Miguel Lastra

Simulation of shallow-water systems using graphics processing units

This paper addresses the speedup of the numerical solution of shallow-water systems in 2D domains by using modern graphics processing units (GPUs). A first order well-balanced finite volume numerical scheme for 2D shallow-water systems is considered. The potential data parallelism of this method is identified and the scheme is efficiently implemented on GPUs for one-layer shallow-water systems. Numerical experiments performed on several GPUs show the high efficiency of the GPU solver in comparison with a highly optimized implementation of a CPU solver.

A High Performance Fingerprint Matching System for Large Databases Based on GPU

Fingerprints are the biometric features most used for identification. They can be characterized through some particular elements called minutiae. The identification of a given fingerprint requires the matching of its minutiae against the minutiae of other fingerprints. Hence, fingerprint matching is a key process. The efficiency of current matching algorithms does not allow their use in large fingerprint databases; to apply them, a breakthrough in running performance is necessary. Nowadays, the minutia cylinder-code (MCC) is the best performing algorithm in terms of accuracy. However, a weak point of this algorithm is its computational requirements. In this paper, we present a GPU fingerprint matching system based on MCC. The many-core computing framework provided by CUDA on NVIDIA Tesla and GeForce hardware platforms offers an opportunity to enhance fingerprint matching. Through a thorough and careful data structure, computation and memory transfer design, we have developed a system that keeps its accuracy and reaches a speed-up up to 100.8× compared with a reference sequential CPU implementation. A rigorous empirical study over captured and synthetic fingerprint databases shows the efficiency of our proposal. These results open up a whole new field of possibilities for reliable real time fingerprint identification in large databases.

Fast fingerprint identification using GPUs

Fingerprints are widely used in a variety of biometric identification systems. The fingerprint matching process is a processing step whose computational requirements limit the size of the fingerprint database that can be dealt with.Fingerprint matching algorithms based on minutiae are one of the most relevant families of biometric identification techniques. The scalability of these models is determined not only by the number of fingerprints but also the number of minutiae per fingerprint. Therefore, processing millions of fingerprints per second requires being able to process hundreds of millions of minutiae per second.In this paper we present a new design of the minutiae based fingerprint matching algorithm presented by Jiang et al. specifically created for GPU based massively parallel architectures. The parallel design allows speed-up ratios of up to 15 with one GPU compared to multi-threaded CPU implementations, and up to 54 using several GPUs in parallel and fingerprint processing rates of between 300,000 and 1,500,000 fingerprints per second.

Interactive global illumination for quasi-static scenes

This paper describes an approach to obtain interactive recalculation of global illumination for scenes with small moving objects (with respect to the complete geometry), on a standard PC, using density estimation techniques

SMOTE-GPU: Big Data preprocessing on commodity hardware for imbalanced classification

Nowadays, it is usual to work with large amounts of data since our capacity of collecting and storing information has increased significantly. The extraction of knowledge from these scenarios is commonly known as “Big Data,” and it is performed on large clusters with MapReduce platforms. Imbalanced classification poses a problem both in traditional and Big Data learning scenarios. Data sampling is one of the ways that allows to improve the performance on imbalanced problems. A commodity hardware-based method for Big Data problems can offload these computations from the expensive and highly demanded hardware that MapReduce platforms require. The characteristics of some sampling methods make them suitable to be adapted to commodity hardware, taking advantage of the parallel computation capabilities of graphics processing units. SMOTE is one of the most popular oversampling methods which is based on the nearest neighbor rule. The proposed SMOTE-GPU efficiently handles large datasets (several millions of instances) on a wide variety of commodity hardware, including a laptop computer.

An Importance Sampling Method for Arbitrary BRDFs

This paper introduces a new BRDF sampling method with reduced variance, which is based on a hierarchical adaptive PDF. This PDF also is based on rejection sampling with a bounded average number of trials, even in regions where the BRDF exhibits high variations. Our algorithm works in an appropiate way with both physical, analytical and measured reflectance models. Reflected directions are sampled by using importance sampling of the BRDF times the cosine term. This fact improves computation of reflected radiance when Monte-Carlo integration is used in Global Illumination.

Efficient multilayer shallow-water simulation system based on GPUs

Abstract The computational simulation of shallow stratified fluids is a very active research topic because these types of systems are very common in a variety of natural environments. The simulation of such systems can be modeled using multilayer shallow-water equations but do impose important computational requirements, especially when applied to large domains. General Purpose Computing on Graphics Processing Units (GPGPU) has become a vivid research field due to the arrival of massively parallel hardware platforms (based on graphics cards) and adequate programming frameworks which have allowed important speed-up factors with respect to not only sequential but also parallel CPU based simulation systems. In this work we present simulation of shallow stratified fluids with an arbitrary number of layers using GPUs. The designed system does fully adapt to the many-core architecture of modern GPUs and several experiments have been carried out to illustrate its scalability and behavior on different GPU models. We propose a new multilayer computational scheme for an underlying 2D mathematical model. This scheme is capable of handling an arbitrary number of layers. The system adds no overhead when used for two-layer scenarios, compared to an existing 2D system specifically designed for just two layers. Our proposal is aimed at creating a GPU-based computational scheme suitable for the simulation of multilayer large-scale real-world scenarios.

A High Order Finite Volume Numerical Scheme for Shallow Water System: An Efficient Implementation on GPUs

In this work we present a high order finite volume numerical scheme for solving the one layer shallow-water system. The numerical solution of this model is useful for several applications related to geophysical flows, and they impose a great demand of computing power. As a consequence, extremely efficient high performance solvers are required. In this work we perform a GPU implementation of the proposed numerical scheme and some computations are made to test the performance of the implementation.

Un algoritmo de muestreo exacto para BRDFs arbitrarias

Resumen Este trabajo presenta un algoritmo para el muestreo eficiente y exacto de BRDF genericas, esto es, apto paracualquiera de los modelos de BRDFs analiticos o adquiridos de la literatura de informatica grafica. Nuestroobjetivo principal consiste en proporcionar una funcion de probabilidad utilizable en algoritmos de iluminacionglobal basados en metodos de Monte-Carlo, que permita un muestreo por importancias proporcional al productode la funcion BRDF y un termino coseno. Mediante la subdivision adaptativa de la BRDF en el disco unidad,obtenemos una estructura jerarquica o quadtree que nos permite aplicar un muestreo por rechazo optimizado enlos nodos. El numero medio de intentos en el muestreo esta acotado y es parametro de la estructura utilizada. Elmetodo se aplica al muestreo de cualquier modelo de BRDF sin necesidad de guia por parte del usuario. CategoriesandSubjectDescriptors (accordingtoACMCCS) : I.3.7[ComputerGraphics]:Three-DimensionalGrap-hics and Realism. Color, shading, shadowing, and texture

Estimación de Densidades usando GPUs

Este articulo presenta un metodo de estimacion de densidades basado en rayos que ha sido implementado completamente en la GPU. La estimacion de densidades basada en rayos reduce el sesgo de los enfoques clasicos basados en fotones aunque tiene un costo computacional mas alto. Proponemos algoritmos y estructuras de datos para la implementacion en GPUs de la estimacion de densidades basada en rayos y mostramos que la tecnica propuesta da hasta un orden de magnitud de aceleracion comparada con su variante CPU. La aceleracion obtenida se incrementa al aumentar el numero de rayos y por tanto el metodo es muy util en aplicaciones que requieran renderizacion de alta calidad.