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Introduction to Volume 14 Issue 2
David Joinerpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 14 Issue 2 — November 2023
Contents
Creating Pathways in Disadvantaged Communities Towards STEM and HPC
Elizabeth Bautista and Nitin Sukhijapp. 2–5
https://doi.org/10.22369/issn.2153-4136/14/2/1BibTeX
@article{jocse-14-2-1,
author={Elizabeth Bautista and Nitin Sukhija},
title={Creating Pathways in Disadvantaged Communities Towards STEM and HPC},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={2--5},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/1}
}
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Today's job market has its challenges in gaining proficient staff but more so in the High Performance Computing area and within a government lab. Competition from industry in terms of the type of perks they provide, being able to negotiate a higher salary and opportunities of remote work all play a part in losing candidates. At the National Energy Research Scientific Computing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL), a site reliability engineer manages the data center onsite 24x7. Further, the facility itself is a unique and complex ecosystem that uses evaporative cooling and recycling of hot air to keep the facility cool. This is in addition to the normal areas to be monitored like the computational systems, the three tier storage, as well as infrastructure and cybersecurity. To explore creating interest into HPC and STEM within the disadvantaged communities near the Laboratory, NERSC partnered with a community college during the pandemic to support high school seniors and freshmen students to provide an educational foundation. In collaboration with the community college, they created a program of specific classes that students needed to take to prepare them for an HPC and/or STEM internships. In certain demographics, students do not believe they can be successful in science or math and require support from the program such as tutors to help them through. With this type of support, students have successfully completed their classes with passing grades. As part of their recruitment process for site reliability engineers to continue to support diversity initiatives at the Laboratory, NERSC implemented an apprenticeship program. This paper describes the current work that includes partnering with a community college program and then NERSC provides a summer internship for the student so they can gain hands-on experience. The first cohort of students have graduated into their internship programs this summer. This paper demonstrates early results from this partnership and how it has impacted the diverse pool of candidates at NERSC.
Cybersecurity and Data Science Curriculum for Secondary Student Computing Programs
Richard Lawrence, Zhenhua He, Dhruva K. Chakravorty, Wesley Brashear, Honggao Liu, Sandra B. Nite, Lisa M. Perez, Chris P. Francis, Nikhil Dronamraju, Xin Yang, Taresh Guleria, and Jeeeun Kimpp. 6–9
https://doi.org/10.22369/issn.2153-4136/14/2/2BibTeX
@article{jocse-14-2-2,
author={Richard Lawrence and Zhenhua He and Dhruva K. Chakravorty and Wesley Brashear and Honggao Liu and Sandra B. Nite and Lisa M. Perez and Chris P. Francis and Nikhil Dronamraju and Xin Yang and Taresh Guleria and Jeeeun Kim},
title={Cybersecurity and Data Science Curriculum for Secondary Student Computing Programs},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={6--9},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/2}
}
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Computing programs for secondary school students are rapidly becoming a staple at High Performance Computing (HPC) centers and Computer Science departments around the country. Developing curriculum that targets specific computing subfields with unmet needs remains a challenge. Here, we report on developments in the two week Summer Computing Academy (SCA) to focus on two such subfields. During the first week, 'Computing for a Better Tomorrow: Data Sciences,' introduced students to real-life applications of big data processing. A variety of topics were covered, including genomics and bioinformatics, cloud computing, and machine learning. During the second week, 'Camp Secure: Cybersecurity,' focused on issues related to principles of cybersecurity. Students were taught online safety, cryptography, and internet structure. The two weeks are unified by a common thread of Python programming. Modules from the SCA program may be implemented at other institutions with relative ease and promote cybertraining efforts nationwide.
Cybersecurity Training for Users of Remote Computing
Marcello Ponce and Ramses van Zonpp. 10–17
https://doi.org/10.22369/issn.2153-4136/14/2/3BibTeX
@article{jocse-14-2-3,
author={Marcello Ponce and Ramses van Zon},
title={Cybersecurity Training for Users of Remote Computing},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={10--17},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/3}
}
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End users of remote computing systems are frequently not aware of basic ways in which they could enhance protection against cyberthreats and attacks. In this paper, we discuss specific techniques to help and train users to improve cybersecurity when using such systems. To explain the rationale behind these techniques, we go into some depth explaining possible threats in the context of using remote, shared computing resources. Although some of the details of these prescriptions and recommendations apply to specific use cases when connecting to remote servers, such as a supercomputer, cluster, or Linux workstation, the main concepts and ideas can be applied to a wider spectrum of cases.
Assessing Shared Material Usage in the High Performance Computing (HPC) Education and Training Community
Susan Mehringer, Kate Cahill, John-Paul Navarro, Scott Lathrop, Charlie Dey, Mary Thomas, and Jeaime H. Powellpp. 18–22
https://doi.org/10.22369/issn.2153-4136/14/2/4BibTeX
@article{jocse-14-2-4,
author={Susan Mehringer and Kate Cahill and John-Paul Navarro and Scott Lathrop and Charlie Dey and Mary Thomas and Jeaime H. Powell},
title={Assessing Shared Material Usage in the High Performance Computing (HPC) Education and Training Community},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={18--22},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/4}
}
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This paper shares the results of a survey conducted October- November 2022. The survey's intent was to learn how the community both shares and discovers training and education materials, whether those needs were being met, and if there were interest in improving how materials are shared. The survey resulted in 112 responses primarily from content authors who are, or support, academics. While the majority of respondents considered themselves successful in finding materials, most also encountered barriers, such as finding materials, but not at the needed depth or level. Most respondents were both interested in, and able to, work toward community efforts to improve finding materials, with most citing lack of staff time as a barrier to doing so. Proposed efforts in community engagement to work toward these efforts are discussed.
Access to Computing Education Using Micro-credentials for Cyberinfrastructure
Dhruva K. Chakravorty, Richard Lawrence, Zhenhua He, Wesley Brashear, Honggao Liu, Andrew J. Palughi, Lisa M. Perez, Xin Yang, Jacob Pavelka, Ritika Mendjoge, Marinus Pennings, Randy McDonald, Gerry Pedraza, and Sunay V. Palsolepp. 23–27
https://doi.org/10.22369/issn.2153-4136/14/2/5BibTeX
@article{jocse-14-2-5,
author={Dhruva K. Chakravorty and Richard Lawrence and Zhenhua He and Wesley Brashear and Honggao Liu and Andrew J. Palughi and Lisa M. Perez and Xin Yang and Jacob Pavelka and Ritika Mendjoge and Marinus Pennings and Randy McDonald and Gerry Pedraza and Sunay V. Palsole},
title={Access to Computing Education Using Micro-credentials for Cyberinfrastructure},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={23--27},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/5}
}
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In response to an increasing demand for digital skills in industry and academia, a series of credentialed short courses that cover a variety of topics related to high performance computing were designed and implemented to enable university students and researchers to effectively utilize research computing resources and bridge the gap for users with educational backgrounds that do not include computational training. The courses cover a diverse array of topics, including subjects in programming, cybersecurity, artificial intelligence/ machine learning, bioinformatics, and cloud computing. The courses are designed to enable the students to apply the skills they learn to their own research that incorporates use of large-scale computing systems. These courses offer advantages to generic online courses in that they teach computing skills relevant to academic research programs. Finally, the micro-credentials obtained from these courses are transcriptable, may be stacked with existing degree programs and credit-bearing courses to create a larger degree plan, and offer a meaningful mechanism of adding to a student's resume.
Multifaceted Approaches for Introducing a Hardware-Thread Migratory Architecture
Aaron Jezghani, Jeffrey Young, Vedavyas Mallela, and Will Powellpp. 28–33
https://doi.org/10.22369/issn.2153-4136/14/2/6BibTeX
@article{jocse-14-2-6,
author={Aaron Jezghani and Jeffrey Young and Vedavyas Mallela and Will Powell},
title={Multifaceted Approaches for Introducing a Hardware-Thread Migratory Architecture},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={28--33},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/6}
}
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The challenges of HPC education span a wide array of targeted applications, ranging from developing a new generation of administrators and facilitators to maintain and support cluster resources and their respective user communities, to broadening the impact of HPC workflows by reaching non-traditional disciplines and training researchers in the best-practice tools and approaches when using such systems. Furthermore, standard x86 and GPU architectures are becoming untenable to scale to the needs of computational research, necessitating software and hardware co-development on less-familiar processors. While platforms such as Cerebras and SambaNova have matured to include common frameworks such as TensorFlow and PyTorch as well as robust APIs, and thus are amenable to production use cases and instructional material, other systems may lack such infrastructure maturity, impeding all but the most technically inclined developers from being able to leverage the system. We present here our efforts and outcomes of providing a codevelopment and instructional platform for the Lucata Pathfinder thread-migratory system in the Rogues Gallery at Georgia Tech. Through a collection of user workflow management, co-development with the platform’s engineers, community tutorials, undergraduate coursework, and student hires, we have been able to explore multiple facets of HPC education in a unique way that can serve as a viable template for others seeking to develop similar efforts.
Orchestrating Cloud-supported Workspaces for a Computational Biochemistry Course at Large Scale
Gil Speyer, Neal Woodbury, Arun Neelicattu, Aaron Peterson, Greg Schwimer, and George Slessmanpp. 34–37
https://doi.org/10.22369/issn.2153-4136/14/2/7BibTeX
@article{jocse-14-2-7,
author={Gil Speyer and Neal Woodbury and Arun Neelicattu and Aaron Peterson and Greg Schwimer and George Slessman},
title={Orchestrating Cloud-supported Workspaces for a Computational Biochemistry Course at Large Scale},
journal={The Journal of Computational Science Education},
year=2023,
month=nov,
volume=14,
issue=2,
pages={34--37},
doi={https://doi.org/10.22369/issn.2153-4136/14/2/7}
}
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A joint proof-of-concept project between Arizona State University and CR8DL, Inc., deployed a Jupyter-notebook based interface to datacenter resources for a computationally intensive, semesterlength biochemistry course project. Facilitated for undergraduate biochemistry students with limited high-performance computing experience, the straightforward interface allowed for large scale computations. As the project progressed, various enhancements were identified and implemented.