1887
  1. Home
  2. Conferences
  3. Conference Proceedings
  4. Fourth EAGE Workshop on High Performance Computing for Upstream 2019
  5. Conference paper

Abstract

Summary

Through the last decades, multiparametric traveltime has been used as an efficient technique for inversion and seismic imaging. Despite its efficiency, the required multiparameter estimations present itself as a challenge, since additional computational costs must be added. To mitigate this problem meta-heuristics, such as Adaptive Differential Evolution (JADE), can be applied. Despite the fast convergence of JADE, the runtime execution of parameter estimation can be suboptimal in many cases, mainly when the intrinsic embarrassingly parallel characteristic of the problem is not fully explored. In this paper, we propose a parallel implementation of parameter estimation with JADE in both GPU (through CUDA) and CPU (through OpenMP). We resort to cloud computing, specifically Amazon Web Service, to validate our implementation; executing our code on five different instances types. Experimental results show a considerable speedup obtained using GPU instances instead of CPU ones. Furthermore, qualitative results reveal similarity between both implementations. Thus, without any loss of quality, we are able to obtain an immense gain in execution time and a reduction of cloud computing costs.

© EAGE Publications BV
Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201903298
2019-10-07
2024-04-27

  • From This Site

    /content/papers/10.3997/2214-4609.201903298
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

References

  1. Barros, T., Ferrari, R., Krummenauer, R. and Lopes, R.
    [2015] Differential evolution-based optimization procedure for automatic estimation of the common-reflection surface traveltime parameters. GEOPHYSICS, 80(6), WD189–WD200.
     [Google Scholar]
  2. Borin, E., Benedicto, C., Rodrigues, I.L., Pisani, F., Tygel, M. and Breternitz, M.
    [2016] PY-PITS: A Scalable Python Runtime System for the Computation of Partially Idempotent Tasks. In: 2016 International Symposium on Computer Architecture and High Performance Computing Workshops (SBAC-PADW). 7–12.
     [Google Scholar]
  3. Garabito, G., Cruz, J.C., Hubral, P. and Costa, J.
    [2001] Common reflection surface stack: A new parameter search strategy by global optimization. In: SEG Technical Program Expanded Abstracts 2001. Society of Exploration Geophysicists.
     [Google Scholar]
  4. Herlihy, M., S.N.
    [2012] The Art of Multiprocessor Programming. Elsevier.
     [Google Scholar]
  5. Jäger, R., Mann, J., Höcht, G. and Hubral, P.
    [2001] Common-reflection-surface stack: Image and attributes. GEOPHYSICS, 66(1), 97–109.
     [Google Scholar]
  6. Neidell, N.S. and Taner, M.T.
    [1971] SEMBLANCE AND OTHER COHERENCY MEASURES FOR MULTICHANNEL DATA. GEOPHYSICS, 36(3), 482–197.
     [Google Scholar]
  7. Okita, N., Coimbra, T., Rodamilans, C., Tygel, M. and Borin, E.
    [2018] Using SPITS to Optimize the Cost of High-Performance Geophysics Processing on the Cloud. In: First EAGE Workshop on High Performance Computing for Upstream in Latin America, Santander, Colombia. EAGE.
     [Google Scholar]
  8. Walda, J. and Gajewski, D.
    [2017] Determination of wavefront attributes by differential evolution in the presence of conflicting dips. GEOPHYSICS, 82(4), V229–V239.
     [Google Scholar]
  9. Zhang, J. and Sanderson, A.C.
    [2009] JADE: Adaptive Differential Evolution With Optional External Archive. IEEE Transactions on Evolutionary Computation, 13(5), 945–958.
     [Google Scholar]
http://instance.metastore.ingenta.com/content/papers/10.3997/2214-4609.201903298
Loading
Accelerating seismic parameter estimation with Adaptive Differential Evolution (JADE) and graphics processing units (GPUs)
Conference Proceedings 2019, 1 (2019); https://doi.org/10.3997/2214-4609.201903298
/content/papers/10.3997/2214-4609.201903298
/content/papers/10.3997/2214-4609.201903298
Loading

Data & Media loading...

Most Cited This Month Most Cited RSS feed

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error