high performance computing on graphics processing units: hgpu.org

We present a scheme for view-dependent level-of-detail control that is implemented entirely on programmable graphics hardware. Our scheme selectively refines and coarsens an arbitrary triangle mesh at the granularity of individual vertices, to create meshes that are highly adapted to dynamic view parameters. Such fine-grain control has previously been demonstrated using sequential CPU algorithms. However, […]

Magnetohydrodynamics (MHD) simulations based on the ideal MHD equations have become a powerful tool for modeling phenomena in a wide range of applications including laboratory, astrophysical, and space plasmas. In general, high-resolution methods for solving the ideal MHD equations are computationally expensive and Beowulf clusters or even supercomputers are often used to run the codes […]

We present Sapporo, a library for performing high-precision gravitational N-body simulations on NVIDIA Graphical Processing Units (GPUs). Our library mimics the GRAPE-6 library, and N-body codes currently running on GRAPE-6 can switch to Sapporo by a simple relinking of the library. The precision of our library is comparable to that of GRAPE-6, even though internally […]

This paper presents an efficient graphics hardware-based method to segment and visualize level-set surfaces as interactive rates. Our method is composed of memory manager, level-set solver, and volume renderer. The memory manager which performs in CPU generates page table, inverse page table and available page stack as well as process the activation and inactivation of […]

We present PacketShader, a high-performance software router framework for general packet processing with Graphics Processing Unit (GPU) acceleration. PacketShader exploits the massively-parallel processing power of GPU to address the CPU bottleneck in current software routers. Combined with our high-performance packet I/O engine, PacketShader outperforms existing software routers by more than a factor of four, forwarding […]

Modern graphics processing units (GPUs) have recently become fully programmable. Thus a powerful and cost-efficient new computational platform for surgical simulations has emerged. A broad selection of publications has shown that scientific computations obtain a significant speedup if ported from the CPU to the GPU. To take advantage of the GPU however, one must understand […]

This paper presents a new algorithm for force directed graph layout on the GPU. The algorithm, whose goal is to compute layouts accurately and quickly, has two contributions. The first contribution is proposing a general multi-level scheme, which is based on spectral partitioning. The second contribution is computing the layout on the GPU. Since the […]

FairRoot is the simulation and analysis framework used by CBM and PANDA experiments at FAIR/GSI. The use of graphics processor units (GPUs) for event reconstruction in FairRoot will be presented. The fact that CUDA (Nvidia’s Compute Unified Device Architecture) development tools work alongside the conventional C/C++ compiler, makes it possible to mix GPU code with […]

Many computer graphics applications require high-intensity numerical simulation. The question arises whether such computations can be performed efficiently on the GPU, which has emerged as a full function streaming processor with high floating point performance. We show in this paper that this is indeed the case using two basic, broadly useful, computational kernels as examples. […]

Using dedicated hardware to do machine learning typically ends up in disaster because of cost, obsolescence, and poor software. The popularization of Graphic Processing Units (GPUs), which are now available on every PC, provides an attractive alternative. We propose a generic 2-layer fully connected neural network GPU implementation which yields over 3X speedup for both […]

In this paper, we report our research on using GPUs to accelerate clustering of very large data sets, which are common in today’s real world applications. While many published works have shown that GPUs can be used to accelerate various general purpose applications with respectable performance gains, few attempts have been made to tackle very […]

This paper presents an effective scheme for clustering a huge data set using a PC cluster system, in which each PC is equipped with a commodity programmable graphics processing unit (GPU). The proposed scheme is devised to achieve three-level hierarchical parallel processing of massive data clustering. The divide-and-conquer approach to parallel data clustering is employed […]