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Introduction to Volume 8 Issue 1
Steven I. Gordonpp. 1–1
A brief introduction to this issue of the Journal of Computational Science Education from the editor.
Volume 8 Issue 1 — February 2017
Contents
Implementation of Computational Aids in Diels-Alder Reactions: Regioselectivity and Stereochemistry of Adduct Formation
Jiyoung Jung, Susan Zirpoli, and Glenn Slickpp. 2–6
https://doi.org/10.22369/issn.2153-4136/8/1/1BibTeX
@article{jocse-8-1-1,
author={Jiyoung Jung and Susan Zirpoli and Glenn Slick},
title={Implementation of Computational Aids in Diels-Alder Reactions: Regioselectivity and Stereochemistry of Adduct Formation},
journal={The Journal of Computational Science Education},
year=2017,
month=feb,
volume=8,
issue=1,
pages={2--6},
doi={https://doi.org/10.22369/issn.2153-4136/8/1/1}
}
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The Diels-Alder reaction is one of the most well-known organic reactions and is widely used for six-membered ring formation. Regio- and stereo-selective Diels-Alder reactions have been emphasized in various areas including pharmaceutical and polymer industries. However, covering the theoretical background of such reactions in an undergraduate class is challenging because the interactions between molecular orbitals is poorly visualized for students. Especially when dealing with polycyclic aromatic hydrocarbons (PAHs) and asymmetric compounds, the complexity of regio- and stereo-selectivity becomes more pronounced. Herein we utilized web-based computational tools (WebMO) to visualize the HOMO-LUMO of each reaction component and their interaction to form chemical bonds. In this study we demonstrated the incorporation of computational aids into a Diels-Alder laboratory class dramatically facilitates students' understanding of several important concepts including frontier orbital theory, thermodynamics of the reaction, three-dimensional visualization, and so on. The assessment of teaching effectiveness prior to and after implementation of computational aids into Diels-Alder reactions will also be discussed in this manuscript.
Educational Module on Genomic Sequence Alignment Using HPC
Angela B. Shiflet, George W. Shiflet, Daniel S. Couch, Pietro Hiram Guzzi, and Mario Cannataropp. 7–11
https://doi.org/10.22369/issn.2153-4136/8/1/2BibTeX
@article{jocse-8-1-2,
author={Angela B. Shiflet and George W. Shiflet and Daniel S. Couch and Pietro Hiram Guzzi and Mario Cannataro},
title={Educational Module on Genomic Sequence Alignment Using HPC},
journal={The Journal of Computational Science Education},
year=2017,
month=feb,
volume=8,
issue=1,
pages={7--11},
doi={https://doi.org/10.22369/issn.2153-4136/8/1/2}
}
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"Aligning SequencesSequentially and Concurrently," an educational computational science module by the authors and available online, develops a sequential algorithm to determine the highest similarity score and the alignments that yield this score for two DNA sequences. Moreover, the module considers several approaches to parallelization and speedup. Besides a serial implementation in C, a parallel program in C/MPI is available. This paper describes the module and details experiences using the material in a bioinformatics course at University "Magna Graecia" of Catanzaro, Italy. Besides being appropriate for such a course, the module can provide a meaningful application for a high performance computing or a data structures class.
VisMo: Augmented Reality Visualization of Scientific Data and Molecular Structures
Max Collins and Dr. Alan B. Craigpp. 12–15
https://doi.org/10.22369/issn.2153-4136/8/1/3BibTeX
@article{jocse-8-1-3,
author={Max Collins and Dr. Alan B. Craig},
title={VisMo: Augmented Reality Visualization of Scientific Data and Molecular Structures},
journal={The Journal of Computational Science Education},
year=2017,
month=feb,
volume=8,
issue=1,
pages={12--15},
doi={https://doi.org/10.22369/issn.2153-4136/8/1/3}
}
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In this paper, we describe and detail the creation of and use for our project that allows for augmented reality visualization of data produced using Blue Waters supercomputer or other high performance computers. While molecular structures have been displayed using augmented reality before [1,6], we created a pipeline for using information from the Protein Data Bank and automatically loading it into an augmented reality scene for further display and interaction. We find it important to create an easy way for students, scientists, and anyone else to be able to visualize molecular structures using Augmented Reality because it offers an interactive three dimensional perspective that is typically not available in the classroom. Learning about molecular structures in 2D is much less comprehensive, and our technique for visualization will be free for the end user and offer a great deal of aid to the learning and teaching process. There is no separate purchase required as long as a user has a smart phone or tablet. This is a helpful addition to scientific papers which, if containing the right target image, can be used as the visualization "anchor". The Protein Data Bank (PDB) houses information about proteins, nucleic acids, and more to help scientists and students understand concepts and ideas in biology and chemistry [5]. Our project goal is to open the PDB up to students and people who are not familiar with augmented reality visualization and allow people to learn using the PDB by visualizing molecular structures in different representations, annotating and interacting with the structures, and offering learning modules for common molecular structures. We created a prototype mobile application allowing for molecular visualization of PDB structures, and are continuing to tweak our project for an eventual release to the public.
GPU-ACCELERATED VLSI ROUTING USING GROUP STEINER TREES
Venkata Suhas Maringanti, Basileal Imana, and Peter Yoonpp. 16–19
https://doi.org/10.22369/issn.2153-4136/8/1/4BibTeX
@article{jocse-8-1-4,
author={Venkata Suhas Maringanti and Basileal Imana and Peter Yoon},
title={GPU-ACCELERATED VLSI ROUTING USING GROUP STEINER TREES},
journal={The Journal of Computational Science Education},
year=2017,
month=feb,
volume=8,
issue=1,
pages={16--19},
doi={https://doi.org/10.22369/issn.2153-4136/8/1/4}
}
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The problem of interconnecting nets with multi-port terminals in VLSI circuits is a direct generalization of the Group Steiner Problem (GSP). The GSP is a combinatorial optimization problem which arises in the routing phase of VLSI circuit design. This problem has been intractable, making it impractical to be used in real-world VLSI applications. This paper presents our work on designing and implementing a parallel approximation algorithm for the GSP based off an existing heuristic on a distributed architecture. Our implementation uses the CUDA-aware MPI approach to compute the approximate minimum-cost Group Steiner tree for several industry-standard VLSI graphs. Our implementation achieves up to 103x speedup compared to the best known serial work for the same graph. We present the speedup results for graphs up to 3k vertices. We also investigate some performance bottleneck issues by analyzing and interpreting the program performance data.
STUDENT PAPER: GPU Acceleration for SQL Queries on Large-Scale Distributed Systems
Linh Nguyen and Paul Hemlerpp. 20–26
https://doi.org/10.22369/issn.2153-4136/8/1/5BibTeX
@article{jocse-8-1-5,
author={Linh Nguyen and Paul Hemler},
title={STUDENT PAPER: GPU Acceleration for SQL Queries on Large-Scale Distributed Systems},
journal={The Journal of Computational Science Education},
year=2017,
month=feb,
volume=8,
issue=1,
pages={20--26},
doi={https://doi.org/10.22369/issn.2153-4136/8/1/5}
}
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General purpose GPUs are a powerful hardware with a number of applications in the realm of relational databases. We further extended a database framework designed to allow for GPU execution queries. Our technique is novel in that it implements Dynamic Parallelism, a new feature in recent hardware, to accelerate SQL JOINs. Query execution results in 1.25X speedup on average with respect to a previous method, also accelerated by GPUs, which employs a multi-dimensional CUDA Grid. More importantly, we divided the queries to run on multiple BW nodes to investigate the scalability of both SELECT and JOIN.