Fast reconstruction of 3D volumes from 2D CT projection data with GPUs

cited By 0International audienceMeso-F.E. modelling of 3D textile composites is a powerful tool, which can help determine mechanical properties and permeability of the reinforcements or composites. The quality of the meso F.E. analyses depends on the quality of the initial model. A direct method based on X-ray tomography imaging is introduced to determine finite element models based on the real geometry of 3D composite reinforcements. The method is particularly suitable regarding 3D textile reinforcements for which internal geometries are numerous and complex. An analysis of the image's texture is performed. A hyperelastic model developed for fibre bundles is used for the simulation of the deformation of the 3D reinforcement. © EDP Sciences, 2016

Surfing the optimization space of a multiple-GPU parallel implementation of a X-ray tomography reconstruction algorithm

The increasing popularity of massively parallel architectures based on accelerators have opened up the possibility of significantly improving the performance of X-ray computed tomography (CT) applications towards achieving real-time imaging. However, achieving this goal is a challenging process, as most CT applications have not been designed for exploiting the amount of parallelism existing in these architectures. In this paper we present the massively parallel implementation and optimization of Mangoose(++), a CT application for reconstructing 3D volumes from 20 images collected by scanners based on cone-beam geometry. The main contribution of this paper are the following. First, we develop a modular application design that allows to exploit the functional parallelism inside the application and to facilitate the parallelization of individual application phases. Second, we identify a set of optimizations that can be applied individually and in combination for optimally deploying the application on a massively parallel multi-GPU system. Third, we present a study of surfing the optimization space of the modularized application and demonstrate that a significant benefit can be obtained from employing the adequate combination of application optimizations. (C) 2014 Elsevier Inc. All rights reserved.This work was partially funded by the Spanish Ministry of Science and Technology under the grant TIN2010-16497, the AMIT project (CEN-20101014) from the CDTI-CENIT program, RECAVA-RETIC Network (RD07/0014/2009), projects TEC2010-21619-C04-01, TEC2011-28972-C02-01, and PI11/00616 from the Spanish Ministerio de Ciencia e Innovacion, ARTEMIS program (S2009/DPI-1802), from the Comunidad de Madrid

X-ray CT on the GPU

Nondestructive testing (NDT) is a collection of analysis techniques used by scientists and technologists as a way of analyzing the interior of an object without damaging the object. Since the analysis is done without damaging the object, NDT is an extremely valuable technique used in various industries for troubleshooting and research. CNDE has a long history of working with a variety of industrial sectors which include Aerospace (commercial and military aviation) and Defense Systems (ground vehicles and personnel protection); Energy (nuclear, wind, fossil); Infrastructure and Transportation (bridges, roadways, dams, levees); and Petro-Chemical (offshore, processing, fuel transport piping) to provide cost-effective tools and solutions. X-ray tomography is the procedure of using X-rays for generating tomographic slices of the required object. The object is bombarded with X-rays and the scanned image intensity values are collected on a detector. A significant drawback in X-ray tomography is the amount of data collected. It is generally huge in the order of gigabytes and hence the processing of data presents a big challenge. One way to speed up the processing of data is to run the programs on a cluster. CNDE uses a 64 node Beowulf cluster to do the reconstruction of an image. However with the advent of the GPU (Graphic Processing Unit) we have a far more cost efficient and time efficient hardware to run the reconstruction algorithm. The GPU can be fitted into a single PC, costs 10 times less than the cluster and also has a longer life time. This thesis has two major components to it. One of it is the desvelopment of new preprocessing and post processing techniques (includes filters, hot pixel removal etc.) to improve the quality of the input data and the other is the implementation of these techniques as well as the reconstruction program on the GPU using CUDA. Speedup on the GPU is not just a matter of porting the developed algorithms in parallel onto the hardware like in a cluster. GPU architecture is extremely complex and involves the usage of many different types of memory each with its own advantages and disadvantages and also many other optimization techniques for accessing and processing the data. These new techniques as well as the introduction of GPU are a significant addition to X-ray program here at CNDE