GGDML: icosahedral models language extensions

Jumah, N., Kunkel, J. M., Zängl, G., Yashiro, H., Dubos, T. and Meurdesoif, T. (2017) GGDML: icosahedral models language extensions. Journal of Computer Science Technology Updates, 4 (1). pp. 1-10. ISSN 2410-2938 doi: 10.15379/2410-2938.2017.04.01.01

Abstract/Summary

The optimization opportunities of a code base are not completely exploited by compilers. In fact, there are optimizations that must be done within the source code. Hence, if the code developers skip some details, some performance is lost. Thus, the use of a general-purpose language to develop a performance-demanding software -e.g. climate models- needs more care from the developers. They should take into account hardware details of the target machine. Besides, writing a high-performance code for one machine will have a lower performance on another one. The developers usually write multiple optimized sections or even code versions for the different target machines. Such codes are complex and hard to maintain. In this article we introduce a higher-level code development approach, where we develop a set of extensions to the language that is used to write a model’s code. Our extensions form a domain-specific language (DSL) that abstracts domain concepts and leaves the lower level details to a configurable source-to-source translation process. The purpose of the developed extensions is to support the icosahedral climate/atmospheric model development. We have started with the three icosahedral models: DYNAMICO, ICON, and NICAM. The collaboration with the scientists from the weather/climate sciences enabled agreed-upon extensions. When we have suggested an extension we kept in mind that it represents a higher-level domain-based concept, and that it carries no lower-level details. The introduced DSL (GGDML- General Grid Definition and Manipulation Language) hides optimization details like memory layout. It reduces code size of a model to less than one third its original size in terms of lines of code. The development costs of a model with GGDML are therefore reduced significantly.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/77672
Identification Number/DOI	10.15379/2410-2938.2017.04.01.01
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Computer Science
Publisher	Cosmos Scholars
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