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The introspective may achieve more: Enhancing existing Geoscientific models with native-language emulated structural reflection

Journal Article · · Computers and Geosciences
 [1];  [2]
  1. The Pennsylvania State Univ., University Park, PA (United States); Office of Science BER
  2. The Pennsylvania State Univ., University Park, PA (United States)
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Geoscientific models manage myriad and increasingly complex data structures as trans-disciplinary models are integrated. They often incur significant redundancy with cross-cutting tasks. Reflection, the ability of a program to inspect and modify its structure and behavior at runtime, is known as a powerful tool to improve code reusability, abstraction, and separation of concerns. Reflection is rarely adopted in high-performance Geoscientific models, especially with Fortran, where it was previously deemed implausible. Practical constraints of language and legacy often limit us to feather-weight, native-language solutions. We demonstrate the usefulness of a structural-reflection-emulating, dynamically-linked metaObjects, gd. We show real-world examples including data structure self-assembly, effortless save/restart and upgrade to parallel I/O, recursive actions and batch operations. We share gd and a derived module that reproduces MATLAB-like structure in Fortran and C++. We suggest that both a gd representation and a Fortran-native representation are maintained to access the data, each for separate purposes. In conclusion, embracing emulated reflection allows generically-written codes that are highly re-usable across projects.

Research Organization:
Pennsylvania State Univ., University Park, PA (United States)
Sponsoring Organization:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
Grant/Contract Number:
SC0010620
OSTI ID:
1397240
Alternate ID(s):
OSTI ID: 1549240
Journal Information:
Computers and Geosciences, Journal Name: Computers and Geosciences Vol. 110; ISSN 0098-3004
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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A Transdisciplinary Review of Deep Learning Research and Its Relevance for Water Resources Scientists journal November 2018
Seasonal and Interannual Patterns and Controls of Hydrological Fluxes in an Amazon Floodplain Lake With a Surface‐Subsurface Process Model journal April 2019

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Related Subjects

58 GEOSCIENCES
Data I/O
Efficient programming
FORTRAN
Generic data structure
Geoscientific modeling
Reflection