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Introduction to Volume 13 Issue 2
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 13 Issue 2 — December 2022
Contents
Bridging Data Science Programming with Advanced Formal Coursework
Wesley A. Brashear, Zhenhua He, Richard Lawrence, Dhruva K. Chakravorty, Tatevik Sekhposyan, Margaret L. Carpenter, and Honggao Liupp. 2–7
https://doi.org/10.22369/issn.2153-4136/13/2/1BibTeX
@article{jocse-13-2-1,
author={Wesley A. Brashear and Zhenhua He and Richard Lawrence and Dhruva K. Chakravorty and Tatevik Sekhposyan and Margaret L. Carpenter and Honggao Liu},
title={Bridging Data Science Programming with Advanced Formal Coursework},
journal={The Journal of Computational Science Education},
year=2022,
month=dec,
volume=13,
issue=2,
pages={2--7},
doi={https://doi.org/10.22369/issn.2153-4136/13/2/1}
}
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In order to fulfill the needs of an evolving job market, formal academic programs are continuously expanding computational training in traditional discipline-specific courses. We developed an informal, twelve contact-hour course tailored for economics students entering a computationally rigorous graduate-level course to help mitigate disparities in computing knowledge between students and prepare them for more advanced instruction within the formal setting. The course was developed to teach the R programming language to students without assuming any prior knowledge or experience in programming or the R environment. In order to allow for ease of implementation across various training approaches, the course was modularized with each section containing distinct topics and learning objectives. These modules can be easily developed as independent lessons so that discipline-specific needs can be addressed through inclusion or exclusion of certain topics. This implementation used the R package 'learnr' to develop the course, which rendered a highly extensible and portable interactive Shiny document that can be deployed on any system on which RStudio is installed. The course is offered as a series of interactive sessions during which students are led through the Shiny notebook by an instructor. Owing to its structure, it can be offered as an asynchronous web-based set of tutorials as well.
HPC Workforce Development of Undergraduates Outside the R1
Scott Feister and Elizabeth Blackwoodpp. 8–11
https://doi.org/10.22369/issn.2153-4136/13/2/2BibTeX
@article{jocse-13-2-2,
author={Scott Feister and Elizabeth Blackwood},
title={HPC Workforce Development of Undergraduates Outside the R1},
journal={The Journal of Computational Science Education},
year=2022,
month=dec,
volume=13,
issue=2,
pages={8--11},
doi={https://doi.org/10.22369/issn.2153-4136/13/2/2}
}
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Many Research-1 (R1) universities create investments in High Performance Computing (HPC) centers to facilitate grant-funded computing projects, leading to student training and outreach on campus. However, creating an HPC workforce pipeline for undergraduates at non-research-intensive universities requires creative, zero-cost education and exposure to HPC. We describe our approach to providing HPC education and opportunities for students at California State University Channel Islands, a four-year university / Hispanic-Serving Institution (HSI) with a primarily first-generation-to-college student population. We describe how we educate our university population in HPC without a dedicated HPC training budget. We achieve this by (1) integrating HPC topics and projects into non-HPC coursework, (2) organizing a campus-wide data analysis and visualization student competition with corporate sponsorship, (3) fielding undergraduate teams in an external, equity-focused supercomputing competition, (4) welcoming undergraduates into faculty HPC research, and (5) integrating research data management principles and practices into coursework. The net effect of this multifaceted approach is that our graduates are equipped with core competencies in HPC and are excited about entering HPC careers.
Impact of the Blue Waters Fellowship Program
Steven I. Gordon, Scott Lathrop, and William Kramerpp. 12–16
https://doi.org/10.22369/issn.2153-4136/13/2/3BibTeX
@article{jocse-13-2-3,
author={Steven I. Gordon and Scott Lathrop and William Kramer},
title={Impact of the Blue Waters Fellowship Program},
journal={The Journal of Computational Science Education},
year=2022,
month=dec,
volume=13,
issue=2,
pages={12--16},
doi={https://doi.org/10.22369/issn.2153-4136/13/2/3}
}
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The Blue Waters Fellowship program supported by the National Science Foundation focused on supporting PhD candidates requiring access to high performance computing resources to advance their computational and data-enabled research. The program was designed to strengthen the workforce engaged in computational research. As the program developed, a number of modifications were made to improve the experience of the fellows and promote their success. We review the program, its evolution, and the impacts it had on the participants. We then discuss how the lessons learned from those efforts can be applied to future educational efforts.
The Multi-Tier Assistance, Training, and Computational Help (MATCH) Project, a Track 2 NSF ACCESS Initiative
Shelley L. Knuth, Julie Ma, Joel C. Adams, Alan Chalker, Ewa Deelman, Layla Freeborn, Vikram Gazula, John Goodhue, James Griffioen, David Hudak, Andrew Pasquale, Dylan Perkins, Alana Romanella, and Mats Ryngepp. 17–20
https://doi.org/10.22369/issn.2153-4136/13/2/4BibTeX
@article{jocse-13-2-4,
author={Shelley L. Knuth and Julie Ma and Joel C. Adams and Alan Chalker and Ewa Deelman and Layla Freeborn and Vikram Gazula and John Goodhue and James Griffioen and David Hudak and Andrew Pasquale and Dylan Perkins and Alana Romanella and Mats Rynge},
title={The Multi-Tier Assistance, Training, and Computational Help (MATCH) Project, a Track 2 NSF ACCESS Initiative},
journal={The Journal of Computational Science Education},
year=2022,
month=dec,
volume=13,
issue=2,
pages={17--20},
doi={https://doi.org/10.22369/issn.2153-4136/13/2/4}
}
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NSF-supported cyberinfrastructure (CI) has been highly successful in advancing science and engineering over the last few decades. During that time, there have been significant changes in the size and composition of the participating community, the architecture and capacity of compute, storage, and networking platforms, and the methods by which researchers and CI professionals communicate. These changes require rethinking the role of research support services and how they are delivered. To address these changes and support an expanding community, MATCH is implementing a model for research support services in ACCESS that comprises three major themes: 1) leverage modern information delivery systems and simplify user interfaces to provide cost-effective, scalable support to a broader community of researchers, 2) engage experts from the community to develop training materials and instructions that can dramatically reduce the learning curve, and 3) employ a matchmaking service that will maintain a database of specialist mentors and student mentees that can be matched with projects to provide the domain-specific expertise needed to leverage ACCESS resources. A new ACCESS Support Portal (ASP) will serve as the single front door for researchers to obtain guided support and assistance. The ASP will leverage emerging, curated tag taxonomies to identify and match inquiries with knowledge base content and expertise. Expert-monitored question and answer platforms will be created to ensure researcher questions are accurately answered and addressed in a timely fashion, and easy-to-use interfaces such as Open OnDemand and Pegasus will be enhanced to simplify CI use and provide context-aware directed help. The result will be a multi-level support infrastructure capable of scaling to serve a growing research community with increasingly specialized support needs, resulting in research discoveries previously hindered by researchers' inability to effectively utilize NSF CI resources. This paper will cover the components of the MATCH project and discuss how MATCH will engage and work with the ACCESS community.
Impact of Blue Waters Education and Training
William Kramer, Scott Lathrop, Steven I. Gordon, Robert M. Panoff, Aaron Weeden, and Lizanne DeStefanopp. 21–30
https://doi.org/10.22369/issn.2153-4136/13/2/5BibTeX
@article{jocse-13-2-5,
author={William Kramer and Scott Lathrop and Steven I. Gordon and Robert M. Panoff and Aaron Weeden and Lizanne DeStefano},
title={Impact of Blue Waters Education and Training},
journal={The Journal of Computational Science Education},
year=2022,
month=dec,
volume=13,
issue=2,
pages={21--30},
doi={https://doi.org/10.22369/issn.2153-4136/13/2/5}
}
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The Blue Waters proposal to NSF, entitled "Leadership-Class Scientific and Engineering Computing: Breaking Through the Limits," identified education and training as essential components for the computational and data analysis research and education communities. The Blue Waters project began in 2007, the petascale computing system began operations on March 28, 2013, and the system served the community longer than originally planned as it was decommissioned in January 2022. This paper contributes to the Blue Waters project's commitment to document the lessons learned and longitudinal impact of its activities. The Blue Waters project pursued a broad range of workforce development activities to recruit, engage, and support a diverse mix of students, educators, researchers, and developers across the U.S. The focus was on preparing the current and future workforce to contribute to advancing scholarship and discovery using computational and data analytics resources and services. Formative and summative evaluations were conducted to improve the activities and track the impact. Many of the lessons learned have been implemented by the National Center for Supercomputing Applications (NCSA) and the New Frontiers Initiative (NFI) at the University of Illinois, and by other organizations. We are committed to sharing our experiences with other organizations that are working to reproduce, scale up, and/or sustain activities to prepare the computational and data analysis workforce.
Enhancing HPC Education and Workflows with Novel Computing Architectures
Jeffrey Young, Aaron Jezghani, Jeffrey Valdez, Sam Jijina, Xueyang Liu, Michael D. Weiner, Will Powell, and Semir Sarajlicpp. 31–38
https://doi.org/10.22369/issn.2153-4136/13/2/6BibTeX
@article{jocse-13-2-6,
author={Jeffrey Young and Aaron Jezghani and Jeffrey Valdez and Sam Jijina and Xueyang Liu and Michael D. Weiner and Will Powell and Semir Sarajlic},
title={Enhancing HPC Education and Workflows with Novel Computing Architectures},
journal={The Journal of Computational Science Education},
year=2022,
month=dec,
volume=13,
issue=2,
pages={31--38},
doi={https://doi.org/10.22369/issn.2153-4136/13/2/6}
}
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Recent HPC education efforts have focused on maximizing the usage of traditional- and cloud-based computing infrastructures that primarily support CPU or GPU hardware. However, recent innovations in CPU architectures from Arm and RISC-V and the acquisition of Field-Programmable Gate Array (FPGA) companies by vendors like Intel and AMD mean that traditional HPC clusters are rapidly becoming more heterogeneous. This work investigates one such example deployed at Georgia Tech – a joint workflow for processor design and reconfigurable computing courses supported by both the HPC-focused Partnership for an Advanced Computing Environment (PACE) and GT's novel architecture center, CRNCH. This collaborative workflow of HPC nodes and 40 remotely accessible Pynq devices supported over 100 students in Spring 2022, and its deployment provides key lessons on sticking points and opportunities for combined HPC and novel architecture workflows.