high performance computing on graphics processing units: hgpu.org

This paper studies the usage of the GPU as a collection of groups of related processing units, where each group communicates in some way to complete a computation efficiently and effectively. In particular, we use the GPU to perform jump flooding to pass information among groups of processing units in the design of two simple […]

IP address lookup is a challenging problem due to the increasing routing table size, and higher line rate. This paper investigates a new way to build an efficient IP lookup scheme using graphics processor units(GPU). Our contribution here is to design a basic architecture for high-performance IP lookup engine with GPU, and to develop efficient […]

SBART was developed in early 1990’s as one of the derivatives from Artificial Evolution by Karl Sims. It has a functionality to create a movie from a bred image through post-processing. The innovation of graphics processing unit (GPU) in these years improved the calculation performance to be fast enough to realize breeding animations in realtime […]

Exploiting the graphics processing unit (GPU) is useful to obtain higher performance with a less number of host machines in grid systems. One problem in GPU-accelerated grid systems is the lack of efficient multitasking mechanisms. In this paper, we propose a cooperative multitasking method capable of simultaneous execution of a graphics application and a CUDA-based […]

A new three-dimensional Navier-Stokes solver for flows in turbomachines has been developed. The new solver is based on the latest version of the Denton codes but has been implemented to run on graphics processing units (GPUs) instead of the traditional central processing unit. The change in processor enables an order-of-magnitude reduction in run-time due to […]

The use of graphics processing units (GPUs) in simulation applications offers a significant speed gain as compared to computations on central processing units (CPUs). Many simulation methods require a large number of independent random variables generated at each step. We present two approaches for implementation of random number generators (RNGs) on a GPU. In the […]

This paper is concerned with a multiresolution representation for maximum intensity projection (MIP) volume rendering based on morphological pyramids which allows progressive refinement. We consider two algorithms for progressive rendering from the morphological pyramid: one which projects detail coefficients level by level, and a second one, called streaming MIP, which resorts the detail coefficients of […]

Computational neuroscience is being revolutionized with the advent of multi-electrode arrays that provide real-time, dynamic perspectives into brain function. Mining neuronal spike streams from these chips is critical to understand the firing patterns of neurons and gain insight into the underlying cellular activity. To address this need, we present a solution that uses a massively […]

Finite volume numerical methods have been widely studied, implemented and parallelized on multiprocessor systems or on clusters. Modern graphics processing units (GPU) provide architectures and new programing models that enable to harness their large processing power and to design computational fluid dynamics simulations at both high performance and low cost. We report on solving the […]

Multi-paradigm, multi-threaded and multi-core computing devices available today provide several orders of magnitude performance improvement over mainstream microprocessors. These devices include the STI Cell Broadband Engine, Graphical Processing Units (GPU) and the Cray massively-multithreaded processorsavailable in desktop computing systems as well as proposed for supercomputing platforms. The main challenge in utilizing these powerful devices is […]

We present a real-time algorithm for rendering translucent objects of arbitrary shapes. We approximate the scattering of light inside the objects using the diffusion equation, which we solve on-the-fly using the GPU. Our algorithm is general enough to handle arbitrary geometry, heterogeneous materials, deformable objects and modifications of lighting, all in real-time. In a pre-processing […]

Training convolutional neural networks (CNNs) on large sets of high-resolution images is too computationally intense to be performed on commodity CPUs. Such architectures, however, achieve state-of-the-art results on low-resolution machine vision tasks such as recognition of handwritten characters. We have adapted the inherent multi-level parallelism of CNNs for Nvidia’s CUDA GPU architecture to accelerate the […]