slurm

This work addresses the high cognitive burden on users and excessive carbon emissions associated with scientific computing due to the complexity of the SLURM job scheduler interface and its lack of energy-aware scheduling mechanisms. To mitigate these issues, the authors propose a modular Perl-based toolkit featuring a simplified command-line interface and a text-based user interface (TUI) that supports job monitoring, cancellation, and automatic generation of specialized submission scripts. A key innovation is the introduction of an “eco-mode” that enables automatic energy-efficient scheduling through off-peak workload shifting. This approach significantly lowers the usability barrier, enhances job management efficiency, and effectively reduces the carbon footprint of research computing workflows.

To address the low scheduling efficiency of uncertainty quantification (UQ) workflows on HPC systems—characterized by unknown task counts, highly heterogeneous execution times, and poor compatibility with static batch schedulers (e.g., SLURM)—this paper proposes a lightweight, system-modification-free dynamic co-scheduling framework. The framework innovatively integrates a UQ modeling bridge (a language-agnostic interface) with HyperQueue to enable runtime adaptive load balancing without assuming prior knowledge of task patterns. Evaluated on GS2 gyrokinetic plasma simulations and Gaussian process surrogate model validation, the framework reduces scheduling overhead by three orders of magnitude and cuts CPU time for long-running simulations by up to 38%, significantly outperforming pure SLURM-based scheduling. It establishes a new, efficient, and portable scheduling paradigm for large-scale, heterogeneous UQ simulations.

To address challenges in high-performance computing (HPC) environments—including heterogeneous LLM deployment, inflexible resource scheduling, and significant performance volatility under multi-model concurrent inference—this paper proposes a scalable LLM inference engine architecture built atop SLURM. The architecture integrates containerized microservices with dynamic resource orchestration, enabling fine-grained, coordinated allocation of CPU, GPU, and memory resources, and provides unified access via RESTful APIs to support both batch and interactive inference workloads. A novel multi-step “tribunal” refinement workflow is introduced to enhance fault tolerance and operational flexibility. Experiments on Llama-series models across multi-node HPC clusters demonstrate sub-50 ms latency and 128 concurrent requests for smaller models (e.g., Llama-3-8B), and stable dual-concurrent execution for large models (e.g., Llama-3-70B), with low scheduling overhead and strong horizontal scalability. The system has been successfully deployed in production applications, including retrieval-augmented generation chatbots.

To address the challenge of unified management and scheduling of quantum computing resources in high-performance computing (HPC) environments, this paper proposes an architecture that deeply integrates quantum devices as first-class computational resources into mainstream HPC job schedulers—specifically Slurm. Methodologically, we design a standardized quantum resource interface and an extensible plugin mechanism, implementing a lightweight integration framework comprising a RESTful API, QASM/YAML-based resource description models, and a quantum runtime adaptation layer. Our approach innovatively enables automatic discovery, on-demand allocation, and full-lifecycle management of heterogeneous quantum hardware platforms—including IBM, Rigetti, and IonQ. The framework has been deployed and validated across multiple HPC centers, achieving sub-200 ms scheduling latency, compatibility with over 90% of classical job workflows, and substantial improvements in both availability and scheduling efficiency for hybrid quantum–classical applications.

SLURM logs in HPC scientific workflows lack explicit case identifiers, hindering direct application of process mining. Method: This paper proposes an automatic job-correlation method based on implicit job dependency modeling—parsing SLURM logs and jointly leveraging spatiotemporal job feature matching and graph-structured modeling to achieve end-to-end clustering of unannotated jobs. Contribution/Results: We introduce the first systematic preprocessing framework for process mining on HPC logs, integrating algorithms such as Heuristics Miner to support process discovery and bottleneck diagnosis. Evaluated on real-world HPC cluster logs, our approach significantly improves workflow traceability, accurately identifies I/O- and scheduler-related performance bottlenecks, and enables high-fidelity reconstruction of end-to-end process models.

This work addresses the inefficiencies of traditional rigid job scheduling in high-performance computing (HPC) clusters, which often result in low resource utilization and prolonged job waiting times. The authors propose a novel malleable job scheduling strategy that dynamically adjusts resource allocations at runtime while prioritizing each job’s preferred configuration. They systematically investigate the interplay among workload characteristics, the proportion of malleable jobs, and scheduling policies. Using the ElastiSim simulation framework and real-world workload traces from the Cori, Eagle, and Theta supercomputers, they evaluate five scheduling strategies across malleable job ratios ranging from 0% to 100%. Experimental results demonstrate that, compared to fully rigid scheduling, the best-performing strategy reduces job turnaround time by 37–67%, shortens makespan by 16–65%, decreases waiting time by 73–99%, and improves node utilization by 5–52%.

This work addresses the lack of a secure, efficient, and compatible programmatic interface in existing Slurm schedulers, which hinders integration with scientific tools and automation systems. We propose Palmetto API, a lightweight RESTful proxy for slurmrestd that introduces, for the first time, fine-grained role-based access control (RBAC) and HTTP response caching while maintaining full compatibility with existing clients. This design significantly enhances interface security and performance, effectively reducing latency from redundant requests. Compatibility validation confirms seamless interoperability with the current Slurm ecosystem, enabling straightforward adoption without disrupting established workflows.

This work addresses the challenge of static resource allocation in production-grade HPC clusters, which struggles to accommodate the time-varying resource demands of scientific applications. To overcome this limitation, the authors propose a non-intrusive MPI malleability approach built upon a dynamic resource management (DRM) framework and leveraging the standard MPI malleability API. The method enables transparent, runtime resource adaptation without requiring modifications to either application code or the underlying scheduler. As the first such solution deployed in real-world production environments, it maintains compatibility with mainstream HPC software stacks and resource managers. Validated on three TOP500 supercomputers, the approach achieves performance comparable to static allocation while substantially reducing node-hours for equivalent workloads, thereby significantly lowering the barrier to adopting elastic scheduling in HPC systems.

This work addresses the inefficiencies of static resource allocation in molecular dynamics simulations, which often leads to idle resources, queuing delays, and increased node-hour costs due to its inability to adapt to time-varying workloads. For the first time, MPI process elasticity is implemented in GROMACS by integrating a dynamic resource management (DRM) middleware that leverages the Slurm workload manager, GROMACS’ native checkpoint/restart mechanism, and a communication-efficiency-aware reconfiguration strategy to enable runtime scaling. Experiments on the MareNostrum 5 supercomputer demonstrate that the proposed approach significantly reduces node-hour consumption and reconfiguration overhead, effectively shortens simulation turnaround time, and substantially improves overall resource utilization efficiency.

This work addresses the technical and behavioral challenges of transitioning from node-exclusive to resource-aware scheduling in production-grade heterogeneous HPC systems, a shift that risks disrupting established scientific workflows. To enable seamless, non-disruptive migration, the authors propose a collaborative operational framework integrating a time-bound compatibility layer, observability-driven feedback mechanisms, and targeted user guidance. Built upon Slurm’s TRES resource model, the approach combines runtime compatibility support, job queue monitoring, and user behavior analysis to preserve workflow continuity while substantially improving scheduling efficiency. Empirical results demonstrate dramatic reductions in median queue wait times—from 277 minutes to under 3 minutes for CPU jobs and from 81 minutes to 3.4 minutes for GPU jobs—alongside high long-term adoption rates among users who embraced the new submission paradigm.