Jul 2026· Practice and Experience in Advanced Research Computing· pp. 1-4· 0 citations· 7 references
Computer Science
TL;DR
PACES (Providing Accelerated Cybertraining for Emerging Scientists) is a collaborative NSF-funded cybertraining program promoting the adoption of advanced CI by providing hands-on training to researchers seeking to incorporate these resources in their own scientific workflows.
Abstract
The utilization of advanced cyberinfrastructure (CI) for scientific research is rapidly growing, catalyzed by the proliferation of artificial intelligence and machine learning (AI/ML)-enabled tools and research workflows. The NSF has facilitated this growth through programs like ACCESS and NAIRR, but many researchers are still overwhelmed when trying to conduct scientific workflows using advanced CI resources. To effectively utilize these resources, researchers must have a deep understanding of advanced computing technologies, the national CI ecosystem, data management practices, and discipline-specific scientific workflows. PACES (Providing Accelerated Cybertraining for Emerging Scientists) is a collaborative NSF-funded cybertraining program promoting the adoption of advanced CI by providing hands-on training to researchers seeking to incorporate these resources in their own scientific workflows. Since its inception in 2024, PACES has hosted two in-person workshops, hosted 78 virtual courses, and provided 18 asynchronous courses. The workshops and short courses cover a variety of topics, including effective utilization of high-performance computing (HPC) resources; programming in Python, R, and Julia; AI/ML; and domain-specific scientific workflows. This extensive range of topics spans researcher experience levels allowing PACES to provide training at any level, meeting researchers where they are to enable rapid adoption of advanced CI resources.
This paper presents a systematic refactoring approach using Claude agentic AI on real-world project-scheduling workloads in a high-performance computing (HPC) environment, where the agent identifies bottlenecks, implements targeted optimizations, and evaluates their effects, while the researcher retains final control.
It is argued that offering federated computing as an important AmSC service would unlock privacy-constrained scientific collaborations, enabling public-private partnerships in model building while exercising and enhancing the platform's own federated infrastructure.
Zilinghan Li, Abhijith Chunduru, H. Krishnan et al.· 0 citations
Coding agents have become real users of high-performance computing (HPC) systems, yet today's HPC abstractions, interfaces, and policies remain designed for human-driven workflows. In our measurement, users running coding agents are only 19.5% of the observed population, but account for 55.8% of job submissions, 29.1%...
Yun-Jia Zheng, Bintang Dwi Marthen, Zachary Pan et al.· 0 citations
BOOSTEDSOSA is introduced, a dual-FPGA ML-assisted Scheduling architecture that integrates a Machine Learning predictor for expected processing times, with a novel temporal-aware training policy, enabling its use in existing HPC systems.
Adam H. Ross, Riccardo Revalor, Aryan Singh et al.· 0 citations
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