Better Scientific Software

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 tutorial will provide 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. Topics include software design, tools, and processes for small teams (including agile workflow management), reproducibility, and scientific software testing (including automated testing and continuous integration), software packaging, and refactoring.

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Contributors:

• Anshu Dubey (Argonne National Laboratory)
• Rinku Gupta (Argonne National Laboratory)
• Greg Watson (Oak Ridge National Laboratory)
• David Rogers (Oak Ridge National Laboratory)