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Reproducible Pedagogy for Cognitive Dissonance Reduction

EasyChair Preprint no. 1613

6 pagesDate: October 9, 2019


We describe a general work-flow which scales intuitively to high-performance computing (HPC) clusters for different domains of scientific computation. We demonstrate our methodology with a radial distribution function calculation in C++, with mental models for FORTRAN and Python as well. We present a pedagogical framework for the development of guided concrete incremental techniques to incorporate domain-specific knowledge and transfer existing expertise for developing high-performance, platform-independent, reproducible scientific software. This is effected by presenting the acceleration of a radial distribution function, a well-known algorithm in computational chemistry. Thus we assert that for domain specific algorithms, there is a language-independent pedagogical methodology which may be leveraged to ensure best practices for the scientific HPC community with minimal cognitive dissonance for practitioners and students.

Keyphrases: best practices, data structure, distributed computing, high performance, High Performance Computing, methodology, molecular dynamic trajectory result, pedagogy, Radial distribution function, reproducible research, tooling

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Rohit Goswami and Sonaly Goswami and Pranay Baldev and Shaivya Anand and Debabrata Goswami},
  title = {Reproducible Pedagogy for Cognitive Dissonance Reduction},
  howpublished = {EasyChair Preprint no. 1613},

  year = {EasyChair, 2019}}
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