high performance computing on graphics processing units: hgpu.org

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 […]

We present performance results for dense linear algebra using recent NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs up to 60% faster than the vendor’s implementation and approaches the peak of hardware capabilities. Our LU, QR and Cholesky factorizations achieve up to 80–90% of the peak GEMM rate. Our parallel LU running on two GPUs […]

Modern graphics processing units (GPUs) are an implementation of parallel stream processors. In recent years, there have been a few studies on mapping ray tracing to the GPU. Since graphics processors are not designed to process complex data structures, it is crucial to explore data structures and algorithms for efficient stream processing. In particular ray […]

This paper describes novel implementations of the KLT feature tracking and SIFT feature extraction algorithms that run on the graphics processing unit (GPU) and is suitable for video analysis in real-time vision systems. While significant acceleration over standard CPU implementations is obtained by exploiting parallelism provided by modern programmable graphics hardware, the CPU is freed […]

We present Glimmer, a new multilevel visualization algorithm for multidimen-sional scaling designed to exploit modern graphics processing unit (GPU) hard-ware. We also present GPU-SF, a parallel, force-based subsystem used by Glim-mer. Glimmer organizes input into a hierarchy of levels and recursively applies GPU-SF to combine and refine the levels. The multilevel nature of the algorithm […]