The mechanism
A distributed job needs a compatible set of devices, enough local memory and the connections required by its partitioning. Having many free devices across a cluster does not guarantee that a sufficiently connected group is available. A scheduler considers resource requests, placement, reservations and policy; Slurm’s topology guide shows how connectivity can inform selection. The broad concept of admitting a parallel job together is distinct from Slurm’s particular time-sliced gang-scheduling feature. Fragmentation, priority and reserved headroom can all leave physically healthy hardware temporarily idle. Useful scheduling therefore balances job completion and service targets rather than maximizing a single occupancy number.
- Specify each job’s complete resource and communication requirements.
- Identify healthy free resources with compatible memory and software configurations.
- Evaluate whether the required connected group exists under current reservations.
- Choose admission and placement according to explicit completion-time or latency objectives.
- Measure wait time, fragmentation and useful progress before changing scheduling policy.
Science and assumptions
Allocation feasibility
An allocation is feasible only when the job simultaneously meets its resource-count, memory, compatibility, topology and policy constraints. Each check applies to the same requested allocation.
Each term is a Boolean constraint for the requested allocation.
Assumptions A pedagogical constraint model; real schedulers also handle failures, priorities, time and implementation-specific resources.
Engineering example
Synthetic cluster has four free devices in each of two slow-linked domains. An eight-device job requiring one local domain waits, while two independent four-device jobs may fit immediately.
Distinctions and tradeoffs
An idle device is not always wasted capacity; it may be an unusable fragment or reserved headroom for a defined service target.
Tight placement can improve communication while increasing queue wait; backfilling smaller jobs uses gaps but must respect promised reservations.
Model shards that fit individually can still wait because their group cannot be placed on a suitable topology.
Specifications and interfaces
- Resource request
- Devices, memory, topology and duration
A count alone omits important compatibility constraints.
- Utilization versus goodput
- Busy resources versus useful accepted work
Unproductive retries or deadline misses can inflate activity.
Interfaces
- Queued jobs and policy Input
- Required resources, priorities and placement constraints
- Admitted allocation Output
- A compatible job resource set and execution start
Check your understanding
Why can an eight-device job wait while eight devices are free?
- The scheduler cannot count beyond seven
- The free devices may not form a compatible required topology
- Device counts automatically guarantee placement
Answer and reasoning
The free devices may not form a compatible required topology
Memory, software and connectivity constraints can prevent a collection of free devices from forming a valid allocation.
Evidence and scope
No real user queue or reservation is inferred. Slurm’s named gang-scheduling implementation is not conflated with generic simultaneous parallel-job admission.
Propagate a stated facility power budget, PUE, readiness and utilization to illustrative work; detailed conversion, battery, cooling and grid models are not included.