Contact Dr Salvatore Filippone
Areas of expertise
Dr. Filippone has an MSc in computer engineering and a PhD in Mathematics, both from the University of Rome "Tor Vergata".
Prior to joining Cranfield he has held positions with IBM Co., where he was a lead developer of the ESSL scientific software products, and at the University of Rome Tor Vergata, where he has taught introductory programming and introductory numerical analysis to engineering undergraduate, and advanced computational methods to graduate students.
Dr. Filippone is the Leader of the "Software Engineering for Tchnical Computing" option of the MSc in Computational Software Techniques for Engineering.
He specializes in software to solve complex problems requiring sophisticated numerical algorithms that will stretch the number crunching abilities of a given machine to their utmost capacity.
- Numerical Linear Algebra
Algorithms, Library Development, Applications
- High Performance Computing
Parallel Computing Programming Environments, Distributed Memory Architectures, Shared Memory Architectures, Software Tools
- Engineering Applications
Parallel Computing Techniques in Fluid Dynamics, Structural Analysis, ElectroMagnetism and Antennas, Optimization.
Articles In Journals
- Abdullahi Hassan A, Cardellini V, D’Ambra P, di Serafino D & Filippone S (2019) Efficient algebraic multigrid preconditioners on clusters of GPUs, Parallel Processing Letters, 29 (1) Article No. 1950001.
- D'Ambra P, Filippone S & Vassilevski P (2018) BootCMatch: a software package for bootstrap AMG based on graph weighted matching, ACM Transactions on Mathematical Software, 44 (4) Article No. 39.
- Filippone S, Cardellini V, Barbieri D & Fanfarillo A (2017) Sparse matrix-vector multiplication on GPGPUs, ACM Transactions on Mathematical Software, 43 (4) Article No. 30.
- Cardellini V, Fanfarillo A & Filippone S (2017) Coarray-based load balancing on heterogeneous and many-core architectures, Parallel Computing, 68 (October) 45-58.
- Bertaccini D & Filippone S (2016) Sparse approximate inverse preconditioners on high performance GPU platforms, Computers and Mathematics with Applications, 71 (3) 693-711.
- Filippone S & D'Ambra P. (2016) A parallel generalized relaxation method for high-performance image segmentation on GPUs, Journal of Computational and Applied Mathematics, 293 35-44.
- Nanthaamornphong A, Carver J, Morris K & Filippone S (2015) Extracting UML Class Diagrams from Object-Oriented Fortran: ForUML, Scientific Programming, 2015 Article No. 421816.
- Cardellini V, Filippone S & Rouson D (2014) Design patterns for sparse-matrix computations on hybrid CPU/GPU platforms, Scientific Programming, 22 (1) 1-19.
- Filippone S & Buttari A (2012) Object-oriented techniques for sparse matrix computations in Fortran 2003, ACM Transactions on Mathematical Software, 38 (4) Article No. 23.
- D’Ambra P, di Serafino D & Filippone S (2012) Performance analysis of parallel Schwarz preconditioners in the LES of turbulent channel flows, Computers and Mathematics with Applications, 65 (3) 352-361.
- Morris K, Rouson DWI, Lemaster MN & Filippone S (2012) Exploring capabilities within ForTrilinos by solving the 3D Burgers equation, Scientific Programming, 20 (3) 275-292.
- D'Ambra P, di Serafino D & Filippone S (2010) MLD2P4: A package of parallel algebraic multilevel domain decomposition preconditioners in Fortran 95, ACM Transactions on Mathematical Software, 37 (3) Article No. 30.
- D'Ambr P, di Serafino D & Filippone S (2007) On the development of PSBLAS-based parallel two-level Schwarz preconditioners, Applied Numerical Mathematics, 57 (11-12) 1181-1196.
- Buttari A, Eijkhout V, Langou J & Filippone S (2007) Performance optimization and modeling of blocked sparse kernels, International Journal of High Performance Computing Applications, 21 (4) 467-484.
- Hassan AA, Cardellini V & Filippone S (2017) A framework for unit testing with coarray Fortran. In: SCS Spring Simulation Multi-Conference (SpringSim'17), Virginia Beach, 23-26 April 2017.
- Cardellini V, Fanfarillo A & Filippone S (2016) Heterogeneous CAF-based load balancing on Intel Xeon Phi. In: 2016 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Chicago, 23-27 May 2016.