Benefits and requirements of grid computing for climate applications. An example with the community atmospheric model

Grid computing is nowadays an established technology in fields such as High Energy Physics and Biomedicine, offering an alternative to traditional HPC for several problems; however, it is still an emerging discipline for the climate community and only a few climate applications have been adapted to the Grid to solve particular problems. In this paper we present an up-to-date description of the advantages and limitations of the Grid for climate applications (in particular global circulation models), analyzing the requirements and the new challenges posed to the Grid. In particular, we focus on production-like problems such as sensitivity analysis or ensemble prediction, where a single model is run several times with different parameters, forcing and/or initial conditions. As an illustrative example, we consider the Community Atmospheric Model (CAM) and analyze the advantages and shortcomings of the Grid to perform a sensitivity study of precipitation with SST perturbations in El Nino area, reporting the results obtained with traditional (local cluster) and Grid infrastructures. We conclude that new specific middleware (execution workflow managers) is needed to meet the particular requirements of climate applications (long simulations, checkpointing, etc.). This requires the side-by-side collaboration of IT and climate groups to deploy fully ported applications, such as the CAM for Grid (CAM4G) introduced in this paper.

[1]  M. Webb,et al.  Quantification of modelling uncertainties in a large ensemble of climate change simulations , 2004, Nature.

[2]  Steven Tuecke,et al.  Internet X.509 Public Key Infrastructure (PKI) Proxy Certificate Profile , 2004, RFC.

[3]  C. Ropelewski,et al.  Global and Regional Scale Precipitation Patterns Associated with the El Niño/Southern Oscillation , 1987 .

[4]  R. Sutton,et al.  Atmospheric GCM Response to Extratropical SST Anomalies: Synthesis and Evaluation* , 2002 .

[5]  Tim N. Palmer,et al.  The economic value of ensemble forecasts as a tool for risk assessment: From days to decades , 2002 .

[6]  Johan Montagnat,et al.  Grid-enabled Virtual Screening Against Malaria , 2006, Journal of Grid Computing.

[7]  W. Gates.  AMIP: The Atmospheric Model Intercomparison Project. , 1992 .

[8]  Steve Dowers,et al.  From stand-alone programs towards grid-aware services and components: a case study in agricultural modelling with interpolated climate data , 2003, Environ. Model. Softw..

[9]  M. Mcphaden,et al.  Genesis and evolution of the 1997-98 El Nino , 1999, Science.

[10]  James J. Hack,et al.  The Dynamical Simulation of the Community Atmosphere Model Version 3 (CAM3) , 2006 .

[11]  Mark Rounsevell,et al.  Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change , 2007 .

[12]  Evangelos Floros,et al.  A Grid-Enabled Regional-Scale Ensemble Forecasting System in the Mediterranean Area , 2010, Journal of Grid Computing.

[13]  James J. Hack,et al.  Simulation of the Global Hydrological Cycle in the CCSM Community Atmosphere Model Version 3 (CAM3): Mean Features , 2006 .

[14]  Gilles Fedak,et al.  The Computational and Storage Potential of Volunteer Computing , 2006, Sixth IEEE International Symposium on Cluster Computing and the Grid (CCGRID'06).

[15]  P. Renard,et al.  Grid Computing for Earth Science , 2009 .

[16]  William E. Allcock,et al.  The Globus Striped GridFTP Framework and Server , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[17]  Johan Blouw,et al.  The LHCb Computing Data Challenge DC06 , 2008 .

[18]  Steven Tuecke,et al.  X.509 Proxy Certificates for Dynamic Delegation , 2004 .

[19]  H. L. Miller,et al.  Climate Change 2007: The Physical Science Basis , 2007 .

[20]  David E. Bernholdt,et al.  The earth system grid: enabling access to multimodel climate simulation data. , 2009 .

[21]  W. Collins,et al.  The Community Climate System Model: CCSM3 , 2004 .

[22]  Michael F. P. O'Boyle,et al.  Automatic Tuning of Inlining Heuristics , 2005, ACM/IEEE SC 2005 Conference (SC'05).

[23]  Ian T. Foster,et al.  The Anatomy of the Grid: Enabling Scalable Virtual Organizations , 2001, Int. J. High Perform. Comput. Appl..

[24]  Ian Foster,et al.  The Grid 2 - Blueprint for a New Computing Infrastructure, Second Edition , 1998, The Grid 2, 2nd Edition.

[25]  Ian T. Foster,et al.  The anatomy of the grid: enabling scalable virtual organizations , 2001, Proceedings First IEEE/ACM International Symposium on Cluster Computing and the Grid.

[26]  Ian T. Foster,et al.  A security architecture for computational grids , 1998, CCS '98.

[27]  Ken Takahashi,et al.  The atmospheric circulation associated with extreme rainfall events in Piura, Peru, during the 1997--1998 and 2002 El Niño events , 2004 .

[28]  Steven Tuecke,et al.  Enabling Scalable Virtual Organizations , 2001 .

[29]  C. Brooks,et al.  AMERICAN METEOROLOGICAL SOCIETY , 2014 .

[30]  J. Palutikof,et al.  Climate change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change. Summary for Policymakers. , 2007 .

[31]  Ian T. Foster,et al.  Globus Toolkit Version 4: Software for Service-Oriented Systems , 2005, Journal of Computer Science and Technology.

[32]  Ami Marowka,et al.  The GRID: Blueprint for a New Computing Infrastructure , 2000, Parallel Distributed Comput. Pract..

[33]  Steven Tuecke,et al.  The Anatomy of the Grid , 2003 .

[34]  K. Skala,et al.  Implementation of the WRF-ARW prognostic model on the Grid , 2010, The 33rd International Convention MIPRO.

[35]  Tao Zhang,et al.  Response of water vapor and clouds to El Niño warming in three National Center for Atmospheric Research atmospheric models , 2006 .

[36]  W. Collins,et al.  The Formulation and Atmospheric Simulation of the Community Atmosphere Model Version 3 (CAM3) , 2006 .

[37]  A. Dai.  Precipitation Characteristics in Eighteen Coupled Climate Models , 2006 .

[38]  Andrea Sulis,et al.  GRID computing approach for multireservoir operating rules with uncertainty , 2009, Environ. Model. Softw..

[39]  W. Collins,et al.  The Community Climate System Model Version 3 (CCSM3) , 2006 .

[40]  D. Randall,et al.  Climate models and their evaluation , 2007 .

[41]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[42]  Minghua Zhang,et al.  Toward understanding the double Intertropical Convergence Zone pathology in coupled ocean‐atmosphere general circulation models , 2007 .

[43]  M. Allen.  Do-it-yourself climate prediction , 1999, Nature.

[44]  Eduardo Huedo,et al.  The GridWay Framework for Adaptive Scheduling and Execution on Grids , 2005, Scalable Comput. Pract. Exp..

[45]  Ian T. Foster.  Globus Toolkit Version 4: Software for Service-Oriented Systems , 2005, NPC.

[46]  Luigi Fusco,et al.  Open Grid Services for Envisat and Earth Observation Applications , 2007 .

[47]  Daeho Jin,et al.  The extratropical sensitivity to the meridional extent of tropical ENSO forcing , 2010 .

[48]  Ken Klingenstein.  3rd Annual PKI R&D Workshop , 2004 .

[49]  Abhay K. Bhushan,et al.  The File Transfer Protocol , 1971, Request for Comments.

[50]  J. Palutikof,et al.  Climate change 2007 : impacts, adaptation and vulnerability , 2001 .

[51]  S. Nicholson,et al.  THE RELATIONSHIP OF THE EL NIÑO–SOUTHERN OSCILLATION TO AFRICAN RAINFALL , 1997 .

[52]  José Manuel Gutiérrez,et al.  Execution management in the GRID, for sensitivity studies of global climate simulations , 2009, Earth Sci. Informatics.

[53]  Peter R. Waylen,et al.  El Niño and annual floods on the north Peruvian littoral , 1986 .

[54]  Ladislav Hluchý,et al.  A roadmap for a dedicated Earth Science Grid platform , 2010, Earth Sci. Informatics.

[55]  Charles E. Catlett.  The Philosophy of TeraGrid: Building an Open, Extensible, Distributed TeraScale Facility , 2002, CCGRID.