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. A detailed list of the projects follows.

Below the list of our projects.

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.

Performance-driven refactoring of Potts associative memory network model.


Leonardo Romor

Neural networks simulations have always been a complex computational chal- lenge because of the requirements of large amount of computational and memory resources. Due to the nature of the problem, a high performance computing approach becomes vital, because the dynamics often involves the update of a large network for a large number of time steps. Moreover, the parameter space can be fairly large. An advanced optimization for the single time step is therefore necessary, as well as a strategy to explore the parameter space in an automatic fashion.