ARTÍCULO
TITULO

Towards a performance portable, architecture agnostic implementation strategy for weather and climate models

Oliver Fuhrer    
Carlos Osuna    
Xavier Lapillonne    
Tobias Gysi    
Ben Cumming    
Mauro Bianco    
Andrea Arteaga    
Thomas Christoph Schulthess    

Resumen

We propose a software implementation strategy for complex weather and climate models that produces performance portable, architecture agnostic codes. It relies on domain and data structure specific tools that are usable within common model development frameworks -- Fortran today and possibly high-level programming environments like Python in the future. We present the strategy in terms of a refactoring project of the atmospheric model COSMO, where we have rewritten the dynamical core and refactored the remaining Fortran code. The dynamical core is built on top of the domain specific ``Stencil Loop Language'' for stencil computations on structured grids, a generic framework for halo exchange and boundary conditions, as well as a generic communication library that handles data exchange on a distributed memory system. All these tools are implemented in C++ making extensive use of generic programming and template metaprogramming. The refactored code is shown to outperform the current production code and is performance portable to various hybrid CPU-GPU node architectures.

 Artículos similares

       
 
Ryota Higashimoto, Soh Yoshida and Mitsuji Muneyasu    
This paper addresses the performance degradation of deep neural networks caused by learning with noisy labels. Recent research on this topic has exploited the memorization effect: networks fit data with clean labels during the early stages of learning an... ver más
Revista: Applied Sciences

 
Sara Rajaram and Cassie S. Mitchell    
The ability to translate Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) into different modalities and data types is essential to improve Deep Learning (DL) for predictive medicine. This work presents DACMVA, a novel framework ... ver más
Revista: Information

 
Fabi Prezja, Leevi Annala, Sampsa Kiiskinen and Timo Ojala    
Diagnosing knee joint osteoarthritis (KOA), a major cause of disability worldwide, is challenging due to subtle radiographic indicators and the varied progression of the disease. Using deep learning for KOA diagnosis requires broad, comprehensive dataset... ver más
Revista: Algorithms

 
Xin Tian and Yuan Meng    
The judicious configuration of predicates is a crucial but often overlooked aspect in the field of knowledge graphs. While previous research has primarily focused on the precision of triples in assessing knowledge graph quality, the rationality of predic... ver más
Revista: Algorithms

 
Hang Yu, Yixi Zhao, Chongben Ni, Jinhong Ding, Tao Zhang, Ran Zhang and Xintian Jiang    
The diverse nature of hull components in shipbuilding has created a demand for intelligent robots capable of performing various tasks without pre-teaching or template-based programming. Visual perception of a target?s outline is crucial for path planning... ver más