FAMOUS, faster: using parallel computing techniques to   accelerate the FAMOUS/HadCM3 climate model with a focus on the   radiative transfer algorithm

Hanappe, P.; Beurivé, Anthony; Laguzet, Florence; Steels, Luc; Boucher, Oliviér; Yamazaki, Y. H.; Aina, T.; Allen, Myles

doi:10.5194/gmd-4-835-2011

Cited by 15 publications

(6 citation statements)

References 15 publications

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“…As already noted, many components within weather and climate models have very flat computational profiles, but one approach to dealing with this is to identify "mini-apps" (analogous to the so-called Berkely dwarfs; Asanovic et al, 2006), which are exemplars of patterns in the code (e.g. common communication behaviours), so that performance issues can be investigated (or predicted) by using a small code base, typically a fraction of the size of the larger code (Heroux et al, 2009). When combined with a DSL approach, lessons learned from investigating mini-apps could be exploited more widely within the codes with minimal human intervention.…”

Section: Weather and Climate Dwarfsmentioning

confidence: 99%

Crossing the chasm: how to develop weather and climate models for next generation computers?

et al. 2018

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Abstract. Weather and climate models are complex pieces of software which include many individual components, each of which is evolving under pressure to exploit advances in computing to enhance some combination of a range of possible improvements (higher spatio-temporal resolution, increased fidelity in terms of resolved processes, more quantification of uncertainty, etc.). However, after many years of a relatively stable computing environment with little choice in processing architecture or programming paradigm (basically X86 processors using MPI for parallelism), the existing menu of processor choices includes significant diversity, and more is on the horizon. This computational diversity, coupled with ever increasing software complexity, leads to the very real possibility that weather and climate modelling will arrive at a chasm which will separate scientific aspiration from our ability to develop and/or rapidly adapt codes to the available hardware. In this paper we review the hardware and software trends which are leading us towards this chasm, before describing current progress in addressing some of the tools which we may be able to use to bridge the chasm. This brief introduction to current tools and plans is followed by a discussion outlining the scientific requirements for quality model codes which have satisfactory performance and portability, while simultaneously supporting productive scientific evolution. We assert that the existing method of incremental model improvements employing small steps which adjust to the changing hardware environment is likely to be inadequate for crossing the chasm between aspiration and hardware at a satisfactory pace, in part because institutions cannot have all the relevant expertise in house. Instead, we outline a methodology based on large community efforts in engineering and standardisation, which will depend on identifying a taxonomy of key activities – perhaps based on existing efforts to develop domain-specific languages, identify common patterns in weather and climate codes, and develop community approaches to commonly needed tools and libraries – and then collaboratively building up those key components. Such a collaborative approach will depend on institutions, projects, and individuals adopting new interdependencies and ways of working.

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Section: Weather and Climate Dwarfsmentioning

confidence: 99%

Crossing the chasm: how to develop weather and climate models for next generation computers?

et al. 2018

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“…They describe technical developments relating to model improvements such as the speed or accuracy of numerical integration schemes (e.g. Hanappe et al, 2011) or new parameterisations, numerical solutions for processes represented in models, and in-depth evaluations of already published models. Also included are papers relating to technical aspects of running models and the reproducibility of results (e.g.…”

Section: Technical Development and Evaluation (Tde)mentioning

confidence: 99%

Editorial: The publication of geoscientific model developments v1.0

2013

Geosci. Model Dev.

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In 2008, the first volume of the European Geosciences Union (EGU) journal Geoscientific Model Development (GMD) was published. GMD was founded because we perceived there to be a need for a space to publish comprehensive descriptions of numerical models in the geosciences. The journal is now well established, with the submission rate increasing over time. However, there are several aspects of model publication that we believe could be further improved. In this editorial we assess the lessons learned over the first few years of the journal's life, and describe some changes to GMD's editorial policy, which will ensure that the models and model developments are published in such a way that they are of maximum value to the community.

These changes to editorial policy mostly focus on improving the rigour of the review process through a stricter requirement for access to the materials necessary to test the behaviour of the models.

Throughout this editorial, "must" means that the stated actions are required, and the paper cannot be published without them; "strongly encouraged" means that we encourage the action, but papers can still be published if the criteria are not met; "may" means that the action may be carried out by the authors or referees, if they so wish.

We have reviewed and rationalised the manuscript types into five new categories. For all papers which are primarily based on a specific numerical model, the changes are as follows:

– The paper must be accompanied by the code, or means of accessing the code, for the purpose of peer-review. If the code is normally distributed in a way which could compromise the anonymity of the referees, then the code must be made available to the editor. The referee/editor is not required to review the code in any way, but they may do so if they so wish.

– All papers must include a section at the end of the paper entitled "Code availability". In this section, instructions for obtaining the code (e.g. from a supplement, or from a website) should be included; alternatively, contact information should be given where the code can be obtained on request, or the reasons why the code is not available should be clearly stated.

– We strongly encourage authors to upload any user manuals associated with the code.

– For models where this is practicable, we strongly encourage referees to compile the code, and run test cases supplied by the authors where appropriate.

– For models which have been previously described in the "grey" literature (e.g. as internal institutional documents), we strongly encourage authors to include this grey literature as a supplement, when this is allowed by the original authors.

– All papers must include a model name and version number (or other unique identifier) in the title.

It is our perception that, since Geoscientific Model Development (GMD) was founded, it has become increasingly common to see model descriptions p...

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“…High-performance computing on GPU or other special, highly parallel hardware is becoming more and more attractive in climate and geophysical research as well (e.g. Hanappe et al, 2011;Horn, 2012). To our knowledge there is no publication about using GPUs for marine ecosystem simulations.…”

Section: E Siewertsen Et Al: Porting Marine Ecosystem Model Spin-upmentioning

confidence: 99%

Porting marine ecosystem model spin-up using transport matrices to GPUs

2013

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Abstract.We have ported an implementation of the spin-up for marine ecosystem models based on transport matrices to graphics processing units (GPUs). The original implementation was designed for distributed-memory architectures and uses the Portable, Extensible Toolkit for Scientific Computation (PETSc) library that is based on the Message Passing Interface (MPI) standard. The spin-up computes a steady seasonal cycle of ecosystem tracers with climatological ocean circulation data as forcing. Since the transport is linear with respect to the tracers, the resulting operator is represented by matrices. Each iteration of the spin-up involves two matrixvector multiplications and the evaluation of the used biogeochemical model. The original code was written in C and Fortran. On the GPU, we use the Compute Unified Device Architecture (CUDA) standard, a customized version of PETSc and a commercial CUDA Fortran compiler. We describe the extensions to PETSc and the modifications of the original C and Fortran codes that had to be done. Here we make use of freely available libraries for the GPU. We analyze the computational effort of the main parts of the spin-up for two exemplar ecosystem models and compare the overall computational time to those necessary on different CPUs. The results show that a consumer GPU can compete with a significant number of cluster CPUs without further code optimization.

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FAMOUS, faster: using parallel computing techniques to accelerate the FAMOUS/HadCM3 climate model with a focus on the radiative transfer algorithm

Cited by 15 publications

References 15 publications

Crossing the chasm: how to develop weather and climate models for next generation computers?

Crossing the chasm: how to develop weather and climate models for next generation computers?

Editorial: The publication of geoscientific model developments v1.0

Porting marine ecosystem model spin-up using transport matrices to GPUs

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