A gpu-based implementation of the mrf algorithm in itk package

The analysis of medical image, in particular Magnetic Resonance Imaging (MRI), is a very useful tool to help the neurologists on the diagnosis. One of the stages on the analysis of MRI is given by a classification based on the Markov Random Fields (MRF) method. It is possible to find in the literature several packages to carry out this analysis, and of course, the classification tasks. One of them is the Insight Segmentation and Registration Toolkit (ITK). The analysis of MRI is an expensive computational task. In order to reduce the execution time spent on the analysis of MRI, parallelism techniques can be used. Currently, Graphics Processing Units (GPUs) are becoming a good choice to reduce the execution time of several applications at a low cost. In this paper, the authors present a GPU-based classification using MRF from the sequential implementation that appears in the ITK package. The experimental results show a spectacular execution time reduction being the GPU-based implementation up to 118 times faster than the sequential implementation included in the ITK package. Moreover, this result is also observed by reducing the total power consumption in a significant amount. Keywords: Magnetic resonance imaging ? Markov random fields ? Insight toolkit ? Graphics processing unit

Similarity search implementations for multi-core and many-core processors

Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications, when processing large volumes of data, query response time can be high enough. In this case, it is necessary to apply mechanisms in order to significantly reduce the average query response time. In this sense, the parallelization of the metric structures processing is an interesting field of research. Modern multi-core and many-core systems offer a very impressive cost/performance ratio. In this paper two new parallel implementations for range queries on Spaghettis data structures have been carried out: one of them on a many-core processor and the other one on a multi-core processor. Both implementations have been compared in terms of execution time and speedup

A gpu-based implementation for range queries on spaghettis data structure

Similarity search in a large collection of stored objects in a metric database has become a most interesting problem. The Spaghettis is an efficient metric data structure to index metric spaces. However, for real applications processing large volumes of generated data, query response times can be high enough. In these cases, it is necessary to apply mechanisms in order to significantly reduce the average query time. In this sense, the parallelization of metric structures is an interesting field of research. The recent appearance of GPUs for general purpose computing platforms offers powerful parallel processing capabilities. In this paper we propose a GPU-based implementation for Spaghettis metric structure. Firstly, we have adapted Spaghettis structure to GPU-based platform. Afterwards, we have compared both sequential and GPU-based implementation to analyse the performance, showing significant improvements in terms of time reduction, obtaining values of speed-up close to 10. Keywords: Databases ? similarity search ? metric spaces ? algorithms ? data structures ? parallel processing ? GPU ? CUD