An operations engineer manages a shared GPU cluster running a mix of training and inference workloads. The job queue contains critical, real-time jobs alongside flexible, multi-node training tasks that can utilize parallel GPU execution, and single-GPU batch jobs. Which queue scheduling policy will minimize latency for critical workloads while maintaining high overall cluster utilization?
Select an answer to reveal the explanation.
Short Explanation and Infographic
Imagine your boss walks in and says, "Our critical real-time application is stalling because someone just submitted a massive, low-priority training run that's hogging all the GPUs!" That's a bad day. If you use a simple First-Come, First-Served scheduler, your high-priority, time-sensitive jobs get stuck behind long-running batch jobs. Not good! And round-robin? It treats a critical microsecond-latency task the same as a background data-mining task. The fix is a smart, priority-aware scheduler. It doesn't just look at who got in line first; it looks at how urgent the job is, how many GPUs are sitting idle, and whether the job can actually be split across multiple GPUs. This keeps the critical stuff moving fast while squeezing every last drop of performance out of your hardware. Trust me, in a production cluster, dynamic, priority-based scheduling is the only way to keep everyone happy.
Full explanation below image
Full Explanation
Managing shared computational resources in an enterprise AI cluster requires balancing two conflicting objectives: meeting strict service-level agreements (SLAs) for critical, latency-sensitive tasks, and maximizing overall hardware efficiency (GPU utilization). A priority-based scheduling policy that incorporates GPU availability and task parallelizability resolves this by dynamically allocating resources based on multi-dimensional criteria: 1. Urgency/Priority: Critical jobs are bumped to the front of the execution queue or allowed to pre-empt lower-priority workloads. 2. Resource Requirements & Availability: The scheduler matches jobs to the exact GPU topography (e.g., NVLink-connected pairs) they require. 3. Parallelizability: Jobs that can scale across multiple nodes are allocated dynamically to absorb idle capacity, while non-parallelizable tasks are confined to single devices, preventing resource fragmentation.
Let's review why the alternative scheduling methods are sub-optimal: - Option A (First-Come, First-Served) causes "head-of-line blocking," where a massive, non-critical training job halts all subsequent workloads, including urgent, real-time tasks. - Option B (Round-Robin) introduces excessive context-switching overhead on GPUs, which is computationally expensive due to VRAM state swap times, and fails to prioritize time-sensitive workloads. - Option D (Longest-Job-First) systematically starves short, critical tasks, leading to unacceptable latency spikes for active users or automated systems relying on real-time model outputs.