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UID:submissions.supercomputing.org_SC25_sess203@linklings.com
SUMMARY:12th SC Workshop on Best Practices for HPC Training and Education
DESCRIPTION:The inherent wide distribution, heterogeneity, and dynamism of
  the current and emerging high-performance computing and software environm
 ents increasingly challenge cyberinfrastructure facilitators, trainers, an
 d educators. The challenge is how to support and train the current multidi
 sciplinary users and prepare the future educators, researchers, developers
 , and policymakers to keep pace with the rapidly evolving HPC environments
  to advance discovery and economic competitiveness for many generations. T
 he twelfth annual full-day workshop on HPC training and education is an AC
 M SIGHPC Education Chapter coordinated effort, aimed at fostering more col
 laborations among the practitioners from traditional and emerging fields t
 o explore educational needs in HPC, to develop and deploy HPC training, an
 d to identify new challenges and opportunities for the latest HPC platform
 s. The workshop will also be a platform for disseminating results and less
 ons learned in these areas and will be captured in a special edition of th
 e Journal of Computational Science Education.\n\nQ/A and Discussion\n-----
 ----------------\nMorning Break - Best Practices for HPC Training and Educ
 ation\n---------------------\nA Retrospective on South Africa's Student Cl
 uster Competition and its Model for Inclusive HPC Outreach and Training (2
 012-2020)\n\nThe Centre for High Performance Computing (CHPC), South Afric
 a’s national supercomputing facility, launched the Student Cluster Competi
 tion (SCC) in 2012 to build HPC awareness and skills among undergraduates.
  Twenty teams of four students began with an intensive week of training in
  Linux, clu...\n\n\nBryan Johnston (Council for Scientific and Industrial 
 Research (CSIR); Centre for High Performance Computing (CHPC), South Afric
 a); Nicholas Thorne (Dell); Matthew Cawood (Texas Advanced Computing Cente
 r (TACC)); Eugene de Beste (Independent); David Macleod (CSIR); and John P
 oole (Clemson University)\n---------------------\nAdvancing HPC skills by 
 developing Large Language Model Retrieval Augmented Generation (LLM-RAG) s
 ystems\n\nLarge Artificial Intelligence (AI) and generative large language
  models (LLM) are key computational drivers. For researchers developing ne
 w tools or incorporating LLMs into their processing pipeline, the scale of
  data and models require supercomputing resources which can only be met th
 rough cloud or...\n\n\nJulia Mullen (MIT Lincoln Laboratory); Sam Corey, L
 auren Milechin, and Riya Tyagi (Massachusetts Institute of Technology (MIT
 )); and Daniel Burrill (MIT Lincoln Laboratory)\n---------------------\nDe
 veloping Findable, Accessible, Interoperable and Reusable (FAIR) AI and HP
 C Training Environments\n\nSunita Chandrasekaran (University of Delaware);
  Vassil Alexandrov (Hartree Centre, STFC); and Sadaf Alam (University of B
 ristol)\n---------------------\nShaping the future workforce: Challenges a
 nd lessons learned in HPC education from national labs and computing cente
 rs\n\nWorkforce training at national laboratories and computing centers is
  essential and typically falls into two categories: foundational training 
 for newcomers and advanced training for experienced users. Foundational to
 pics such as version control, build systems, and basic HPC usage are widel
 y transfer...\n\n\nPatrick Diehl, Ying Wai Li, and Christoph Junghans (Los
  Alamos National Laboratory (LANL)); John K. Holmen, Elijah MacCarthy, and
  Suzanne Parete-Koon (Oak Ridge National Laboratory (ORNL)); Yun He, Rebec
 ca Hartman-Baker, Charles Lively, Kevin Gott, and Lipi Gupta (Lawrence Ber
 keley National Laboratory (LBNL)); Kristina Streu, Yasaman Ghadar, and Pai
 ge Kinsley (Argonne National Laboratory (ANL)); Jane Herriman and Erik W. 
 Draeger (Lawrence Livermore National Laboratory); Victor Eijkhout (Texas A
 dvanced Computing Center); and Susan Mehringer (Cornell University Center 
 for Advanced Computing)\n---------------------\nExperience and Outcomes Or
 ganizing a Hackathon in the Physical Sciences\n\nDespite its growing impor
 tance in physical sciences, research computing with cluster resources rema
 ins difficult to access and sustain, especially in long-term, multi-instit
 utional projects. Challenges include site-specific workflows, evolving sof
 tware stacks, and rapid changes in hardware post-Gene...\n\n\nAaron Jezgha
 ni (Georgia Institute of Technology, The Nab Collaboration) and Jason Fry 
 (Eastern Kentucky University, The Nab Collaboration)\n--------------------
 -\nBuilding Expertise, Connections, and Communities for AI and HPC Trainin
 g and Education: NAIRR Pilot User Experience Group Initiatives\n\nGiven th
 e rapidly changing computing landscape propelled with innovations and conv
 ergence of new cutting-edge technologies such as HPC, AI, Cybersecurity, Q
 uantum computing and more, the accelerated need for upskilling/reskilling 
 the workforce to mitigate skills gaps is becoming increasingly importa...\
 n\n\nNitin Sukhija (Slippery Rock University of Pennsylvania), Shelley Knu
 th and Alana Romanella (University of Colorado Boulder), and Marisa Brazil
  (Arizona State University)\n---------------------\nHPC-ED: Testing Automa
 ted Agents to Assess the Quality of Training Resource Metadata\n\nWe prese
 nt a proof-of-concept system for automating quality assurance (QA) in the 
 HPC-ED federated training catalog using large language models (LLMs). The 
 HPC-ED project aggregates metadata for training resources from multiple pa
 rtner catalogs, improving discoverability for the high-performance com...\
 n\n\nHabiba Morsy (Kean University, University of Virginia); Charlie Dey (
 Texas Advanced Computing Center); Zilu Wang (Cornell University); Mary Tho
 mas (University of California, San Diego (UCSD)); and Essence Toone and Da
 vid Joiner (Kean University)\n---------------------\nAfternoon Break - Bes
 t Practices for HPC Training and Education\n---------------------\nEnhanci
 ng HPC Curriculum through Competitions\n\nHigh Performance Computing (HPC)
  is a critical driver of progress in artificial intelligence (AI), data-in
 tensive science, and engineering. At the National University of Singapore,
  concepts of parallelism are taught in courses such as Parallel Computing 
 and Parallel and Concurrent Programming. These...\n\n\nCristina Carbunaru 
 and Sriram Sami (National University of Singapore)\n---------------------\
 nLunch break (on your own)\n---------------------\nInvestigating User Atti
 tudes Towards and Benefits from Integrating AI Assistants into Research Co
 mputing Support\n\nHigh-Performance Computing clusters for research comput
 ing, hosted by universities, are essential for the institution's ongoing t
 eaching, learning, and research. The learners have a range of experience a
 nd comfort with such platforms and require support regularly. To assist us
 ers on the Unity Resear...\n\n\nInjila Rasul and Georgia Stuart (Universit
 y of Massachusetts Amherst)\n---------------------\nExpanding the CyberAmb
 assadors Program to Include Mentoring for Emerging CI Careers\n\nThis pape
 r describes initial efforts to expand the CyberAmbassadors program (NSF Aw
 ard #1730137) to include training on mentoring skills for the cyberinfrast
 ructure (CI) workforce. The new curriculum will help CI professionals at a
 ll levels develop the self-assessment, planning, and networking skill...\n
 \n\nKaty Luchini-Colbry, Dirk Colbry, and Julie Rojewski (Michigan State U
 niversity)\n---------------------\nTeaching AI Through Narrative Data: A P
 ractical Framework for Data Science and Retrieval‑Augmented Generation\n\n
 By centering the workshop on a single, richly structured dataset, particip
 ants gained technical skills while developing deeper data intuition and pr
 oblem-solving abilities across the AI/ML pipeline. Moving seamlessly from 
 data familiarization to feature engineering, model selection, and evaluati
 on, ...\n\n\nCharlie Dey and Susan Lindsey (Texas Advanced Computing Cente
 r (TACC); University of Texas, Austin)\n---------------------\nClosing Rem
 arks- HPC Education and Training: Emerging Opportunities and Challenges\n\
 nThe rapid advancement of new HPC technologies has facilitated the converg
 ence of artificial intelligence (AI), big data analytics, and HPC platform
 s to solve complex, large-scale, real-time analytics and applications for 
 scientific and non-scientific fields. Given the dynamism of today’s compu.
 ..\n\n\nNitin Sukhija (Slippery Rock University of PA)\n------------------
 ---\nBuilding Scalable and Inclusive Foundations for HPC: Lessons from UC 
 Merced’s Introductory HPC Training Program\n\nHigh-performance computing (
 HPC) is increasingly vital across diverse disciplines, including those his
 torically underrepresented in computational research, such as sociology, p
 sychology, and the arts. To lower barriers to entry, the University of Cal
 ifornia, Merced (UC Merced) created a 90-minute in...\n\n\nYue Yu (Univers
 ity of California, Merced)\n---------------------\nTwelfth SC Workshop on 
 Best Practices for HPC Training and Education\n\nThe inherent wide distrib
 ution, heterogeneity, and dynamism of the current and emerging high-perfor
 mance computing and software environments increasingly challenge cyberinfr
 astructure facilitators, trainers, and educators. The challenge is how to 
 support and train the current multidisciplinary users...\n\n\nNitin Sukhij
 a (Slippery Rock University of Pennsylvania); Scott Lathrop (University of
  Illinois Urbana-Champaign); Nia Alexandrov (Science and Technology Facili
 ties Council (STFC)); and Julia Mullen (Massachusetts Institute of Technol
 ogy (MIT), Lincoln Laboratory)\n\nRecording: Livestreamed, Recorded\n\nReg
 istration Category: Technical Program Reg Pass, Workshop Reg Pass\n\nSessi
 on Chairs: Nitin Sukhija (Slippery Rock University of Pennsylvania); Scott
  Lathrop (University of Illinois Urbana-Champaign); Evguenia Alexandrova (
 Hartree Centre, STFC; Science and Technology Facilities Council (STFC)); a
 nd Julie Mullen (Massachusetts Institute of Technology (MIT), Lincoln Labo
 ratory)
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