high performance computing on graphics processing units: hgpu.org

Graphics Processing Units (GPUs) have been recently used as coprocessors capable of performing tasks that are not necessarily related to graphics processing in order to optimize computing resources. The use of GPUs has being extended to a wide variety of intensive-computation applications among which audio processing is included. However data transactions between the CPU and […]

This document explains some common methods of visualizing complex functions and how to implement them on the GPU. Using the fragment shader, we visualize complex functions in the complex plane with the domain coloring method. Then using the vertex shader, we visualize complex functions defined on a unit sphere like spherical harmonics. Finally, we redesign […]

GPU-based clusters are an attractive option for parallel volume rendering. One of the key issues in parallel volume rendering is load balancing, keeping a balanced workload per node is essential for improving performance. A good number of dynamic load balancing schemes have been proposed throughout the years. However, most of these approaches require runtime dynamic […]

High Performance Computing (HPC) systems are nowadays more and more heterogeneous. Different processor types can be found on a single node including accelerators such as Graphics Processing Units (GPUs). To cope with the challenge of programming such complex systems, this work presents a domain-specific approach to automatically generate code tailored to different processor types. Low-level […]

The use of general-purpose computing on a GPU is an effective way to accelerate the FDTD method. This paper introduces flexibility to the theoretically best available approach. It examines the performance on both Tesla- and Fermi-architecture GPUs, and identifies the best way to determine the GPU parameters for the proposed method.

We investigate multi-level parallelism on GPU clusters with MPI-CUDA and hybrid MPI-OpenMP-CUDA parallel implementations, in which all computations are done on the GPU using CUDA. We explore efficiency and scalability of incompressible flow computations using up to 256 GPUs on a problem with approximately 17.2 billion cells. Our work addresses some of the unique issues […]

We investigate efficient algorithmic realisations for robust deconvolution of grey-value images with known space-invariant point-spread function, with emphasis on 1D motion blur scenarios. The goal is to make deconvolution suitable as preprocessing step in automated image processing environments with tight time constraints. Candidate deconvolution methods are selected for their restoration quality, robustness and efficiency. Evaluation […]

Recently scientific simulation application take advantage of modern accelerator technology more and more. For in-situ visualization techniques especially in this case scalability will become an issue. In this work we present a scalability evaluation for a hybrid parallelized streamline extraction algorithm.

The Computing Frontiers conference focuses on a wide spectrum of advanced methodologies, technologies and radically new solutions relevant to the development of the whole spectrum of computing over the next few decades. The goals of the meeting range over from embedded to high-performance computing. We seek contributions on novel algorithms, computing paradigms, computational models, application […]

We present CUDACLAW, a data-parallel solution framework for 2D and 3D hyperbolic partial differential equation (PDE) systems. CUDACLAW is a finite volume method based on time adaptive point-wise Riemann problem solvers, and can handle linear and nonlinear problems. The framework is tailored for the GPU architecture, optimized to take advantage of the powerful computational potential, […]

This paper describes FFT Cooley-Tukey algorithm implementation used in MRI image reconstruction on a revolutionary parallel computing machine, Connex Array. By taking advantage of it’s vectorial structure and processing manner, MRI image reconstruction was much faster than most of usual MRI commercial scanners. Results are remarkable. Our proposal in this paper is the use of […]

Representing uncertainty when creating visualizations is becoming more indispensable to understand and analyze scientific data. Uncertainty may come from different sources, such as, ensembles of experiments or unavoidable information loss when performing data reduction. One natural model to represent uncertainty is to assume that each position in space instead of a single value may take […]