Implementación de un algoritmo para eliminación de ruido impulsivo en imágenes y análisis comparativo de tiempos de respuesta bajo arquitectura GPU y CPU

El presente trabajo, tuvo como propósito general determinar en procesamiento digital de imágenes, tiempos de respuesta al implementar un algoritmo en diferentes arquitecturas (CPU Y GPU), utilizando interpolación a través de funciones de base radial. Para cumplir con este objetivo, se parte de una investigación previa sobre eliminación de ruido impulsivo en imágenes, a partir de allí se plantea en base a una solución en pseudocódigo un algoritmo apropiado para la arquitectura CPU y arquitectura GPU. Sobre la arquitectura GPU se detallan las particularidades identificadas al momento de la implementación (utilizando tecnología CUDA); restricciones sobre de la plataforma y alternativas de implementación. Consecuente a la implementación, se plantea un conjunto de pruebas con imágenes, las cuales tienen ruido del tipo sal y pimienta y de diferentes dimensiones (ancho, alto), estas pruebas buscan determinar los tiempos de respuesta en cuanto a eliminación de ruido por parte del algoritmo implementado en las dos arquitecturas. Las pruebas en tiempos de respuesta generan resultados que son analizados, principalmente evidenciando una correcta eliminación de los pixeles ruidosos (que alcanzan los 55 mil en una sola imagen) en el caso de las dos arquitecturas, y adicionalmente el tiempo de respuesta claramente bajo (mayor rapidez en procesamiento) en la arquitectura CPU con respecto a la arquitectura GPU.The research had as a primary objective determine in the field of digital processing images, response times in different architectures (CPU and GPU), using interpolation trough the basis radial functions. To achieve this objective, it start with a previous research about impulsive noise elimination in images, from there its proposed an appropiate algorithm based in a pseudocode solution, to the CPU architecture and GPU architecture. For the GPU architecture is detailed several particularities identified at the implementation phase (using CUDA technology); platform restrictions and implementation work-arounds. As a result of the implementation phase, its proposed a test images, which have noise salt and pepper with different dimensions (width, height), these tests seek to determine response times about noise elimination by the algorithm implemented on the two architectures. In testing the results were analyzed, mainly showing a correct noise elimination in images (reaching up to 55 thousand noisy pixels in a image) at both CPU and GPU architectures, additionally a clearly lower response time (faster in processing) on the CPU Architecture regarding to the GPU Architecture

High performance content-based matching using GPUs

Matching incoming event notifications against received subscriptions is a fundamental part of every publish-subscribe infrastructure. In the case of content-based systems this is a fairly complex and time consuming task, whose performance impacts that of the entire system. In the past, several algorithms have been proposed for efficient content-based event matching. While they differ in most aspects, they have in common the fact of being conceived to run on conventional, sequential hardware. On the other hand, modern Graphical Processing Units (GPUs) offer off-the-shelf, highly parallel hardware, at a reasonable cost. Unfortunately, GPUs introduce a totally new model of computation, which re- quires algorithms to be fully redesigned. In this paper, we describe a new content-based matching algorithm designed to run efficiently on CUDA, a widespread architecture for general purpose programming on GPUs. A detailed com- parison with SFF, the matching algorithm of Siena, known for its efficiency, demonstrates how the use of GPUs can bring impressive speedups in content-based matching. At the same time, this analysis demonstrates the peculiar aspects of CUDA programming that mostly impact performance