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arXiv · 2608.13824

Balancing Workload Performance and Slurm Stress: Four Nextflow Deployment Strategies

Nil Tianchen Mu·William Dizon·Glen Otero·Torey Battelle
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Abstract

Wide Nextflow fan-outs on shared Slurm clusters can submit tens of thousands of short tasks. Deployment settings route them through individual jobs, arrays, or nested schedulers inside enclosing allocations. These settings determine workflow turnaround and RPC volume, a shared cost that can degrade scheduler responsiveness. Existing comparisons evaluate whole workflow systems, while per-task queueing metrics cannot span architectures that dispatch inside existing allocations. We contribute a reproducible measurement protocol and benchmark harness. A clean-start clock begins before backend startup or allocation requests, placing architecturally different backends on a common time axis. Per-user Slurm sdiag counters attribute request count as the primary RPC demand measure and controller processing time as sensitivity context, separate from cluster-wide state. We apply the method to Slurm native dispatch, Slurm job arrays, HyperQueue, and Flux on the shared ASU Phoenix production cluster and a single-user Dev cluster. On Phoenix, every aggregation strategy improves both objectives relative to native dispatch; Flux has the lowest RPC demand, while HyperQueue's fastest median walltime is not stable across replicates. On Dev, arrays and Flux improve walltime, while HyperQueue trades the lowest RPC demand for the slowest completion. The method lets an HPC site compare deployment strategies using both user-visible performance and scheduler impact, then select the fastest strategy within its own RPC-demand limit.

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Nil Tianchen Mu, William Dizon, Glen Otero, Torey Battelle. 2026-08-19. Balancing Workload Performance and Slurm Stress: Four Nextflow Deployment Strategies. https://arxiv.org/abs/2608.13824

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