SCoPE@Scuola: (In)-formative Paths on Topics Related with High Performance, Parallel and Distributed Computing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10659)


The SCoPE@Scuola initiative was born with the aim to inspire curiosity in high school students about High Performance Computing (HPC) and Parallel and Distributed Computing (PDC). The HPC/PDC world could be an interesting matter for students because is a necessary tool to solve challenging problems in science and technology and it provides context where a plenty of knowledge acquired at school can find a real application. In fact, the themes related to HPC/PDC involve a large range of knowledge and skills: from mathematical modelling of problems to algorithm design, from software implementation to design and management of complex computer systems. The initiative, begun at the end of 2014, involved several schools in the Naples (Italy) district, and has also been used for work-based learning activities and projects aimed to avoid students “dropouts”. The results collected during all the last years make us hopeful that such initiative could be useful both to increment students awareness about the utility in the real world of all the knowledge acquired at school and to help them in their future educational and/or working choices.


Education Scientific computing Parallel and Distributed Computing 


  1. 1.
    A High Performance Message Passing Library. Accessed 06 Oct 2017
  2. 2.
    Attivitá di alternanza Scuola-Lavoro - guida operativa per la scuola. Accessed 06 Oct 2017
  3. 3.
    BLAS (Basic Linear Algebra Subprograms). Accessed 06 Oct 2017
  4. 4.
    HPC Wire: Intel Brings Parallel Computing to High School. Accessed 06 Oct 2017
  5. 5.
    Il Programma “SCUOLA VIVA”. Accessed 06 Oct 2017
  6. 6.
    NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduate. Accessed 06 Oct 2017
  7. 7.
    Science, Technology, Engineering and Math: Education for Global Leadership. Accessed 06 Oct 2017
  8. 8.
    The Apache Hadoop Project. Accessed 06 Oct 2017
  9. 9.
    The Apache Hive data warehouse software. Accessed 06 Oct 2017
  10. 10.
    The Exascale Computing Project. Accessed 06 Oct 2017
  11. 11.
    The GNU Octave Software. Accessed 06 Oct 2017
  12. 12.
    The MATLAB Software. Accessed 06 Oct 2017
  13. 13.
    The Message Passing Interface (MPI) Standard. Accessed 06 Oct 2017
  14. 14.
    The SCoPE PON Project and the SCoPE data center. Accessed 06 Oct 2017
  15. 15.
    TORQUE Resource Manager. Accessed 06 Oct 2017
  16. 16.
    Recommendation of the European Parliament and of the Council on key competences for lifelong learning (2006/962/EC), December 2006. Accessed 06 Oct 2017
  17. 17.
    Communication from the Commission Europe 2020: A strategy for smart, sustainable and inclusive growth, March 2010. Accessed 06 Oct 2017
  18. 18.
    Barone, G.B., Boccia, V., Bottalico, D., Campagna, R., Carracciuolo, L.: SCoPE@Scuola: percorsi (in)formativi sulle tematiche del supercalcolo. In: Atti della Conferenza DIDAMATICA 2016 - Innovazione: sfida comune di scuola, universitá, ricerca e impresa. Associazione Italiana per l’Informatica ed il Calcolo Automatico (AICA), Milano, Italia (2016)Google Scholar
  19. 19.
    Carracciuolo, L., Casaburi, D., D’Amore, L., D’Avino, G., Maffettone, P., Murli, A.: Computational simulations of 3D large-scale time-dependent viscoelastic flows in high performance computing environment. J. Nonnewton. Fluid Mech. 166(23–24), 1382–1395 (2011)CrossRefzbMATHGoogle Scholar
  20. 20.
    Carracciuolo, L., D’Amore, L., Murli, A.: Towards a parallel component for imaging in PETSc programming environment: a case study in 3-D echocardiography. Parallel Comput. 32(1), 67–83 (2006)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Cesar, E., Cortés, A., Espinosa, A., Margalef, T., Moure, J.C., Sikora, A., Suppi, R.: Teaching parallel programming in interdisciplinary studies. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 66–77. Springer, Cham (2015). CrossRefGoogle Scholar
  22. 22.
    Connor, C., Bonnie, A., Grider, G., Jacobson, A.: Next generation HPC workforce development: the computer system, cluster, and networking summer institute. In: 2016 Workshop on Education for High-Performance Computing (EduHPC), pp. 32–39, November 2016Google Scholar
  23. 23.
    Eijkhout, V.: Teaching MPI from mental models. In: 2016 Workshop on Education for High-Performance Computing (EduHPC), pp. 14–18, November 2016Google Scholar
  24. 24.
    Gallopoulos, E., Sameh, A.: CSE: content and product. IEEE Comput. Sci. Eng. 4(2), 39–43 (1997)CrossRefGoogle Scholar
  25. 25.
    Gardner, W.B., Carter, J.D.: Using the pilot library to teach message-passing programming. In: 2014 Workshop on Education for High Performance Computing, pp. 1–8, November 2014Google Scholar
  26. 26.
    Mayer-Schonberger, V., Cukier, K.: Big Data: A Revolution That Will Transform How We Live, Work, and Think. Houghton Mifflin Harcourt, Boston (2013)Google Scholar
  27. 27.
    Nowicki, M., Marchwiany, M., Szpindler, M., Bała, P.: On-line service for teaching parallel programming. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 78–89. Springer, Cham (2015). CrossRefGoogle Scholar
  28. 28.
    Rostami, M.A., Bücker, H.M.: An educational module illustrating how sparse matrix-vector multiplication on parallel processors connects to graph partitioning. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 135–146. Springer, Cham (2015). CrossRefGoogle Scholar
  29. 29.
    Schlarb, M., Hundt, C., Schmidt, B.: SAUCE: a web-based automated assessment tool for teaching parallel programming. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 54–65. Springer, Cham (2015). CrossRefGoogle Scholar
  30. 30.
    Slezak, D.F., Turjanski, P.G., Montaldo, D., Mocskos, E.E.: Hands-on experience in HPC with secondary school students. IEEE Trans. Educ. 53(1), 128–135 (2010)CrossRefGoogle Scholar
  31. 31.
    Sozykin, A., Chernoskutov, M., Koshelev, A., Zverev, V., Ushenin, K., Solovyova, O.: Teaching heart modeling and simulation on parallel computing systems. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 102–113. Springer, Cham (2015). CrossRefGoogle Scholar
  32. 32.
    Sterling, T., Becker, D.J., Savarese, D., Dorband, J.E., Ranawake, U.A., Packer, C.V.: Beowulf: a parallel workstation for scientific computation. In: Proceedings of the 24th International Conference on Parallel Processing, pp. 11–14. CRC Press (1995)Google Scholar
  33. 33.
    Torbert, S., Vishkin, U., Tzur, R., Ellison, D.J.: Is teaching parallel algorithmic thinking to high school students possible?: one teacher’s experience. In: Proceedings of the 41st ACM Technical Symposium on Computer Science Education, SIGCSE 2010, pp. 290–294. ACM, New York (2010)Google Scholar
  34. 34.
    Valentine, D.: HPC/PDC immunization in the introductory computer science sequence. In: 2014 Workshop on Education for High Performance Computing, pp. 9–14, November 2014Google Scholar
  35. 35.
    Wing, J.M.: Computational thinking. Commun. ACM 49(3), 33–35 (2006)CrossRefGoogle Scholar
  36. 36.
    Zarestky, J., Bangerth, W.: Teaching high performance computing: lessons from a flipped classroom, project-based course on finite element methods. In: 2014 Workshop on Education for High Performance Computing, pp. 34–41, November 2014Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of Naples Federico IINaplesItaly
  2. 2.Liceo Classico e Scientifico “R. Cartesio”Giugliano, NaplesItaly
  3. 3.Italian National Research CouncilNaplesItaly

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