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Abstract

Summary

Full Waveform Inversion (FWI) is a mathematically and computationally challenging inverse problem of finding a quantitative rock-property description of the subsurface to match observed seismogram data. The inversion employs a forward model, which relates the subsurface to the observed seismograms. FWI and other seismic applications require High Performance Computing (HPC) to simulate the dynamics of such complex models. Not a long ago, companies, research institutes, and universities used to acquire clusters of computers to maintain on-premise. Recently, cloud computing has become an alternative posing a challenge to the end-users, who have to decide whether they should execute their applications: on their local clusters or burst them to a remote cloud provider. In this paper, we present a decision support method to choose the correct environment considering trade-offs, such as resource costs, performance, and availability on such heterogeneous execution platforms. We evaluated the system using our FWI application and preliminary results indicate that users of HPC applications can benefit from such a cloud advisory system to reduce costs, turnaround times, and even boost local platforms.

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/content/papers/10.3997/2214-4609.201414024
2015-09-13
2024-04-23

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References

  1. Aravkin, A., Friedlander, M., Herrmann, F. and van Leeuwen, T.
    [2012] Robust inversion, dimensionality reduction, and randomized sampling. Mathematical Programming, 134(1), 101–125.
     [Google Scholar]
  2. Aravkin, A.Y. and van Leeuwen, T.
    [2012] Estimating nuisance parameters in inverse problems. Inverse_Problems, 28(11), 115016.
     [Google Scholar]
  3. Becker, S., Horesh, L., Aravkin, A., van den Berg, E. and Zhuk, S.
    [2015] General optimization framework for robust and regularized 3d fwi. 77th_EAGE_Conference_and_Exhibition_2015, EAGE, 4.
     [Google Scholar]
  4. Binotto, A., Sultanum, N. and Cerqueira, R.
    [2014] Cloud-based remote visualization of big data to subsurface exploration. SPE Large Scale Computing and Big Data Challenges in Reservoir Simulation Conference and Exhibition, Society of Petroleum Engineers, SPE—172983—MS.
     [Google Scholar]
  5. Buyya, R., Broberg, J. and Goscinski, A.M.
    [2010] Cloud computing: Principles and paradigms, vol. 87. John Wiley & Sons.
     [Google Scholar]
  6. Gupta, A. et al.
    [2013] The who, what, why and how of high performance computing applications in the cloud. Proceedings of the International Conference on Cloud Computing Technology and Science.
     [Google Scholar]
  7. Horesh, L. and Haber, E.
    [2009] Sensitivity computation of the ℓ1 minimization problem and its application to dictionary design of ill-posed problems. Inverse Problems, 25(9), 095009.
     [Google Scholar]
  8. Mantripragada, K., Binotto, A., Tizzei, L.P. and Netto, M.A.S.
    [2015] A feasibility study of using hpc cloud environment for seismic exploration. 77th EAGE Conference and Exhibition 2015, EAGE, 4pp.
     [Google Scholar]
  9. Moore, S. et al.
    [2015] Semi-discrete matrix free formulation of 3d full waveform elastic modeling and inversion. 77th EAGE Conference and Exhibition 2015, EAGE, 4pp.
     [Google Scholar]
  10. Napper, J. and Bientinesi, P.
    [2009] Can cloud computing reach the top500?Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop.
     [Google Scholar]
  11. Ostermann, S., Iosup, A., Yigitbasi, N., Prodan, R., Fahringer, T. and Epema, D.
    [2010] A performance analysis of EC2 cloud computing services for scientific computing. In: International Conference on Cloud Computing. Springer Berlin Heidelberg.
     [Google Scholar]
  12. Souza, P. et al.
    [2014] A cluster of workstations for seismic data processing using gpu. EAGE Workshop on High Performance Computing for Upstream, EAGE, 4pp.
     [Google Scholar]
  13. Vecchiola, C, Pandey, S. and Buyya, R.
    [2009] High-Performance Cloud Computing: A View of Scientific Applications. International Symposium on on Pervasive Systems, Algorithms, and Networks.
     [Google Scholar]
  14. Virieux, J. and Operto, S.
    [2009] An overview of full-waveform inversion in exploration geophysics. Geophysics, 74(6), WCC1–WCC26.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201414024
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Supporting the Scheduling over Local HPC and Cloud Platforms: An FWI Case Study
Conference Proceedings 2015, 1 (2015); https://doi.org/10.3997/2214-4609.201414024
/content/papers/10.3997/2214-4609.201414024
/content/papers/10.3997/2214-4609.201414024
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