KINTSUGI GPU-Only SLURM Scheduling FAQs

Question 1

What hardware is this optimized for?

Accepted Answer

The research and configurations are verified on HiPerGator using NVIDIA B200 and Turin GPUs, but the logic is adaptable to most SLURM-based HPC environments.

Question 2

How does this skill handle workload overflow?

Accepted Answer

It implements a round-robin assignment strategy across GPU accounts, allowing overflow cycles to queue in SLURM. Queuing for a GPU is still significantly faster than immediate execution on a CPU.

Question 3

Can I use BaSiC caching to speed up the process instead?

Accepted Answer

No, this skill references research showing that caching BaSiC flatfields causes 15-20% intensity errors for sparse markers. GPU acceleration is the recommended path for both speed and accuracy.

Question 4

Why is GPU-only scheduling better than CPU fallback?

Accepted Answer

GPU-only scheduling is up to 25x faster for tasks like stitching because BaSiC illumination correction uses parallel DCT operations on GPUs (CuPy), whereas CPU processing (SciPy) is sequential and much slower.

Question 5

How does it prevent QC tasks from blocking GPU jobs?

Accepted Answer

The skill provides guidance on targeting specific Snakemake rules for registration, ensuring that non-GPU QC dependencies don't consume limited SLURM scheduling slots.

KINTSUGI GPU-Only SLURM Scheduling

Características Principales

Casos de Uso

KINTSUGI GPU-Only SLURM Scheduling

Características Principales

Casos de Uso