Workflow management systems like Nextflow are increasingly popular among researchers building computational pipelines, but their default configurations rarely account for the realities of shared HPC clusters. Left untuned, these tools can flood schedulers with thousands of short-lived jobs, request resources they never use, or create bursty submission patterns that degrade cluster performance for all users. This presentation examines Nextflow resource management on SLURM clusters with a focus on the concerns that matter most to HPC operators: scheduler interaction, fair-share impact, resource efficiency, and cluster-wide utilization. Using a computationally demanding genome alignment pipeline as an example, we'll explore how executor configuration, process-level resource directives, and monitoring strategies affect not just individual pipeline performance but overall cluster health. We'll cover common anti-patterns we've encountered—over-provisioned memory requests, runaway task submissions, poor locality awareness—and the configuration and design patterns that prevent them. Whether you're supporting researchers who use workflow managers or evaluating how to integrate them into your site's policies and documentation, the goal is to give you practical knowledge for keeping these tools running well on shared infrastructure.
