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    Extreme Science and Engineering Discovery Environment

    Extreme Science and Engineering Discovery Environment

    Conference

    年发文量

    研究主题

    论文(1317)

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    1Bridging the Cyberinfrastructure Training Gap: Assessing the Impact of the COMPLECS HPC Training Program for Research End-Users.
    Nicole Wolter, Marty Charles Kandes,Monica Sweet,Mahidhar Tatineni,Mary Thomas,Robert Sinkovits

    The COMPLECS CyberTraining program addresses the gap between traditional developer-centric high-performance computing training and the practical needs of modern researchers. Using a three-tier curriculum, the program has impacted more than 1,700 participants in its first 30 months, hosting 43 webinars and 5 immersive, multi-day workshops. Here, we review the program's growth and offer insights for cyberinfrastructure training and improved strategies for user engagement and resource dissemination.

    2026
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    2Developing Cyberinfrastructure Professionals As Research Partners: Lessons from the First Cohort of CIPIVOT
    Winona Snapp-Childs, Elizabett A. Hillery,Julie A. Wernert,Scott Michael,David Y. Hancock,Kosali Simon,Preston M. Smith,Matthew Huber,Abhinav Thota, Douglas Schultz, Michael Navicky, Eric Collins,

    The CI-PIVOT project, a collaboration between Indiana University, Purdue, and Mississippi State, addresses the gap between complex HPC/AI resources and domain researchers by transforming science experts into embedded Cyberinfrastructure Professionals (CIPs). Through an 18-month cohort model, this project establishes a scalable framework for professionalizing the CIP role, providing insights into training that can be generalized across the broader research ecosystem.

    2026
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    3Mapping Scientific Computing Capacity in Africa: Insights from a Preliminary Discovery Survey.
    Bryan Johnston, Katherine Elizabeth Johnston,Suresh Maslamoney

    This paper addresses the preliminary findings of the discovery survey conducted to establish an initial picture of High-Performance Computing (HPC) resources across Africa. The pilot survey yielded 51 complete submissions from 23 countries, revealing a landscape far more diverse - and in many cases more capable - than previously documented. It also revealed several previously unknown systems, underscoring the value of coordinated continental data-gathering. Simultaneously, the survey exposed methodological and engagement-related limitations that must be addressed before a reliable, long-term HPC catalogue can be effectively realised. We reflect on the execution of the preliminary survey, examining issues such as inconsistent data quality, uneven community participation, gaps in technical detail, and the challenges of surfacing HPC assets in highly resource-constrained environments. Drawing on these observations, we outline the key lessons learned and define the plans for a more robust and targeted follow-up survey. Our aim is to refine the methodology, strengthen community coordination, and build toward a validated, sustainable framework for representing HPC capacity across Africa. Throughout the manuscript, we acknowledge the survey's limitations and use the preliminary dataset to present a snapshot of the current scientific computing infrastructure landscape in Africa.

    2026
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    4Integrating Heterogeneous HPC Resources into National Cyberinfrastructure: A Dual-SidedWorkflow Ecosystem for "ACCESS" Resource Providers and Staff.
    Esen Gokpinar Shelton,John-Paul Navarro,Dinuka De Silva, Eric Blau,Winona Snapp-Childs

    This paper presents a dual-sided software ecosystem for the NSFfunded ACCESS project, built to streamline the onboarding of heterogeneous HPC resources. Using user-centered design and cognitive frameworks, the application replaces fragmented, high-load processes with a unified workflow that links Resource Providerfacing onboarding pathways to staff verification. Its UI/UX, built around a roadmap-badge-task model, provides a scalable approach for coordinating multi-actor HPC platforms and governance-intensive environments.

    2026
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    5Using AI-coding to Quickly Create Custom System Administration Tools.
    Vikram Gazula, Sandesh Lamichhane, Bhushan Chitre, Robert Quick,James Griffioen

    Advanced system administration tools, particularly tools that are specifically designed for, and have direct knowledge of the local HPC environment, can greatly improve the productivity of SysAdmins, but often require in-house development efforts beyond the capabilities of many HPC centers. However, recent advances in AI-coding are making it possible for HPC centers with limited development resources to create custom SysAdmin tools quickly and easily. This paper describes our use of AI-coding (via Claude Code) to develop two custom SysAdmin tools (a Cluster Explorer and a Realtime Chatbot) designed to meet our specific needs and tailored to our local HPC environment. We describe our experience with AI code generation, the types of prompts we used to successfully create two highly useful tools in a very short amount of time, and lessons learned.

    2026
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    高被引作者

    作者引用发文
    Thomas R Furlani68518
    John Towns5945
    Shelley L. Knuth5638
    Ian Foster55716
    Dan Stanzione5536
    Nick Nystrom3964
    Steven Tuecke3677
    Matthew W. Vaughn3676
    David Y. Hancock3669
    Dhabaleswar Panda36614

    高产作者

    作者引用发文
    Suresh Marru29342
    Marlon Pierce28539
    Eroma Abeysinghe11628
    Sudhakar Pamidighantam12428
    Marcus Christie11224
    Craig A. Stewart35022
    Mahidhar Tatineni23322
    Dhruva K Chakravorty7922
    Joe Stubbs6121
    Lisa M Perez7021

    相关期刊

    Computing in Science and Engineering
    Parallel Computing
    Software Composition
    International Conference for High Performance Computing, Networking, Storage and Analysis
    e-Science and Grid Computing
    International Advanced Research Workshop on High Performance Computing
    Platform for Advanced Scientific Computing
    Supercomputing frontiers and innovations
    International Journal of High Performance Computing Applications
    IEEE International Conference on High Performance Computing, Data, and Analytics