high performance computing on graphics processing units: hgpu.org

Knowledge extraction from structured data aims for identifying valid, novel, potentially useful, and ultimately understandable patterns in the data. The core step of this process is the application of a data mining algorithm in order to produce an enumeration of particular patterns and relationships in large databases. Clustering is one of the major data mining […]

This paper investigates the integration of feature extraction, object recognition and 3D reconstruction by stereo vision into a unified framework. In doing so, stereo vision can be made more robust by applying feature extraction results to the stereo matching process, and object recognition can be extended through the integration of depth information as another feature […]

This paper describes the use of GPU clusters to accelerate backpropagation for Synthetic Aperture Sonar (SAS) systems. We extended a GPU-based implementation of backpropagation to support clusters of GPU-enhanced nodes. The GPU accelerated implementation formed a 3,936 x 3,936 SAS image from 60s of sonar data in under 12s using a single GTX480, and under […]

Bit error rate simulations are used to estimate the error probability for a communications channel. Typically, many millions of trials must be run in order to have a reasonable estimate of the error probability. The Communications System Toolbox in MATLAB contains tools that allow the user to construct these simulations, but executing the required trials […]

BACKGROUND: With the maturation of next-generation DNA sequencing (NGS) technologies, the throughput of DNA sequencing reads has soared to over 600 gigabases from a single instrument run. General purpose computing on graphics processing units (GPGPU), extracts the computing power from hundreds of parallel stream processors within graphics processing cores and provides a cost-effective and energy […]

Image fusion is an imaging technique to visualize information from multiple imaging sources by one single image, which is widely used in remote sensing, medical imaging etc. In this work, we study two variational approaches to image fusion which are closely related to the standard TV-L2 and TV-L1 image approximation methods. We investigate their convex […]

Nowadays, computers embed many CPUs and at least one GPU. Workstations can host several GPU cards, which are well suited for scientific and engineering computations. Such computers are linked through high bandwidth networks to compose clusters for HPC. These machines provide highly parallel multicore architectures while being cost-effective. Moreover, they significantly reduce dissipated power, and […]

In this thesis several steps towards the optimization and acceleration of a Monte Carlo code for the simulation of light propagation in particulate scattering media have been taken. This is performed by parallelizing a Monte Carlo code originally written by E. Berrocal [1] and running the simulation on a modern computer graphic card; a process […]

This paper explores the early implementation of highperformance routines for the solution of multiple large Hermitian eigenvector and eigenvalue systems on a Graphics Processing Unit (GPU). We report a performance increase of up to two orders of magnitude over the original EISPACK routines with a NVIDIA Tesla C2050 GPU, potentially allowing an order of magnitude […]

This paper proposes a parallel solver for the nonlinear systems in Bernstein form based on subdivision and the Newton-Raphson method, where the Kantorovich theorem is employed to identify the existence of a unique root and guarantee the convergence of the Newton-Raphson iterations. Since the Kantorovich theorem accommodates a singular Jacobian at the root, the proposed […]

In recent years, the multi-cores and General-Purpose GPU (GPGPU) architectures have become general platforms for various of parallel applications, with lots of parallel algorithms being proposed for this interesting persperctive. In this report, we study and develop a particular kind of artificial neural network (ANN), in hopfield model, to solve some optimization problems, since it […]

Low-latency solutions for syntactic parsing are needed if parsing is to become an integral part of user-facing natural language applications. Unfortunately, most state-of-the-art constituency parsers employ large probabilistic context-free grammars for disambiguation, which renders them impractical for real-time use. Meanwhile, Graphics Processor Units (GPUs) have become widely available, offering the opportunity to alleviate this bottleneck […]