Objectives

The general objective of this project is the development of parallel algorithms for the study of anisotropic flow in relativistic heavy ion collisions using graphics processing unit.

The most important goal of physics with ultra-relativistic heavy ions is the discovery and characterization of QGP. After nine years of research at the RHIC some of the properties of phase

transition from hadronic matter to quark gluon plasma have been discovered. The fluid like behavior of QGP was one of the most striking results obtained by the four experiments from RHIC. The success of hydrodynamic models arises from the prediction of collective flow of partons in the hot environment produced after the collision of heavy ions.

But the picture is not yet completed. Direct and elliptic flow are defined as the first and second harmonic coefficient v1 respectively v2 of an Fourier expansion of the azimuthal distribution of produced particles with respect to the reaction plane and carry information on the early stages of the collision. Although the hydro model is successful in explaining the elliptic flow at low transverse momentum it is not as successful in describing what is happening at high transverse momentum. Because of this, one of the secondary objectives will be the study of direct and elliptic flow in several nucleus-nucleus interactions (Au-Au at sqrt(sNN) =200 GeV, Pb-Pb at sqrt(sNN)=5.5 TeV). This will be done by looking at the behavior of direct and elliptic flow vs. transverse momentum, rapidity, centrality of collision also function of different species of particles.

Another secondary objective will be the development of several new numerical algorithms using parallel processing on GPU. The advantage of using high performance of GPU is that one can have the same computing power as a regular CPU cluster but at a lower cost and power consumption. The most important milestones in the development of these algorithms will be the identification of the most successful way in which a sequential CPU algorithm cam be parallelized and the benchmark tests using 3 different computing architectures: one based on classical CPU, one based on GPU and a hybrid one.