Skip to main content
SpringerLink
Log in

Performance Analysis of WRF Simulations in a Public Cloud and HPC Environment

  • Conference paper
  • First Online:
Complex, Intelligent, and Software Intensive Systems (CISIS 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 611))

Included in the following conference series:

Abstract

The Weather Research and Forecasting (WRF) Model is a numerical weather prediction system designed for both atmospheric research and operational forecasting needs. WRF requires a large amount of CPU power which increases drastically if WRF is used to model a big geographical area with a high resolution. To satisfy the computational demand WRF requires large number of computing resources through infrastructures such as clusters in grid or cloud. In this paper the performance analysis of different WRF simulations to the Amazon Web Services (AWS) cloud computing environment (single node and cluster) compared to that of a HCP cluster is presented.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 259.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    SR-IOV is a method of device virtualization that provides higher I/O performance and lower CPU utilization when compared to traditional virtualized network interfaces.

  2. 2.

    A placement group is a logical grouping of instances within a single Availability Zone. Placement groups are recommended for applications that benefit from low network latency, high network throughput, or both.

References

  1. The weather research & forecasting model. http://www.wrf-model.org/index.php. Accessed 06 Mar 2017

  2. Thackston, R., Fortenberry, R.C.: The performance of low-cost commercial cloud computing as an alternative in computational chemistry. J. Comput. Chem. 36(12), 926–933 (2015)

    Article  Google Scholar 

  3. Zhang, Q., Cheng, L., Boutaba, R.: Cloud computing: state-of-the-art and research challenges. J. Internet Serv. Appl. 1(1), 7–18 (2010)

    Article  Google Scholar 

  4. Evangelinos, C., Hill, C.N.: Cloud computing for parallel scientific HPC applications: feasibility of running coupled atmosphere-ocean climate models on Amazon’s EC2. In: The 1st Workshop on Cloud Computing and its Applications (CCA) (2008)

    Google Scholar 

  5. Zinno, I., Elefante, S., Mossucca, L., De Luca, C., Manunta, M., Terzo, O., Lanari, R., Casu, F.: A first assessment of the p-sbas dinsar algorithm performances within a cloud computing environment. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 8(10), 4675–4686 (2015)

    Article  Google Scholar 

  6. Pilosu, L., Ruiu, P., Goga, K., Budroni, M.A.: Automated cloud computing approach for the simulation of chemo-hydrodynamic problems. In: 2016 10th International Conference on Complex, Intelligent, and Software Intensive Systems (CISIS), pp. 438–443. IEEE (2016)

    Google Scholar 

  7. Mossucca, L., Terzo, O., Goga, K., Acquaviva, A., Abate, F., Provenzano, R.: NGS workflow optimization using a hybrid cloud infrastructure. Int. J. Adv. Netw. Serv. 5(3 & 4), 2012 (2012)

    Google Scholar 

  8. Flores-Contreras, J., Parlavantzas, N., Duran-Limon, H.A.: Efficient execution of the WRF model, other HPC applications in the cloud. Earth Sci. Inform. 9(Issue 3), 365–382 (2016). doi:10.1007/s12145-016-0253-7

    Google Scholar 

  9. Overview of the cluster configuration. https://www.lrz.de/services/compute/linux-cluster/overview/. Accessed 30 Jan 2017

  10. Amazon machine image (AMI). http://docs.aws.amazon.com/AWSEC2/latest/UserGuide/AMIs.html. Accessed 06 Mar 2017

  11. Amazon cfncluster. https://aws.amazon.com/hpc/cfncluster/. Accessed 06 Mar 2017

  12. Aws cloudformation. https://aws.amazon.com/cloudformation/. Accessed 06 Mar 2017

  13. Aws virtual private cloud (VPC). https://aws.amazon.com/vpc/. Accessed 06 Mar 2017

  14. Amazon simple storage service (s3). https://aws.amazon.com/s3/. Accessed 06 Mar 2017

  15. Amazon elastic block store. https://aws.amazon.com/ebs/. Accessed 06 Mar 2017

  16. Fiori, E., Comellas, A., Molini, L., Rebora, N., Siccardi, F., Gochis, D.J., Tanelli, S., Parodi, A.: Analysis and hindcast simulations of an extreme rainfall event in the mediterranean area: the genoa 2011 case. Atmos. Res. 138, 13–29 (2014)

    Article  Google Scholar 

  17. Rasmussen, R.M., Thompson, G., Manning, K.: Explicit forecasts of winter precipitation using an improved bulk microphysics scheme. Part I: Description and sensitivity analysis. Mon. Weather Rev. 132(2), 519–542 (2004)

    Article  Google Scholar 

  18. Hong, S.-Y., Noh, Y., Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134(9), 2318–2341 (2006)

    Article  Google Scholar 

  19. Supermuc petascale system. https://www.lrz.de/services/compute/supermuc/systemdescription/Flyer.pdf. Accessed 15 Mar 2017

  20. Docker. https://www.docker.com/what-docker#/overview. Accessed 06 Mar 2017

Download references

Author information

Authors and Affiliations

  1. Istituto Superiore Mario Boella (ISMB), Torino, Italy

    Klodiana Goga, Pietro Ruiu & Olivier Terzo

  2. CIMA Research Foundation, Savona, Italy

    Antonio Parodi

Authors
  1. Klodiana Goga
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Antonio Parodi
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pietro Ruiu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Olivier Terzo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Klodiana Goga .

Editor information

Editors and Affiliations

  1. Department of Information and Communication Engineering, Faculty of Information Engineering, Fukuoka Institute of Technology, Fukuoka, Japan

    Leonard Barolli

  2. Politècnico di Torino, Istituto Superiore Mario Boella, Turin, Italy

    Olivier Terzo

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG

About this paper

Cite this paper

Goga, K., Parodi, A., Ruiu, P., Terzo, O. (2018). Performance Analysis of WRF Simulations in a Public Cloud and HPC Environment. In: Barolli, L., Terzo, O. (eds) Complex, Intelligent, and Software Intensive Systems. CISIS 2017. Advances in Intelligent Systems and Computing, vol 611. Springer, Cham. https://doi.org/10.1007/978-3-319-61566-0_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-61566-0_35

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61565-3

  • Online ISBN: 978-3-319-61566-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation