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Introduction to Volume 14 Issue 1
David Joinerpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 14 Issue 1 — July 2023
Contents
Python-Based Tools for Modeling Transport in Porous Media Columns
Boyang Lu and David Lampertpp. 8–16
https://doi.org/10.22369/issn.2153-4136/14/1/2BibTeX
@article{jocse-14-1-2,
author={Boyang Lu and David Lampert},
title={Python-Based Tools for Modeling Transport in Porous Media Columns},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={8--16},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/2}
}
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The fate and transport of dissolved constituents in porous media has important applications in the earth and environmental sciences and many engineering disciplines. Mathematical models are commonly applied to simulate the movement of substances in porous media using the advection-dispersion equation. Whereas computer programs based on numerical solutions are commonly employed to solve the governing equations for these problems, analytical solutions also exist for some important one-dimensional cases. These solutions are often still quite complex to apply in practice, and therefore computational tools are still needed to apply them to determine the concentrations of dissolved substances as a function of space and time. The Python Programming Language provides a variety of tools that enable implementation of analytical solutions into useful tools and facilitate their application to experimental data. Python provides an important but underutilized tool in environmental modeling courses. This article highlights the development of a series of Python-based computing tools that can be used to numerically compute the values of an analytical solution to the onedimensional advection-dispersion equation. These tools are targeted to graduate and advanced undergraduate courses that teach environmental modeling and the application of Python for computing.
Approaching Exascale: Best Practices for Training a Diverse Workforce using Hackathons
Izumi Barker, Mozhgan Kabiri Chimeh, Kevin Gott, Thomas Papatheodore, and Mary P. Thomaspp. 17–22
https://doi.org/10.22369/issn.2153-4136/14/1/3BibTeX
@article{jocse-14-1-3,
author={Izumi Barker and Mozhgan Kabiri Chimeh and Kevin Gott and Thomas Papatheodore and Mary P. Thomas},
title={Approaching Exascale: Best Practices for Training a Diverse Workforce using Hackathons},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={17--22},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/3}
}
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Given the anticipated growth of the high-performance computing market, HPC is challenged with expanding the size, diversity, and skill of its workforce while also addressing post-pandemic distributed workforce protocols and an ever-expanding ecosystem of architectures, accelerators, and software stacks. As we move toward exascale computing, training approaches need to address how to best prepare future computational scientists and enable established domain researchers to stay current and master tools needed for exascale architectures. This paper explores adding hybrid and virtual Hackathons to the training mix to bridge traditional programming curricula and hands-on skills needed among diverse communities. We outline current learning and development programs available; explain the benefits and challenges in implementing hackathons for training using experience gained from the Open Hackathons program (formerly the GPU Hackathons program); discuss how to engage diverse communities—from early career researchers to veteran scientists; and recommend best practices for implementing these events.
Teaching Accelerated Computing and Deep Learning at a Large-Scale with the NVIDIA Deep Learning Institute
Bálint Gyires-Tóth, Işıl Öz, and Joe Bungopp. 23–30
https://doi.org/10.22369/issn.2153-4136/14/1/4BibTeX
@article{jocse-14-1-4,
author={B\'{a}lint Gyires-T\'{o}th and I\c{s}\imathl \"{O}z and Joe Bungo},
title={Teaching Accelerated Computing and Deep Learning at a Large-Scale with the NVIDIA Deep Learning Institute},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={23--30},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/4}
}
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Researchers and developers in a variety of fields have benefited from the massively parallel processing paradigm. Numerous tasks are facilitated by the use of accelerated computing, such as graphics, simulations, visualisations, cryptography, data science, and machine learning. Over the past years, machine learning and in particular deep learning have received much attention. The development of such solutions requires a different level of expertise and insight than that required for traditional software engineering. Therefore, there is a need for novel approaches to teaching people about these topics. This paper outlines the primary challenges of accelerated computing and deep learning education, discusses the methodology and content of the NVIDIA Deep Learning Institute, presents the results of a quantitative survey conducted after full-day workshops, and demonstrates a sample adoption of DLI teaching kits for teaching heterogeneous parallel computing.
Preliminary Results of Applying Modified MSA Algorithm on Quantum Annealers (MAQ)
Melody Leepp. 31–40
https://doi.org/10.22369/issn.2153-4136/14/1/5BibTeX
@article{jocse-14-1-5,
author={Melody Lee},
title={Preliminary Results of Applying Modified MSA Algorithm on Quantum Annealers (MAQ)},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={31--40},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/5}
}
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We propose a modified MSA algorithm on quantum annealers with applications in areas of bioinformatics and genetic sequencing. To understand the human genome, researchers compare extensive sets of these genetic sequences – or their protein counterparts – to identify patterns. This comparison begins with the alignment of the set of (multiple) sequences. However, this alignment problem is considered nondeterministically-polynomial time complete and, thus, current classical algorithms at best rely on brute force or heuristic methods to find solutions. Quantum annealing algorithms are able to bypass this need for sheer brute force due to their use of quantum mechanical properties. However, due to the novelty of these algorithms, many are rudimentary in nature and limited by hardware restrictions. We apply progressive alignment techniques to modify annealing algorithms, achieving a linear reduction in spin usage whilst introducing more complex heuristics to the algorithm. This opens the door for further exploration into quantum computing-based bioinformatics, potentially allowing for a deeper understanding of disease detection and monitoring.
An Educational and Training Perspective on Integrating Hybrid Technologies with HPC Systems for Solving Real-World Commercial Problems
Stefano Mensa, Emre Sahin, George Williamson, and Robert J. Allanpp. 41–45
https://doi.org/10.22369/issn.2153-4136/14/1/6BibTeX
@article{jocse-14-1-6,
author={Stefano Mensa and Emre Sahin and George Williamson and Robert J. Allan},
title={An Educational and Training Perspective on Integrating Hybrid Technologies with HPC Systems for Solving Real-World Commercial Problems},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={41--45},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/6}
}
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Delivering training and education on hybrid technologies (including AI, ML, GPU, Data and Visual Analytics including VR and Quantum Computing) integrated with HPC resources is key to enable individuals and businesses to take full advantage of digital technologies, hence enhancing processes within organisations and providing the enabling skills to thrive in a digital economy. Supercomputing centres focused on solving industry-led problems face the challenge of having a pool of users with little experience in executing simulations on large-scale facilities, as well as limited knowledge of advanced computational techniques and integrated technologies. We aim not only at educating them in using the facilities available, but to raise awareness of methods which have the potential to increase their productivity. In this paper, we provide our perspective on how to efficiently train industry users, and how to engage with them about wider digital technologies and how these, used efficiently together, can benefit their business.
Sustainable and Scalable Setup for Teaching Big Data Computing
Linh B Ngo and Hoang Buipp. 46–52
https://doi.org/10.22369/issn.2153-4136/14/1/7BibTeX
@article{jocse-14-1-7,
author={Linh B Ngo and Hoang Bui},
title={Sustainable and Scalable Setup for Teaching Big Data Computing},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={46--52},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/7}
}
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As more students want to pursue a career in big data analytics and data science, big data education has become a focal point in many colleges and universities' curricula. There are many challenges when it comes to teaching and learning big data in a classroom setting. One of the biggest challenges is to prepare big data infrastructure to provide meaningful hands-on experience to students. Setting up necessary distributed computing resource is a delicate act for instructors and system administrators because there is no one size fit all solutions. In this paper, we propose an approach that facilitates the creation of the computing environment on both personal computers and public cloud resources. This combined approach meet different needs and can be used in an educational setting to facilitate different big data learning activities. We discuss and reflect on our experience using these systems in teaching undergraduate and graduate courses.
Exascale Computing Project's Broadening Participation Initiative
Suzanne Parete-Koon, Mary Ann Leung, Sreeranjani Ramprakash, and Lois Curfman McInnespp. 53–54
https://doi.org/10.22369/issn.2153-4136/14/1/8BibTeX
@article{jocse-14-1-8,
author={Suzanne Parete-Koon and Mary Ann Leung and Sreeranjani Ramprakash and Lois Curfman McInnes},
title={Exascale Computing Project's Broadening Participation Initiative},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={53--54},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/8}
}
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This article gives an overview of ECP's Broadening Participation Initiative (https://www.exascaleproject.org/hpc-workforce/), which has the mission of establishing a sustainable plan to recruit and retain a diverse workforce in the DOE high-performance computing community by fostering a supportive and inclusive culture within the computing sciences at DOE national laboratories. We will describe key activities within three complementary thrusts: establishing an HPC Workforce Development and Retention Action Group, creating accessible 'Intro to HPC' training materials, and launching the Sustainable Research Pathways for High-Performance Computing (SRP-HPC) workforce development program. We are leveraging ECP's unique multilab partnership to work toward sustainable collaboration across the DOE community, with the long-term goal of changing the culture and demographic profile of DOE computing sciences.
Computational Analysis of SARS-CoV-2 Therapeutics Development
Samuel Biggerstaff, Jennifer L. Muzyka, and David Tothpp. 55–59
https://doi.org/10.22369/issn.2153-4136/14/1/9BibTeX
@article{jocse-14-1-9,
author={Samuel Biggerstaff and Jennifer L. Muzyka and David Toth},
title={Computational Analysis of SARS-CoV-2 Therapeutics Development},
journal={The Journal of Computational Science Education},
year=2023,
month=jul,
volume=14,
issue=1,
pages={55--59},
doi={https://doi.org/10.22369/issn.2153-4136/14/1/9}
}
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SARS-CoV-2 (also known as COVID-19) is a coronavirus that has recently emerged and impacted nearly every human on the planet. The nonstructural protein 12 (NSP 12) is an RNA-dependent RNA polymerase that replicates viral RNA in a cell to infect it. Interrupting this function should prohibit the virus from replicating within the body and would decrease the severity of the virus's effects in patients. The objective of this project is to identify potential inhibitors for NSP 12 that might be suitable as antiviral drugs. Thus, we obtained the structure of NSP 12 from RCSB's protein data bank. The protein structure was analyzed using computer software (Chimera and PyRx), and ligands obtained from the ZINC database and RCSB's protein data bank were docked to NSP 12. The resulting binding affinities were recorded, and binding geometries analyzed.