Software Carpentry - Overview MIT

Software Carpentry Team

January 2014

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More About Software Carpentry

History

What We Teach

What We Actually Teach

How to THINK like a programmer

Who We Teach

Who We Are

Our Goals for You

We will take you on a tour of:

Some High-Level Advice

Be fluent in multiple languages

You speak multiple languages when interacting with a computer. Choosing to use a new tool, library, or language can be similar to learning a new language:

Make it work right first, make it fast later.

Increase debugging bandwidth

Don't Repeat Yourself (or Others)

Automate common actions by saving simple blocks of code into scripts

Refactor commonly used blocks of code into functions

Group commonly used functions into libraries

Reduce Complexity

Basic strategies

Back up your data!

Use version control for checkpointing and collaboration

Verify and Validate your Code

Principles of verification and validation

Document your computational work

Schedule

Closing Thoughts

You sometimes need geeks. You never need dorks.

References and Further Reading

Research Literature

Programming Languages for Scientific Computing

Matthew G. Knepley

Preprint: http://arxiv.org/pdf/1209.1711.pdf

Gives an overview of modern programming languages and techniques such as code generation, templates, and mixed-language designs. This is a preprint, so expect some rough spots.

Two Solitudes

Greg Wilson

Slides: http://www.slideshare.net/gvwilson/two-solitudes

Describes Greg's journey as a scientist and leader for the Software Carpentry project, provides some insight into the differences between industry and academics.

Best Practices for Scientific Computing

D. A. Aruliah, C. Titus Brown, Neil P. Chue Hong, Matt Davis, Richard T. Guy, Steven H. D. Haddock, Katy Huff, Ian Mitchell, Mark Plumbley, Ben Waugh, Ethan P. White, Greg Wilson, Paul Wilson

Preprint: http://arxiv.org/abs/1210.0530

Good summary paper of many fundamental practices for working with and developing scientific software. This is a preprint, so expect some rough spots.

Web References

What Every Computer Scientist Should Know About Floating-Point Arithmetic

David Golberg

Web article: http://docs.oracle.com/cd/E19957-01/806-3568/ncg_goldberg.html

Introduction to the IEEE floating-point standard, its implications, and many of the common pitfalls when using floating-point numbers in scientific computing

Science Code Manifesto

http://sciencecodemanifesto.org

Publicly signed commitment to clear licensing and curation of software associated with research publications.