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2019 ATPESC Tutorial: Better Scientific Software

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posted on 2019-10-21, 14:50 authored by Jared O'NealJared O'Neal, Katherine Riley, Anshu DubeyAnshu Dubey, James Willenbring

The computational science and engineering (CSE) community is in the midst of an extremely challenging period created by the confluence of disruptive changes in computing architectures, demand for greater scientific reproducibility, and new opportunities for greatly improved simulation capabilities, especially through coupling physics and scales. Computer architecture changes require new software design and implementation strategies, including significant refactoring of existing code. Reproducibility demands require more rigor across the entire software endeavor. Code coupling requires aggregate team interactions including integration of software processes and practices. These challenges demand large investments in scientific software development and improved practices. Focusing on improved developer productivity and software sustainability is both urgent and essential.


This full day tutorial distills multi-project and multi-years’ experience from members of the IDEAS project, and creators of the BSSw.io community website. It provides information and hands-on experience with software practices, processes, and tools explicitly tailored for CSE. Goals are improving the productivity of those who develop CSE software and increasing the sustainability of software artifacts. We discuss practices that are relevant for projects of all sizes, with emphasis on small teams, and on aggregate teams composed of small teams.


Outline

* Objectives, Motivation, & Overview [45 min]

* Requirements & Test-Driven Development [45 min]

* Software Design & Testing [60 min]

* Licensing [45 min]

* Agile Methodologies & Useful Git Tools [60 min]

* Git Workflows [30 min]

* Code Coverage & Continuous Integration [40 min]

* Software Refactoring & Documentation [35 min]

Funding

U.S. Department of Energy Office of Science, Office of Advanced Scientific Computing Research (ASCR), and by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

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