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Extensible domain-specific programming for the sciences
The notion of scientists as programmers begs the question of what sort of programming language would be a good fit. The common answer seems to be both none of them and all of them. Many scientific applications are a combination of general-purpose and domain-specific languages: R for statistical elements, MATLAB for matrix-based computations, Perl-based regular expressions for string matching, C or FORTRAN for high performance parallel computations, and scripting languages such as Python to glue them all together. This clumsy situation demonstrates the need for different domain-specific language features.
Our hypothesis is that programming could be made easier, less error-prone and result in higher-quality code if languages could be easily extended, by the programmer, with the domain-specific features that a programmer or scientists needs for their particular task at hand. This talk demonstrates the meta-language processing tools that support this composition of programmer-selected language features, with several extensions chosen from the previously mentioned list of features.