Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Exploring the multiple-GPU design space
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
GPU-based 3D cone-beam CT image reconstruction for large data volume
Journal of Biomedical Imaging
CUDA optimization strategies for compute- and memory-bound neuroimaging algorithms
Computer Methods and Programs in Biomedicine
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Most small-animal X-ray computed tomography (CT) scanners are based on cone-beam geometry with a flat-panel detector orbiting in a circular trajectory. Image reconstruction in these systems is usually performed by approximate methods based on the algorithm proposed by Feldkamp, Davis and Kress (FDK). Currently there is a strong need to speedup the reconstruction of X-Ray CT data in order to extend its clinical applications. The evolution of the semiconductor detector panels has resulted in an increase of detector elements density, which produces a higher amount of data to process. This work focuses on future high-resolution studies (density up to 4096 pixeles), in which multiple level of parallelism will be needed in the reconstruction. In addition, this paper addresses the future challenges of processing high-resolution images in many-core and distributed architectures. In our evaluation section we demonstrate that our solution is 17% faster than recent related works.