High-Performance Computing (HPC) job scheduling involves balancing conflicting objectives such as minimizing makespan, reducing wait times, optimizing resource use, and ensuring fairness. Traditional methods, including heuristic-based, e.g., First-Come-First-Served (FJFS) and Shortest Job First (SJF), or intensive optimization techniques, often lack adaptability to dynamic workloads and, more importantly, cannot simultaneously optimize multiple objectives in HPC systems. To address this, we propose a novel Large Language Model (LLM)-based scheduler using a ReAct-style framework (Reason + Act), enabling iterative, interpretable decision-making. The system incorporates a scratchpad memory to track scheduling history and refine decisions via natural language feedback, while a constraint enforcement module ensures feasibility and safety. We evaluate our approach using OpenAI's O4-Mini and Anthropic's Claude 3.7 across seven real-world HPC workload scenarios, including heterogeneous mixes, bursty patterns, and adversarial cases etc. Comparisons against FCFS, SJF, and Google OR-Tools (on 10 to 100 jobs) reveal that LLM-based scheduling effectively balances multiple objectives while offering transparent reasoning through natural language traces. The method excels in constraint satisfaction and adapts to diverse workloads without domain-specific training. However, a trade-off between reasoning quality and computational overhead challenges real-time deployment. This work presents the first comprehensive study of reasoning-capable LLMs for HPC scheduling, demonstrating their potential to handle multiobjective optimization while highlighting limitations in computational efficiency. The findings provide insights into leveraging advanced language models for complex scheduling problems in dynamic HPC environments.

High-Performance Computing (HPC) is crucial for performing advanced computational tasks, yet their complexity often challenges users, particularly those unfamiliar with HPC-specific commands and workflows. This paper introduces Hypothetical Command Embeddings (HyCE), a novel method that extends Retrieval-Augmented Generation (RAG) by integrating real-time, user-specific HPC data, enhancing accessibility to these systems. HyCE enriches large language models (LLM) with real-time, user-specific HPC information, addressing the limitations of fine-tuned models on such data. We evaluate HyCE using an automated RAG evaluation framework, where the LLM itself creates synthetic questions from the HPC data and serves as a judge, assessing the efficacy of the extended RAG with the evaluation metrics relevant for HPC tasks. Additionally, we tackle essential security concerns, including data privacy and command execution risks, associated with deploying LLMs in HPC environments. This solution provides a scalable and adaptable approach for HPC clusters to leverage LLMs as HPC expert, bridging the gap between users and the complex systems of HPC.

The workshop will be held in conjunction with SC22: The International Conference for High Performance Computing, Networking, Storage and Analysis located in Dallas, TX on November 13 - 18, 2022. The intent of this workshop is to bring together researchers, practitioners, and scientific communities to discuss methods that utilize extreme scale systems for machine learning. This workshop will focus on the greatest challenges in utilizing HPC for machine learning and methods for exploiting data parallelism, model parallelism, ensembles, and parameter search. We invite researchers and practitioners to participate in this workshop to discuss the challenges in using HPC for machine learning and to share the wide range of applications that would benefit from HPC powered machine learning.

Traditional and AI-Focused HPC Compute, Storage, Software, and Services: Market Analysis and Forecasts Over the past two decades, enterprises have realized the value of using clusters of computers to solve complex mathematical, computational, and simulation/modeling problems. By addressing these massive problems using parallel computing techniques (allowing the problem to be split into parts that can be tackled by individual or groups of processors), the time to complete a solution can be drastically reduced. However, as enterprises have become more focused on automating manual processes, as well as incorporating some degree of cognition or intelligence into their systems, it has become clear that these processes require the ingestion and analysis of large amounts of data, and single workstation or server-based processing would simply lack the speed and power to provide results in a reasonable amount of time. Tractica forecasts that the overall market for enterprise high performance computing (HPC) hardware, software, storage, and networking equipment will reach $31.5 billion annually by 2025, an increase from approximately $18.8 billion in 2017. The market is currently dominated by HPC equipment utilized for traditional use cases, or situations in which an HPC system is used for heavy-duty number crunching, simulation, and analysis, techniques that require the brute force of cluster computing to reduce the time to complete complex calculations.

How is computer security different in a high-performance computing (HPC) context from a typical IT context? On the surface, a tongue-in-cheek answer might be, "just the same, only faster." After all, HPC facilities are connected to networks the same way any other computer is, often run the same, typically Linux-based operating systems as are many other common computers, and have long been subject to many of the same styles of attacks, be they compromised credentials, system misconfiguration, or software flaws. Such attacks have ranged from the "wily hacker" who broke into U.S. Department of Energy (DOE) and U.S. Department of Defense (DOD) computing systems in the mid-1980s,42 to the "Stakkato" attacks against NCAR, DOE, and NSF-funded supercomputing centers in the mid-2000s,24,39 to the thousands of probes, scans, brute-force login attempts, and buffer overflow vulnerabilities that continue to plague high-performance computing facilities today. On the other hand, some HPC systems run highly exotic hardware and software stacks. In addition, HPC systems have very different purposes and modes of use than most general-purpose computing systems, of either the desktop or server variety. This fact means that aside from all of the normal reasons that any network-connected computer might be attacked, HPC computers have their own distinct systems, resources, and assets that an attacker might target, as well as their own distinctive attributes that make securing such systems somewhat distinct from securing other types of computing systems. The fact that computer security is context- and mission-dependent should not be surprising to security professionals--"security policy is a statement of what is, and what is not, allowed,"7--and each organization, will therefore have a somewhat distinctive security policy.