Abstract
GPU clusters nowadays combine enormous computational resources of GPUs and multi-core CPUs. This paper describes a distributed program architecture that leverages all resources of such a cluster to incrementally reconstruct, segment and render 3D cone beam computer tomography (CT) data with the objective to provide the user with results as quickly as possible at an early stage of the overall computation. As the reconstruction of high-resolution data sets requires a significant amount of time, our system first creates a low-resolution preview volume on the head node of the cluster, which is then incrementally supplemented by high-resolution blocks from the other cluster nodes using our multi-resolution renderer. It is further used for graphically choosing reconstruction priority and render modes of sub-volume blocks. The cluster nodes use their GPUs to reconstruct and render sub-volume blocks, while their multi-core CPUs are used to segment already available blocks.
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Frey, S., Müller, C., Strengert, M., Ertl, T. (2009). Concurrent CT Reconstruction and Visual Analysis Using Hybrid Multi-resolution Raycasting in a Cluster Environment. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2009. Lecture Notes in Computer Science, vol 5875. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10331-5_34
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DOI: https://doi.org/10.1007/978-3-642-10331-5_34
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-10330-8
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