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Exam NCP-AIO Topic 5 Question 9 Discussion

Actual exam question for NVIDIA's NCP-AIO exam
Question #: 9
Topic #: 5
You are managing a deep learning workload on a Slurm cluster with multiple GPU nodes, but you notice that jobs requesting multiple GPUs are waiting for long periods even though there are available resources on some nodes.
How would you optimize job scheduling for multi-GPU workloads?

Suggested Answer: B Vote an answer

Comprehensive and Detailed Explanation From Exact Extract:
To optimize scheduling of multi-GPU jobs in Slurm, it is essential to correctly specify GPU requests in job scripts using--gres=gpu:<number>and enable/configureSlurm's backfill scheduler. Backfill allows smaller jobs to run opportunistically in gaps without delaying larger multi-GPU jobs, improving cluster utilization and reducing wait times for multi-GPU jobs. Proper configuration ensures efficient packing and priority handling of GPU resources.

by liutao272 at Nov 07, 2025, 04:39 PM

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liutao272
2025-11-07 16:39:47
The correct answer is C. Set up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts between them.
Why C is correct:
This is a classic Slurm cluster fragmentation issue on multi-GPU nodes.
Even when free GPUs exist, a multi-GPU job (e.g., requesting --gres=gpu:4) cannot start if those free GPUs are scattered across nodes that already have single-GPU jobs running. Slurm’s default backfill scheduler sees the resources as “available” but cannot pack the multi-GPU job because no single node has all 4 GPUs free simultaneously.
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