high performance computing on graphics processing units: hgpu.org

This article gives an introduction to GPU usage for High Performance Computing. After setting the context, we will describe the hardware and the programming languages currently available to programmers. From these explanations we will touch on the implications of these technologies for simulation codes and try to give trends for the future.

We develop a hybrid multiGPUs and CPUs version of an algorithm to model seismic wave propagation based on the spectral-element method in the case of models of the Earth containing both fluid and solid layers. Thanks to the overlapping of communications between processing nodes on the computer with calculation by means of non-blocking message passing, […]

This article is a review of the work that we are carrying out to efficiently simulate shallow water flows. In this paper, we focus on the efficient implementation of path-conservative Roe type high-order finite volume schemes to simulate shallow flows that are supposed to be governed by the one-layer or two-layer shallow water systems, formulated […]

We describe a hybrid Lyapunov solver based on the matrix sign function, where the intensive parts of the computation are accelerated using a graphics processor (GPU) while executing the remaining operations on a general-purpose multi-core processor (CPU). The initial stage of the iteration operates in single-precision arithmetic, returning a low-rank factor of an approximate solution. […]

While many-core accelerator architectures, such as today’s Graphics Processing Units (GPUs), offer orders of magnitude more raw computing power than contemporary CPUs, their massive parallelism often produces complex dynamic behaviors even with the simplest applications. Using a fixed set of hardware or simulator performance counters to quantify behavior over a large interval of time such […]

We describe an architecture for massive simulation of a distributed behavioral model using graphics hardware. By leveraging on the recent programmable capabilities of GPUs we implemented the model capable of managing a large aggregate motion of birds in a virtual environment that can avoid both static and dynamic obstacles. We demonstrate the effectiveness of our […]

The ability to model objects composed of multiple materials has become increasingly more demanded in scientific applications. The visualization of a discrete multi-material volume often suffers from voxelization of the boundary between materials. We propose a contouring method that can be efficiently implemented on the GPU to reduce the artifacts and jaggedness along the material […]

We present Barra, a simulator of Graphics Processing Units (GPU) tuned for general purpose processing (GPGPU). It is based on the UNISIM framework and it simulates the native instruction set of the Tesla architecture at the functional level. The inputs are CUDA executables produced by NVIDIA tools. No alterations are needed to perform simulations. As […]

As is typical in evolutionary algorithms, fitness evaluation in GP takes the majority of the computational effort. In this paper we demonstrate the use of the Graphics Processing Unit (GPU) to accelerate the evaluation of individuals. We show that for both binary and floating point based data types, it is possible to get speed increases […]

SIFT is a widely-used algorithm that extracts features from images; using it to extract information from hundreds of terabytes of aerial and satellite photographs requires parallelization in order to be feasible. We explore accelerating an existing serial SIFT implementation with OpenMP parallelization and GPU execution.

A new approach to interactive segmentation based on random walks was recently introduced that shows promise for allowing physicians more flexibility to segment arbitrary objects in an image. This report has two goals: To introduce a novel computational method for applying the random walker algorithm in 2D/3D using the Graphics Processing Unit (GPU) and to […]

In this poster, we present how our previously published method of computing continuous 2D scatterplots can be performed with hardware acceleration on a GPU. By doing this, we exploit the parallel processing ability of current graphics hardware to improve the performance of continuous scatterplots by up to two orders of magnitude. For medium-sized tetrahedral data […]