high performance computing on graphics processing units: hgpu.org

This paper presents a graphics processor based implementation of the Finite Difference Time Domain (FDTD), which uses a central finite differencing scheme for solving Maxwell’s equations for electromagnetics. FDTD simulations can be very computationally expensive and require thousands of CPU hours to solve on traditional general purpose processors. Modern Graphics Processing Units (GPUs) found in […]

A systematic approach to the comparison of the graphics processor (GPU) and reconfigurable logic is defined in terms of three throughput drivers. The approach is applied to five case study algorithms, characterized by their arithmetic complexity, memory access requirements, and data dependence, and two target devices: the nVidia GeForce 7900 GTX GPU and a Xilinx […]

GPU-based raycasting offers an interesting alternative to conventional slice-based volume rendering due to the inherent flexibility and the high quality of the generated images. Recent advances in graphics hardware allow for the ray traversal and volume sampling to be executed on a per-fragment level completely on the GPU leading to interactive framerates. In this work […]

We present a novel external sorting algorithm using graphics processors (GPUs) on large databases composed of billions of records and wide keys. Our algorithm uses the data parallelism within a GPU along with task parallelism by scheduling some of the memory-intensive and compute-intensive threads on the GPU. Our new sorting architecture provides multiple memory interfaces […]

We present a method that performs a rigid 2D/3D image registration efficiently on the Graphical Processing Unit (GPU). As one main contribution of this paper, we propose an efficient method for generating realistic DRRs that are visually similar to x-ray images. Therefore, we model some of the electronic post-processes of current x-ray C-arm-systems. As another […]

Due to the variety and importance of applications of treecodes and FMM, the combination of algorithmic acceleration with hardware acceleration can have tremendous impact. Alas, programming these algorithms efficiently is no piece of cake. In this contribution, we aim to present GPU kernels for treecode and FMM in, as much as possible, an uncomplicated, accessible […]

We present a new approach for dynamic LOD processing for geometry images (GIs) in the graphics processing unit (GPU). A GI mipmap is constructed from a scanned 3D model, then a mipmap selector map is created from the camera position’s information and the GI-mipmap. Using the mipmap selector map and the current camera position, some […]

Volumetric data rendering has become an important tool in various medical procedures as it allows the unbiased visualization of fine details of volumetric medical data (CT, MRI, fMRI). However, due to the large amount of computation involved, the rendering time increases dramatically as the size of the data set grows. This paper presents several acceleration […]

BACKGROUND:The Smith-Waterman algorithm is one of the most widely used tools for searching biological sequence databases due to its high sensitivity. Unfortunately, the Smith-Waterman algorithm is computationally demanding, which is further compounded by the exponential growth of sequence databases. The recent emergence of many-core architectures, and their associated programming interfaces, provides an opportunity to accelerate […]

Nowadays, the use of machine learning methods for visual object detection has become widespread. Those methods are robust. They require an important processing power and a high memory bandwidth which becomes a handicap for real-time applications. The recent evolution of commodity PC computer graphics boards (GPU) has the potential to accelerate those algorithms.

We propose an efficient implementation of the Octagon Abstract Domain (OAD) on Graphics Processing Unit (GPU) by exploiting stream processing to speed-up OAD computations. OAD is a relational numerical abstract domain which approximates invariants as conjunctions of constraints of the form

We study the performance of three parallel algorithms and their hybrid variants for solving tridiagonal linear systems on a GPU: cyclic reduction (CR), parallel cyclic reduction (PCR) and recursive doubling (RD). We develop an approach to measure, analyze, and optimize the performance of GPU programs in terms of memory access, computation, and control overhead. We […]