High-Performance Domain-Specific Library for Hydrologic Data Processing

Hydrologists must process many gigabytes of data for hydrologic simulations, which takes time and resources degrading performance. The performance issues are caused mainly by domain scientists’ preference for using Python, which trades performance for productivity. In my thesis, I demonstrate that using the static compilation technique to compile Python to generate C code along with several optimizations reduces time and resources for hydrologic data processing. I developed a Domain Specific Library (DSL) which is a subset of Python and compiles to Sparse Polyhedral Framework - Intermediate Representation (SPF-IR), which allows opportunities for optimizations like read reduction fusion which are not available in Python. We fused the file I/O to perform computation on small chunks of data (stream computation) in order to reduce the memory footprint. The C code we generated from SPF-IR shows an average speed-up of 2.58x over the existing hand-optimized implementations and can totally eliminate the temporary storage required. DSL users can still enjoy the ease of use of Python but get performance better than the C code

Parsing Fortran-77 with proprietary extensions

Far from the latest innovations in software development, many organizations still rely on old code written in "obsolete" programming languages. Because this source code is old and proven it often contributes significantly to the continuing success of these organizations. Yet to keep the applications relevant and running in an evolving environment, they sometimes need to be updated or migrated to new languages or new platforms. One difficulty of working with these "veteran languages" is being able to parse the source code to build a representation of it. Parsing can also allow modern software development tools and IDEs to offer better support to these veteran languages. We initiated a project between our group and the Framatome company to help migrate old Fortran-77 with proprietary extensions (called Esope) into more modern Fortran. In this paper, we explain how we parsed the Esope language with a combination of island grammar and regular parser to build an abstract syntax tree of the code.Comment: Accepted at ICSME'23 Industrial trac

Automatic Fortran to C++ conversion with FABLE

AbstractBackgroundIn scientific computing, Fortran was the dominant implementation language throughout most of the second part of the 20th century. The many tools accumulated during this time have been difficult to integrate with modern software, which is now dominated by object-oriented languages.ResultsDriven by the requirements of a large-scale scientific software project, we have developed a Fortran to C++ source-to-source conversion tool named FABLE. This enables the continued development of new methods even while switching languages. We report the application of FABLE in three major projects and present detailed comparisons of Fortran and C++ runtime performances.ConclusionsOur experience suggests that most Fortran 77 codes can be converted with an effort that is minor (measured in days) compared to the original development time (often measured in years). With FABLE it is possible to reuse and evolve legacy work in modern object-oriented environments, in a portable and maintainable way. FABLE is available under a nonrestrictive open source license. In FABLE the analysis of the Fortran sources is separated from the generation of the C++ sources. Therefore parts of FABLE could be reused for other target languages