Modern HPC systems are placing increasing demands on job schedulers due to their scale and novel hardware. El Capitan’s Rabbit nodes exemplify this challenge: unlike traditional systems where storage is remote and shared, Rabbit nodes wire local NVMe SSDs directly to compute nodes via PCIe, forcing schedulers to actively track storage topology, capacity, and cross-job persistence, concerns they were never designed to handle. We introduce Flux Fiction, a fully plugin-based HPC system emulator built on top of Flux that replays historical job traces to evaluate scheduling policies in Flux. We validate Flux Fiction against the LLNL Tuolumne cluster using two workloads across four queueing policies, achieving a P99-bounded slowdown error below 1 in 7 of 8 experiments and a maximum utilization error of 1.2%. We then use Flux Fiction to explore Rabbit storage scheduling, demonstrating its ability to explore novel scheduling scenarios.

El Capitan, currently the world's largest supercomputer at 1.742 Ex-aflop/s, introduces challenges in scheduling due to its scale and innovative rabbit nodes, which traditional schedulers cannot efficiently handle. Flux, a resource and job management system, handles dynamic resource allocation tailored for exascale systems through its graph-based scheduler, Fluxion. This work introduces the Flux Emulator, a tool designed to test scheduling policies in Fluxion without impacting production systems. The emulator plugs into the real components of Flux and Fluxion to mimic job execution, emulate resource usage, and collect information on how the job behaves. Preliminary tests show negligible overhead introduced by the emulator and demonstrate its effectiveness in evaluating scheduli ng policies, like conservative backfilling, in a fraction of the time required with a real system.

The economics of flash vs. disk storage is driving HPC centers to incorporate faster solid-state burst buffers into the storage hierarchy in exchange for smaller parallel file system (PFS) bandwidth. In systems with an underprovisioned PFS, avoiding I/O contention at the PFS level will become crucial to achieving high computational efficiency. In this paper, we propose novel batch job scheduling techniques that reduce such contention by integrating I/O awareness into scheduling policies such as EASY backfilling. We model the available bandwidth of links between each level of the storage hierarchy (i.e., burst buffers, I/O network, and PFS), and our I/O-aware schedulers use this model to avoid contention at any level in the hierarchy. We integrate our approach into Flux, a next-generation resource and job management framework, and evaluate the effectiveness and computational costs of our I/O-aware scheduling. Our results show that by reducing I/O contention for underprovisioned PFSes, our solution reduces job performance variability by up to 33% and decreases I/O-related utilization losses by up to 21%, which ultimately increases the amount of science performed by scientific workloads.

This paper investigates data-preservation, a feature of scientific workflow middleware (SWM) useful for supporting data provenance and "smart recomputation." We observe that in order for an SWM supporting data preservation to achieve decent performance, it should execute on top of copy-on-write file systems. Unfortunately, most file systems in-use at scientific computing facilities were designed without copy-on-write semantics. In response, we design, implement and evaluate a middleware-level solution that is based on user-provided hints and parallelization. The solution can be deployed on top of current file systems and is able to scale almost arbitrarily. Our validation is based on real use-cases from astrophysics and experiments on a cluster with 4 file systems