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Recent Memory and Performance Improvements in Octopus Code

  • Joseba Alberdi-Rodriguez
  • Micael J. T. Oliveira
  • Pablo García-Risueño
  • Fernando Nogueira
  • Javier Muguerza
  • Agustin Arruabarrena
  • Angel Rubio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8582)

Abstract

In this work we present the improvements made to the Octopus code in order to reduce the memory requirements and to optimise parallel data distribution. Both topics are central for efficiency and feasibility of calculations when the system must be run in a large HPC environment. These modifications were mainly made in the real-space mesh partitioning and mapping algorithms, and are thus transferable to other codes using this type of real-space representation of data. The code became much more efficient, and we present several scalability results showing that it is now possible to address ab-initio quantum-mechanical simulations of the interaction of light with big biomolecules, paving the way for a better understanding of phenomena such as energy conversion in plants.

Keywords

DFT TDDFT Octopus software HPC memory optimisations 

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References

  1. 1.
    García-Risueño, P., Ibáñez, P.E.: A review of high performance computing foundations for scientists. International Journal of Modern Physics C (IJMPC), 1230001 (2012)Google Scholar
  2. 2.
    Andrade, X., Alberdi-Rodriguez, J., Strubbe, D., Oliveira, M., Nogueira, F., Castro, A., Muguerza, J., Arruabarrena, A., Louie, S., Aspuru-Guzik, A., et al.: Time-dependent density-functional theory in massively parallel computer architectures: the octopus project. Journal of Physics: Condensed Matter 24(23), 233202 (2012)Google Scholar
  3. 3.
    Castro, A., Appel, H., Oliveira, M., Rozzi, C.A., Andrade, X., Lorenzen, F., Marques, M.A.L., Gross, E.K.U., Rubio, A.: Octopus: a tool for the application of time-dependent density functional theory. Phys. Status Solidi (b) 243(11), 2465–2488 (2006)CrossRefGoogle Scholar
  4. 4.
    Marques, M.A.L., Castro, A., Bertsch, G.F., Rubio, A.: Octopus: a first-principles tool for excited electron–ion dynamics. Comput. Phys. Commun. 151(1), 60–78 (2003)CrossRefGoogle Scholar
  5. 5.
    Hohenberg, P., Kohn, W.: Inhomogeneous Electron Gas. Phys. Rev. 136(3B), B864–B871 (1964)Google Scholar
  6. 6.
    Kohn, W., Sham, L.J.: Self-Consistent Equations Including Exchange and Correlation Effects. Phys. Rev. 140(4A), A1133–A1138 (1965)Google Scholar
  7. 7.
    Fiolhais, C., Nogueira, F., Marques, M.A.L. (eds.): A Primer in Density Functional Theory, 1st edn. Lecture Notes in Physics, vol. 620. Springer (2003)Google Scholar
  8. 8.
    Marques, M.A., Maitra, N.T., Nogueira, F.M.: Fundamentals of Time-Dependent Density Functional Theory, vol. 837. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  9. 9.
    Genovese, L., Deutsch, T., Neelov, A., Goedecker, S., Beylkin, G.: Efficient solution of Poisson’s equation with free boundary conditions. J. Chem. Phys. 125(7), 074105 (2006)Google Scholar
  10. 10.
    Pippig, M.: PFFT - An extension of FFTW to massively parallel architectures. SIAM J. Sci. Comput. 35, C213 – C236 (2013)Google Scholar
  11. 11.
    Rozzi, C.A., Varsano, D., Marini, A., Gross, E.K.U., Rubio, A.: Exact coulomb cutoff technique for supercell calculations. Phys. Rev. B 73, 205119 (2006)Google Scholar
  12. 12.
    Karypis, G., Kumar, V.: METIS - Unstructured Graph Partitioning and Sparse Matrix Ordering System, Version 2.0. Technical report (1995)Google Scholar
  13. 13.
    Karypis, G., Kumar, V.: Parallel multilevel k-way partitioning scheme for irregular graphs. In: Proceedings of the 1996 ACM/IEEE Conference on Supercomputing (CDROM), Supercomputing 1996. IEEE Computer Society, Washington, DC (1996)Google Scholar
  14. 14.
    García-Risueño, P., Alberdi-Rodriguez, J., Oliveira, M.J.T., Andrade, X., Pippig, M., Muguerza, J., Arruabarrena, A., Rubio, A.: A survey of the performance of classical potential solvers for charge distributions. Journal of Computational Chemistry 35(6), 427–444 (2014)CrossRefGoogle Scholar
  15. 15.
    Sosa, C., Knudson, B.: IBM system Blue Gene solution: Blue Gene/P application development. IBM International Technical Support Organization (2008)Google Scholar
  16. 16.
    Gilge, M.: et al.: IBM System Blue Gene Solution Blue Gene/Q Application Development. IBM Redbooks (2013)Google Scholar
  17. 17.
    Nethercote, N., Walsh, R., Fitzhardinge, J.: Building workload characterization tools with valgrind. In: 2006 IEEE International Symposium on Workload Characterization, p. 2 (2006)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Joseba Alberdi-Rodriguez
    • 1
    • 2
    • 3
  • Micael J. T. Oliveira
    • 3
    • 4
  • Pablo García-Risueño
    • 5
  • Fernando Nogueira
    • 3
  • Javier Muguerza
    • 1
  • Agustin Arruabarrena
    • 1
  • Angel Rubio
    • 2
    • 6
    • 7
  1. 1.Dept. of Computer Architecture and TechnologyUniversity of the Basque Country UPV/EHUDonostia-San SebastiánSpain
  2. 2.Nano-Bio Spectroscopy Group and European Theoretical Spectroscopy Facility, Spanish nodeUniversity of the Basque Country UPV/EHUDonostia-San SebastiánSpain
  3. 3.Center for Computational PhysicsUniversity of CoimbraCoimbraPortugal
  4. 4.Unité NanomatUniversité de LiègeLiègeBelgium
  5. 5.Institut für Physik und IRIS AdlershofHumboldt Universität zu BerlinBerlinGermany
  6. 6.Centro de Física de MaterialesUniversity of the Basque Country UPV/EHUDonostia-San SebastiánSpain
  7. 7.Fritz-Haber Institut der Max-Planck GesellschaftBerlinGermany

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