Simulation and modelling of gravitational microlensing events using graphical processing units : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Computer Science at Massey University, Albany (Auckland), New Zealand

Abstract

This thesis presents the results of a study into the use of graphical processing units (GPUs) in the simulation and modelling of gravitational microlensing. Two simulation approaches were investigated: magni cation maps and the use of a dynamic engine for directly simulating gravitational microlensing light curves. It was found that the GPUs are able to speed up the generation of magni cation maps dramatically. Very high performance in light curve extraction from magni cation maps using GPUs is also achieved. Furthermore, the use of texture memory speeds up the extraction of light curves in a further 75% improvement in performance. They provide a speed up of over a 100 faster than CPUs in light curve simulations with nite source e ects. The dynamic engine approach use a hybrid computation method with both CPUs and GPUs to simulate light curves for complex microlensing events. It allows us to model microlensing events with orbital motion e ects, which are usually done on a cluster computer, on just a desktop computer with GPUs. Modelling strategies and optimization techniques are developed and applied to model di erent types of microlensing events. GPU architectures show great promise for tackling the computationally expensive task of numerical modelling of microlensing events. With the modelling strategies developed here, microlensing modelling can be performed on a desktop computer at only a fraction of the cost of a cluster computer. The approach in this thesis provides a very cost-e ective solution for the microlensing modelling challenge.