Modern Parallel Architectures to Speed Up Numerical Simulation

Part of the Advances in Mechanics and Mathematics book series (AMMA, volume 41)


Applications of graphics processing units (GPU) and field programmable gate array (FPGA) for computer codes acceleration are discussed. Most of the high positions in the top-100 list of supercomputers (clusters) are taken by a hybrid type hardware. First, the authors provide an idea about GPU and FPGA architectures. The use of FPGA has two main obstacles, involving the necessity for manual coding of algorithms up to the register transfer level (RTL). So, modern high level synthesis (HLS) technology to use FPGA is briefly introduced. Then several examples of speeding up algorithms mostly from the Earth Sciences are given. The considered examples of GPU use are: decomposition of seismic records by wave packages (performance gain of 350 times is achieved); the convolution problems with Green’s function (the computation time at single GPU is 162 times faster than the original code version); and tsunami wave propagation (simulation of tsunami wave propagation was accelerated 100 times compared to one CPU). In some cases FPGA shows even better results compared to GPU, in particular for tsunami modelling (five times faster than compared even to GPU Tesla K40), HD video stream processing. As for FPGA-based data processing, the following examples are here considered: searching for small objects on a series of images; searching object on the image; and motif search in DNA sequence. In all cases comparison with one CPU is given.


  1. 1.
    Candes, E. J., Donoho, D. L.: New Tight Frames of Curvelets and Optimal Representations of Objects with Piecewise-C2 Singularities. Comm. Pure Appl. Math. 57, 219–266 (2002).CrossRefGoogle Scholar
  2. 2.
    What’s the Difference Between a CPU and a GPU? Cited 14 Aug 2017.
  3. 3.
    Duchkov, A. A., Andersson, F. A., Hoop, M. V.: Discrete Almost-Symmetric Wave Packets and Multiscale Geometrical Representation of (Seismic) Waves. IEEE Transactions on Geoscience and Remote Sensing. 48, No. 9, 3408–3423 (2010).CrossRefGoogle Scholar
  4. 4.
    Flynn’s taxonomy Cited 14 Aug 2017.
  5. 5.
    Forecast Inundation Models Cited 23 Aug 2017.
  6. 6.
    Goryunov, E., Romanenko, A., Lavrentiev, M., Lysakov, K.: Modern simulation tools for real time numerical simulation of ocean-related processes. OCEANS 2015 - MTS/IEEE Washington 7404385 (2016)Google Scholar
  7. 7.
    GPU Technology conference Cited 14 Aug 2017.
  8. 8.
    Hennenfent, G., Herrmann, F.: Seismic Denoising with Non-Uniformly Sampled Curvelets. Computing in Science and Engineering. 8 (3), 16–25 (2006).CrossRefGoogle Scholar
  9. 9.
    High-level synthesis. Cited 14 Aug 2017.
  10. 10.
    Hundreds of applications accelerated. Cited 14 Aug 2017.
  11. 11.
    Lavrentiev, M.M., Romanenko, A.A.: Modern Hardware Solutions to Speed Up Tsunami Simulation Codes. Geophysical research abstracts, 12, EGU2010-3835 (2010)Google Scholar
  12. 12.
    Lavrentiev, M., Romanenko, A.: Modern Hardware to Simulate Tsunami Wave Propagation. Proc. Automation, Control, and Information Technology (ACIT 2010), 151–157 (2010)Google Scholar
  13. 13.
    Lavrentiev, M., Romanenko, A.: Tsunami Wave Parameters Calculation before the Wave Approaches Coastal Line. Proceedings of the Twenty-fourth (2014) International Ocean and Polar Engineering Conference, Busan, Korea, June 15-20, 2014, 3, 96–102 (2014)Google Scholar
  14. 14.
    Lavrentiev, M., Romanenko, A., Lysakov, K.: Modern Computer Architecture to Speed-Up Calculation of Tsunami Wave Propagation. Proceedings of the Eleventh (2014) Pacific/Asia Offshore Mechanics Symposium, Shanghai, China, October 12–16, 186–191 (2014)Google Scholar
  15. 15.
    Lysakov, K., Shadrin M.: FPGA Based Hardware Accelerator for High Performance Data–Stream Processing. Pattern Recognition and Image Analysis, 23, No. 1, 26–34 (2013)CrossRefGoogle Scholar
  16. 16.
    Naghizadeh, M., Sacchi, M. D.: Beyond Alias Hierarchical Scale Curvelet Interpolation of Regularly and Irregularly Sampled Seismic Data. Geophysics. 75, 189–202 (2010).CrossRefGoogle Scholar
  17. 17.
    Neelamani, R., Baumstein, A. I., Gillard, D. G., Hadidi, M. T., Soroka, W. L.: Coherent and Random Noise Attenuation Using the Curvelet Transform. The Leading Edge. 27, No. 2, 240–246 (2008).CrossRefGoogle Scholar
  18. 18.
    Nikitin, V.V., Romanenko, A.A., Duchkov, A.A., Andersson, F.: Parallel implementation of 3D-wave package decomposition on GPU and its application in geophysics. Vestnik of NSU. IT series. 11, No. 1, 93–104 (2013) (in Russian).Google Scholar
  19. 19.
  20. 20.
    Piz Daint computer system. Cited 14 Aug 2017.
  21. 21.
  22. 22.
    Titov, V.V., Synolakis, C.E.: Numerical modeling of tidal wave runup. Journal of Waterway, Port, Coastal and Ocean Engineering. 124, No 4, 157–171 (1998).CrossRefGoogle Scholar
  23. 23.
    Trinity computer system. Cited 14 Aug 2017.
  24. 24.
    Zyatkov, N., Ayzenberg, A., Aizenberg, A.M., Romanenko, A.: Highly-optimized TWSM Algorithm for Modeling Cascade Diffraction in Terms of Propagation-absorption Matrices. Extended Abstracts, 75-th Conference and Exhibition, European Association of Geoscientists & Engineers, London, England, 10–13 June 2013, Th-P02–11 (2013)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Novosibirsk State UniversityNovosibirskRussian Federation
  2. 2.Institute of Automation and ElectrometryNovosibirskRussian Federation

Personalised recommendations