What is a key advantage of dynamic, priority-based job scheduling in an AI cluster?
A.
It operates completely independently of job priority, user role, or service-level objectives defined for different workloads.
B.
It is designed primarily for lightly utilized or idle clusters, where there is little or no contention for resources.
C.
It ensures time-critical or high-priority workloads receive prompt access to constrained compute resources when contention occurs.
D.
It allocates identical resource shares to every submitted job, regardless of workload type or business impact.
The Answer Is:
C
This question includes an explanation.
Explanation:
The correct answer is C because priority-based scheduling is specifically intended to allocate scarce GPU resources according to workload importance. NVIDIA Run:ai documentation states: “Optimized workload scheduling — Ensure high-priority jobs get GPU resources. Workloads dynamically receive resources based on demand.” NVIDIA also explains that the Run:ai Scheduler “allows the prioritization of workloads across different departments and projects within the organization at large scales, based on the resource distribution set by the system administrator.”
NVIDIA’s scheduler concepts further confirm that “Workload’s priority sets the scheduling precedence within a project,” and that high-priority workloads can preempt lower-priority preemptible workloads in the same scheduling queue. Therefore, the key advantage is that urgent or high-priority workloads can receive timely access to constrained compute resources when resource contention occurs.
[Reference: NVIDIA Run:ai Documentation — Overview; Introduction to Workloads; Scheduler Concepts and Principles.]
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