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### References

[1] G. Bella, S. Filippone, A. De Maio and M. Testa, A Simulation Model for Forest Fires, in J. Dongarra, K. Madsen, J. Wasniewski, editors, Proceedings of PARA 04 Workshop on State of the Art in Scientific Computing, pp. 546–553, Lecture Notes in Computer Science, Springer, 2005.

[2] D. Bertaccini and S. Filippone, Sparse approximate inverse preconditioners on high performance GPU platforms, Comput. Math. Appl., 71, (2016), no. 3, 693–711.

[3] A. Buttari, D. di Serafino, P. D’Ambra, S. Filippone, 2LEV-D2P4: a package of high-performance preconditioners, Applicable Algebra in Engineering, Communications and Computing, Volume 18, Number 3, May, 2007, pp. 223-239

[4] P. D’Ambra, S. Filippone, D. Di Serafino On the Development of PSBLAS-based Parallel Two-level Schwarz Preconditioners Applied Numerical Mathematics, Elsevier Science, Volume 57, Issues 11-12, November-December 2007, Pages 1181-1196.

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[6] Dongarra, J., DuCroz, J., Hammarling, S. and Duff, I., A Set of level 3 Basic Linear Algebra Subprograms, ACM Trans. Math. Softw. vol. 16, 1–17, 1990.

[7] J. J. Dongarra and R. C. Whaley, A User’s Guide to the BLACS v. 1.1, Lapack Working Note 94, Tech. Rep. UT-CS-95-281, University of Tennessee, March 1995 (updated May 1997).

[8] I. Duff, M. Marrone, G. Radicati and C. Vittoli, Level 3 Basic Linear Algebra Subprograms for Sparse Matrices: a User Level Interface, ACM Transactions on Mathematical Software, 23(3), pp. 379–401, 1997.

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[10] S. Filippone and M. Colajanni, PSBLAS: A Library for Parallel Linear Algebra Computation on Sparse Matrices, ACM Transactions on Mathematical Software, 26(4), pp. 527–550, 2000.

[11] S. Filippone and A. Buttari, Object-Oriented Techniques for Sparse Matrix Computations in Fortran 2003, ACM Transactions on Mathematical Software, 38(4), 2012.

[12] S. Filippone, P. D’Ambra, M. Colajanni, Using a Parallel Library of Sparse Linear Algebra in a Fluid Dynamics Applications Code on Linux Clusters, in G. Joubert, A. Murli, F. Peters, M. Vanneschi, editors, Parallel Computing - Advances & Current Issues, pp. 441–448, Imperial College Press, 2002.

[13] Gamma, E., Helm, R., Johnson, R., and Vlissides, J. 1995. Design Patterns: Elements of Reusable Object-Oriented Software. Addison-Wesley.

[14] Karypis, G. and Kumar, V., METIS: Unstructured Graph Partitioning and Sparse Matrix Ordering System. Minneapolis, MN 55455: University of Minnesota, Department of Computer Science, 1995. Internet Address: http://www.cs.umn.edu/~karypis.

[15] Lawson, C., Hanson, R., Kincaid, D. and Krogh, F., Basic Linear Algebra Subprograms for Fortran usage, ACM Trans. Math. Softw. vol. 5, 38–329, 1979.

[16] Machiels, L. and Deville, M. Fortran 90: An entry to object-oriented programming for the solution of partial differential equations. ACM Trans. Math. Softw. vol. 23, 32–49.

[17] Metcalf, M., Reid, J., Cohen, M., Bader, R. Modern Fortran explained. Oxford University Press, 2024.

[18] Metcalf, M., Reid, J. and Cohen, M. Modern Fortran explained. Oxford University Press, 2011.

[19] Rouson, D.W.I., Xia, J., Xu, X.: Scientific Software Design: The Object-Oriented Way. Cambridge University Press (2011)

[20] M. Snir, S. Otto, S. Huss-Lederman, D. Walker and J. Dongarra, MPI: The Complete Reference. Volume 1 - The MPI Core, second edition, MIT Press, 1998.

[21] D. Barbieri, V. Cardellini, S. Filippone and D. Rouson Design Patterns for Scientific Computations on Sparse Matrices, HPSS 2011, Algorithms and Programming Tools for Next-Generation High-Performance Scientific Software, Bordeaux, Sep. 2011

[22] Cardellini, V., Filippone, S., and Rouson, D. 2014, Design patterns for sparse-matrix computations on hybrid CPU/GPU platforms, Scientific Programming 22, 1, 1–19.

[23] D. Barbieri, V. Cardellini, A. Fanfarillo, S. Filippone, Three storage formats for sparse matrices on GPGPUs, Tech. Rep. DICII RR-15.6, Università di Roma Tor Vergata (February 2015).

[24] S. Filippone, V. Cardellini, D. Barbieri, and A. Fanfarillo. Sparse matrix-vector multiplication on GPGPUs. ACM Trans. Math. Softw., 43(4):30:1–30:49, 2017.

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