allocation inputs
- Resource request + policy + availability
Actual rules depend on the installation.
- Boundary
- Job placement
Distinct from kernel instruction scheduling.
Interfaces
- Job requests and cluster state Input
- Resource requirements and availability
- Worker allocation Output
- Assigned execution resources
Check your understanding
What does a cluster scheduler primarily decide?
- Which available resources a job receives
- The position of every electron in a GPU
- The next token probability inside a model
Answer and reasoning
Which available resources a job receives
It allocates and manages execution resources; lower software and hardware layers execute the assigned workload.
Evidence and scope
Stationarity and a well-defined system boundary are required.
No current calculator directly models this lesson’s main mechanism. Use the library for related bounds while retaining their stated omissions.