Students' Projects

Student’s final projects represent the ultimate delivery of the master program. They are also the brightest representation of the program itself.

They combine advanced computing technologies with scientific and industrial frontier applications.

The applications range from: partial differential equations, mechanical and fluidynamical modelling, satellite data processing, neuroscience modelling, molecular dynamics.


Below the list of MHPC projects.

A computational ecosystem for near real-time satellite data processing


Stefano Piani

The aim of this work is the development of a computational ecosystem for nearly real-time inversion of high spectral resolution infrared data coming from meteorological satellites. The ecosystem has been developed as nearly real-time demonstration project to elaborate the level 2 products derived from MTG-IRS

Download link: A computational ecosystem for near real-time satellite data processing

Improving Performance of Basis-set-free Hartree-Fock Calculations Through Grid-based Massively Parallel Techniques


Edwin Fernando Posada Correa

Multicenter numerical integration scheme for polyatomic molecules has been implemented as an initial step to develop a complete basis-set-free Hartree-Fock (HF) software. The validation of the integration scheme includes the integration of the total density and the calculation of Coulomb potentials for several diatomic molecules. A finite difference method is used to solve Poisson's equation for the Coulomb potential on numerical orbitals expanded on the interlocking multicenter quadrature grid. The implementation which rely on OpenMP and CUDA shows a speedup up to 30x.

Hybrid Parallelisation Strategies for Boundary Element Methods


Nicola Giuliani

Whenever a mathematical problem admits a boundary integral representation, it can be straightforwardly discretised by Boundary Element Methods (BEM). In this work, we present an efficient hybrid parallel solver for FSI problems based on collocation BEM.

High-performance implementation of the Density Peak clustering algorithm


Marco Borelli

We developed a parallel implementation of the “Density Peak” clustering algorithm, exploiting C++11, OpenMP and the FLANN library for k-nearest-neighbour search. The modified algorithm is approximately 50 times faster than the original version on datasets with half a million points, and scales almost linearly with the dataset size. Thanks to improvements on the density estimation and assignation procedure, the algorithm is also unsupervised and non-parametric.

Thesis not made available by the student.

Analysis of Hybrid Parallelization strategies: Simulation of Anderson Localization and Kalman Filter for LHCb Triggers


Jimmy Aguilar Mena

This thesis presents two experiences of hybrid programming applied to condensed matter and high energy physics. The two projects differ in various aspects, but both of them aim to analyse the benefits of using accelerated hardware to speedup the calculations in current science-research scenarios.