My PhD research activity was funded by the Istituto Nazionale di Fisica
Nucleare (INFN) and by the Physics Department of the University of Pisa.
The goal of the research activity is to face the problem of the reconstruction
by exploiting two different kinds of parallelism in two different research
fields: high energy physics (HEP) and medical physics. Reconstruction can
be a very challenging computational task in both fields.
As regards HEP experiments, I worked to the optimization of the Fast
Tracker (FTK) processor for the ATLAS experiment at the Large Hadron
Collider (LHC) located at the CERN laboratory in Geneva. ATLAS is one
of the two general purpose detectors designed to measure the product of the
proton-proton collisions at the LHC. The key role in the novel technology is
played by the Associative Memory (AM) that is a massively parallel system
used to provide very fast online event selection of the images of the particles
emerging from the collisions of protons.
As regards medical physics, I developed a parallel implementation of
an iterative algorithm for image reconstruction in 3D Positron Emission
Tomography (PET) using Graphics Processing Units (GPUs). The implementation
of the iterative algorithms can be a very challenging task due to
the massive amount of computation required to incorporate accurate system
modeling. For this reason, GPUs are currently used to attain reconstructed
images in a practical time because they high performance supporting
massive parallel computing power
