Reproducible Pedagogy for Cognitive Dissonance Reduction

EasyChair Preprint no. 1613

Abstract

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

@Booklet{EasyChair:1613,
  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}}