NVIDIA NCP-AIO Exam - Topic 2 Question 4 Discussion

Actual exam question for NVIDIA's NCP-AIO exam

Question #: 4
Topic #: 2

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?

AReduce memory allocation per job so more jobs can run concurrently, freeing up resources faster for multi-GPU workloads.

BEnsure that job scripts use --gres=gpu:<number> and configure Slurm's backfill scheduler to prioritize multi-GPU jobs efficiently.

CSet up separate partitions for single-GPU and multi-GPU jobs to avoid resource conflicts between them.

DIncrease time limits for smaller jobs so they don't interfere with multi-GPU job scheduling.

Show Suggested Answer

Suggested Answer: B

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/configure Slurm'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 Val at Oct 03, 2025, 02:43 AM

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Willie

5 months ago

Reducing memory allocation? Sounds risky.

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Tiera

5 months ago

I think C could help a lot too.

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Misty

6 months ago

I’m surprised this is even an issue with available resources!

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Linsey

6 months ago

Wait, why would increasing time limits help?

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Justine

6 months ago

B is definitely the way to go!

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Yasuko

6 months ago

Increasing time limits for smaller jobs sounds familiar, but I feel like that might just delay the multi-GPU jobs even more.

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Silvana

6 months ago

I practiced a similar question where adjusting memory allocation helped, but I’m not convinced it would solve the waiting issue for multi-GPU jobs.

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Rodrigo

7 months ago

I think setting up separate partitions might be a good idea to avoid conflicts, but I wonder if that could complicate scheduling overall.

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Pearlene

7 months ago

I remember reading about how using --gres=gpu: can help with resource allocation, but I'm not sure if that's the best option here.

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Kristeen

7 months ago

I'm leaning towards option C. Keeping the workloads separate seems like the safest bet to ensure the multi-GPU jobs get the resources they need. But I'll need to double-check the details to make sure I'm not missing anything.

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Caprice

7 months ago

Separating the partitions for single and multi-GPU jobs seems like a good way to avoid conflicts. But I'm not sure if that's the most efficient use of resources. Maybe I should explore options B and C to see which one would work better.

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Silvana

7 months ago

Hmm, I think the key here is to make sure Slurm is prioritizing the multi-GPU jobs correctly. Option B sounds promising - I'll need to look into configuring the backfill scheduler properly.

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Earlean

8 months ago

This is a tricky one. I'd need to really understand how Slurm's scheduling works to figure out the best approach. Maybe I should start by reviewing the Slurm documentation on multi-GPU job management.

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