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The ATLAS Experiment is one of the four large detectors located at the Large Hadron Collider (LHC) at CERN in Geneva, Switzerland. In summer 2008, ATLAS is expected to start collecting data from proton-proton collisions at 14 TeV centre-of-mass energy. In the centre of the detector, the reconstruction of charged particle tracks is performed by silicon and drift tube based sub-detectors. In order to achieve the ATLAS physics goals the resolutions of the measured track parameters should not be degraded by more than 20% due to misalignment. Thus, the relative positions of the silicon detector elements have to be known to an accuracy of about 10 micrometers in the coordinate with the best measurement precision. This requirement can be achieved by track based alignment algorithms combined with measurements from hardware based alignment techniques. A robust track based alignment method based on track residual and overlap residual optimisation has been developed and implemented into the ATLAS offline software framework. The alignment algorithm has been used to align a test beam setup and also part of the final ATLAS detector using cosmic ray muons. Several simulation studies showed that the algorithm will be able to align the full detector with collision data. In addition to detector misalignments, limitations in the knowledge of the proton structure are going to affect physics discoveries at the LHC. Therefore, parton distribution uncertainties in high-mass Drell-Yan processes have been determined. This study includes the analysis of the forward-backward asymmetry. It has been performed on the level of next-to-leading order in both, Monte Carlo simulation using k-factors and parton distribution functions. This analysis is crucial for new physics searches with the ATLAS detector.

Ray tracing is an attractive rendering option because it can produce high quality images that faithfully represent physically-based phenomena. Its embarrassingly parallel nature makes it a natural choice for rendering large-scale scene data, especially on machines that lack specialized graphics hardware. Unfortunately, the traditional recursive ray tracing algorithm is exceptionally memory inefficient for large scenes, especially when using a shading model that generates incoherent secondary rays. Queueing ray tracers have been shown to control scene state under these conditions, but they allow ray state to grow unchecked. Instead, we propose a ray tracing framework that controls both ray and scene state by dynamically adjusting the rendering algorithm to meet memory requirements. Our dynamic scheduling framework generalizes recursive and queueing tracers into a spectrum of ray schedules that can vary the active amount of ray and scene data in order to match the characteristics of the hardware’s memory system. This dissertation describes our dynamic ray scheduling approach that operates on memory-bound work units, which consist of both rays and scene data. It builds these work units by tracing rays iteratively and queueing them in spatial regions along with nearby data. By dynamically scheduling these work units, our approach can reduce data loads and improve total runtime by 2x to 30x . In addition, we show that our algorithm scales across more than 1000 distributed processors, which is an order of magnitude larger than previously published results. Our approach enables the use of complex lighting models on large data, particularly scientific data, which improves image quality and thereby improves the scientific insights possible. / text

Optimal large eddy simulation (OLES) is an approach to LES sub-grid modeling that requires multi-point correlation data as input. Until now, this has been obtained by analyzing DNS statistics. In the finite-volume OLES formulation studied here, under the assumption of small-scale homogeneity and isotropy, these correlations can be theoretically determined from Kolmogorov inertial-range theory, small-scale isotropy, along with the quasi-normal approximation. These models are expressed as generalized quadratic and linear finite volume operators that represent the convective momentum flux. These finite volume operators have been analyzed to determine their characteristics as numerical approximation operators and as models of small-scale effects. In addition, the dependence of the model operators on the anisotropy of the grid and on the size of the stencils is analyzed to develop idealized general operators that can be used on general grids. The finite volume turbulence operators developed here will be applicable in a wide range of LES problems. / text

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