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.

Climate events like tornadoes, hurricanes, and droughts are major twenty-first-century challenges. Mitigation efforts lag due to costly monitoring tools and complex data access. We present a dashboard with long-term temperature and weather trends, using nine global metrics from the NEX-GDDP-CMIP6 dataset, projecting to 2100. Our analysis flags countries facing major wet-bulb temperature hikes, linking these to expected economic impacts. Initial findings suggest that, in a worst-case scenario, some Northern Hemisphere nations may experience over a 10°C wet-bulb temperature rise between 2020 and 2090, potentially causing GDP losses exceeding 30%.

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.

Ensuring reproducibility in high-performance computing (HPC) applications is a significant challenge, particularly when nondeterministic execution can lead to untrustworthy results. Traditional methods that compare final results from multiple runs often fail because they provide sources of discrepancies only a posteriori and require substantial resources, making them impractical and unfeasible. This paper introduces an innovative method to address this issue by using scalable capture and comparing intermediate multi-run results. By capitalizing on intermediate checkpoints and hash-based techniques with user-defined error bounds, our method identifies divergences early in the execution paths. We employ Merkle trees for checkpoint data to reduce the I/O overhead associated with loading historical data. Our evaluations on the nondeterministic HACC cosmology simulation show that our method effectively captures differences above a predefined error bound and significantly reduces I/O overhead. Our solution provides a robust and scalable method for improving reproducibility, ensuring that scientific applications on HPC systems yield trustworthy and reliable results.