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Better Scientific Software tutorial @ Improving Scientific Software (2022)

Version 2 2022-04-07, 13:56
Version 1 2022-04-03, 19:50
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posted on 2022-04-07, 13:56 authored by David E. BernholdtDavid E. Bernholdt, Rinku Gupta, Patricia GrubelPatricia Grubel, David Rogers

Producing scientific software is a challenge. The high-performance modeling and simulation community, in particular, is dealing with the confluence of disruptive changes in computing architectures and new opportunities (and demands) for greatly improved simulation capabilities, especially through coupling physics and scales. At the same time, computational science and engineering (CSE), as well as other areas of science, are experiencing increasing focus on scientific reproducibility and software quality.

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 tutorial distills experience from members of the IDEAS Productivity project and the creators of the BSSw.io community website drawn from many scientific software projects over many years. The tutorial will provide information about software practices, processes, and tools explicitly tailored for CSE. Topics to be covered include: Agile methodologies and tools, software design and refactoring, testing and continuous integration, Git workflows for teams, and reproducibility. Material will be mostly at the beginner and intermediate levels. There will also be opportunities to discuss topics raised by the audience.

Funding

This work was supported by the 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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