Heterogeneous multicore systems for signal processing

This thesis explores the capabilities of heterogeneous multi-core systems, based on multiple Graphics Processing Units (GPUs) in a standard desktop framework. Multi-GPU accelerated desk side computers are an appealing alternative to other high performance computing (HPC) systems: being composed of commodity hardware components fabricated in large quantities, their price-performance ratio is unparalleled in the world of high performance computing. Essentially bringing “supercomputing to the masses”, this opens up new possibilities for application fields where investing in HPC resources had been considered unfeasible before. One of these is the field of bioelectrical imaging, a class of medical imaging technologies that occupy a low-cost niche next to million-dollar systems like functional Magnetic Resonance Imaging (fMRI). In the scope of this work, several computational challenges encountered in bioelectrical imaging are tackled with this new kind of computing resource, striving to help these methods approach their true potential. Specifically, the following main contributions were made: Firstly, a novel dual-GPU implementation of parallel triangular matrix inversion (TMI) is presented, addressing an crucial kernel in computation of multi-mesh head models of encephalographic (EEG) source localization. This includes not only a highly efficient implementation of the routine itself achieving excellent speedups versus an optimized CPU implementation, but also a novel GPU-friendly compressed storage scheme for triangular matrices. Secondly, a scalable multi-GPU solver for non-hermitian linear systems was implemented. It is integrated into a simulation environment for electrical impedance tomography (EIT) that requires frequent solution of complex systems with millions of unknowns, a task that this solution can perform within seconds. In terms of computational throughput, it outperforms not only an highly optimized multi-CPU reference, but related GPU-based work as well. Finally, a GPU-accelerated graphical EEG real-time source localization software was implemented. Thanks to acceleration, it can meet real-time requirements in unpreceeded anatomical detail running more complex localization algorithms. Additionally, a novel implementation to extract anatomical priors from static Magnetic Resonance (MR) scansions has been included.

Severe Accident Simulation of Small Modular Reactors

Since the Three Mile Island Unit 2 (TMI-2), accident in 1979 which led to the meltdown of about one half of the reactor core and to limited releases of radioactive materials to the environment, an important international effort has been made on severe accident research. The present work aims to investigate the behaviour of a Small Modular Reactor during severe accident conditions. In order to perform these analyses, a SMR has been studied for the European reference severe accident analysis code ASTEC, developed by IRSN and GRS. In the thesis will be described in detail the IRIS Small Modular Reactor; the reference reactor chosen to develop the ASTEC input deck. The IRIS model was developed in the framework of a research collaboration with the IRSN development team. In the thesis will be described systematically the creation of the ASTEC IRIS input deck: the nodalization scheme adopted, the solution used to simulate the passive safety systems and the strong interaction between the reactor vessel and the containment. The ASTEC SMR model will be tested against the RELAP-GOTHIC coupled code model, with respect to a Design Basis Accident, to evaluate the capability of the ASTEC code on reproducing correctly the behaviour of the nuclear system. Once the model has been validated, a severe accident scenario will be simulated and the obtained results along with the nuclear system response will be analysed.