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Introduction to Volume 4 Issue 1
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 4 Issue 1 — October 2013
Contents
Computational Math, Science, and Technology (C-MST) Approach to General Ed
Osman Yasarpp. 2–10
https://doi.org/10.22369/issn.2153-4136/4/1/1BibTeX
@article{jocse-4-1-1,
author={Osman Yasar},
title={Computational Math, Science, and Technology (C-MST) Approach to General Ed},
journal={The Journal of Computational Science Education},
year=2013,
month=oct,
volume=4,
issue=1,
pages={2--10},
doi={https://doi.org/10.22369/issn.2153-4136/4/1/1}
}
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In this paper, we present a computational approach to teaching general education courses that expose students to science and computing principles in engaging contexts, including modeling and simulation, games, and history. The courses use scalable curriculum modules organized in layers of increasing difficulties in order to balance learning challenges and student abilities. We describe the computational pedagogy followed in these modules and courses, with particular attention to the simulation-based course, namely introduction to computational science, to present a case study for those considering similar initiatives.
Introducing Transition Matrices and Their Biological Applications
Angela B. Shiflet and George W. Shifletpp. 11–15
https://doi.org/10.22369/issn.2153-4136/4/1/2BibTeX
@article{jocse-4-1-2,
author={Angela B. Shiflet and George W. Shiflet},
title={Introducing Transition Matrices and Their Biological Applications},
journal={The Journal of Computational Science Education},
year=2013,
month=oct,
volume=4,
issue=1,
pages={11--15},
doi={https://doi.org/10.22369/issn.2153-4136/4/1/2}
}
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The Blue Waters Undergraduate Petascale Education Program (NSF) sponsors the development of educational modules that help students understand computational science and the importance of high performance computing. As part of this materials development initiative, we developed two modules, "Time after Time: Age- and Stage-Structured Models" and "Probable Cause: Modeling with Markov Chains," which develop application problems involving transition matrices and provide accompanying programs in a variety of systems (C/MPI, C, MATLAB, Mathematica). Age- and stage-structured models incorporate the probability of an animal passing from one age or stage to the next as well as the animal's average reproduction at each age or stage. Markov chain models are based on the probability of passing from one state to another. These educational materials follow naturally from another Blue Waters module, "Living Links: Applications of Matrix Operations to Population Studies," which provides a foundation for the use of matrix operations. This paper describes the two modules and details experiences using the resources in classes.
STEM-Based Computing Educational Resources on the Web
Tatiana Ringenberg and Aejandra Maganapp. 16–23
https://doi.org/10.22369/issn.2153-4136/4/1/3BibTeX
@article{jocse-4-1-3,
author={Tatiana Ringenberg and Aejandra Magana},
title={STEM-Based Computing Educational Resources on the Web},
journal={The Journal of Computational Science Education},
year=2013,
month=oct,
volume=4,
issue=1,
pages={16--23},
doi={https://doi.org/10.22369/issn.2153-4136/4/1/3}
}
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This paper explores the landscape of computing educational resources found on the web together with teaching and learning materials that can facilitate the integration of computational thinking into the classroom. In specific, this paper focuses in finding and describing existing learning environments that integrate computational thinking into a STEM discipline This study provides initial steps towards that goal of providing a comprehensive list of STEM-based computational resources on the web that also provides guiding information, which can help teachers and parents make decisions to evaluate and integrate these resources easily for educational purposes.
Transformation of a Mathematics Department's Teaching and Research Through a Focus on Computational Science
Yanlai Chen, Gary Davis, Sigal Gottlieb, Adam Hausknecht, Alfa Heryudono, and Saeja Kimpp. 24–29
https://doi.org/10.22369/issn.2153-4136/4/1/4BibTeX
@article{jocse-4-1-4,
author={Yanlai Chen and Gary Davis and Sigal Gottlieb and Adam Hausknecht and Alfa Heryudono and Saeja Kim},
title={Transformation of a Mathematics Department's Teaching and Research Through a Focus on Computational Science},
journal={The Journal of Computational Science Education},
year=2013,
month=oct,
volume=4,
issue=1,
pages={24--29},
doi={https://doi.org/10.22369/issn.2153-4136/4/1/4}
}
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Undergraduate teaching that focuses on student-driven research, mentored by research active faculty, can have a powerful effect in bringing relevance and cohesiveness to a department's programs. We describe and discuss such a program in computational mathematics, and the effects this program has had on the students, the faculty, the department and the university.
STUDENT PAPER: Solving the Many-Body Polarization Problem on GPUs: Application to MOFs
Brant Tudor and Brian Spacepp. 30–34
https://doi.org/10.22369/issn.2153-4136/4/1/5BibTeX
@article{jocse-4-1-5,
author={Brant Tudor and Brian Space},
title={STUDENT PAPER: Solving the Many-Body Polarization Problem on GPUs: Application to MOFs},
journal={The Journal of Computational Science Education},
year=2013,
month=oct,
volume=4,
issue=1,
pages={30--34},
doi={https://doi.org/10.22369/issn.2153-4136/4/1/5}
}
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Massively Parallel Monte Carlo, an in-house computer code available at http://code.google.com/p/mpmc/, has been successfully utilized to simulate interactions between gas phase sorbates and various metal-organic materials. In this regard, calculations involving polarizability were found to be critical, and computationally expensive. Although GPGPU routines have increased the speed of these calculations immensely, in its original state, the program was only able to leverage a GPUs power on small systems. In order to study larger and evermore complex systems, the program model was modified such that limitations related to system size were relaxed while performance was either increased or maintained. In this project, parallel programming techniques learned from the Blue Waters Undergraduate Petascale Education Program were employed to increase the efficiency and expand the utility of this code.
Parallelization of the Knapsack Problem as an Introductory Experience in Parallel Computing
Michael Crawford and David Tothpp. 35–39
https://doi.org/10.22369/issn.2153-4136/4/1/6BibTeX
@article{jocse-4-1-6,
author={Michael Crawford and David Toth},
title={Parallelization of the Knapsack Problem as an Introductory Experience in Parallel Computing},
journal={The Journal of Computational Science Education},
year=2013,
month=oct,
volume=4,
issue=1,
pages={35--39},
doi={https://doi.org/10.22369/issn.2153-4136/4/1/6}
}
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As part of a parallel computing course where undergraduate students learned parallel computing techniques and got to run their programs on a supercomputer, one student designed and implemented a sequential algorithm and two versions of a parallel algorithm to solve the knapsack problem. Performance tests of the programs were conducted on the Ranger supercomputer. The performance of the sequential and parallel implementations was compared to determine speedup and efficiency. We observed 82%-86% efficiency for the MPI version and 89% efficiency for the OpenMP version for sufficiently large inputs to the problem. Additionally, we discuss both the student and faculty member's reflections about the experience.