Adapting algorithms to massively parallel hardware

In the recent years, the trend in computing has shifted from delivering processors with faster clock speeds to increasing the number of cores per processor. This marks a paradigm shift towards parallel programming in which applications are programmed to exploit the power provided by multi-cores. Usually there is gain in terms of the time-to-solution and the memory footprint. Specifically, this trend has sparked an interest towards massively parallel systems that can provide a large number of processors, and possibly computing nodes, as in the GPUs and MPPAs (Massively Parallel Processor Arrays). In this project, the focus was on two distinct computing problems: k-d tree searches and track seeding cellular automata. The goal was to adapt the algorithms to parallel systems and evaluate their performance in different cases