high performance computing on graphics processing units: hgpu.org

Jit4OpenCL is a new compiler that converts scientific applications written in Python/NumPy into OpenCL code. This compiler is based on unPython, an ahead-of-time compiler from Python/Numpy to an intermediate form and OpenMP code, and on jit4GPU, a just-in-time compiler that converts that intermediate code into AMD CAL code that is specific for AMD GPUs. The […]

Derivatives arise frequently in graphics and scientific computation applications. As GPU’s become more widely used for scientific computation the need for derivatives can be expected to increase. To meet this need we have added symbolic differentiation as a built in language feature in the HLSL shading language. The symbolic derivative is computed at compile time […]

In this paper, we propose the use of graphics processing units as a low-cost and efficient solution of electromagnetic (and other) numerical problems. Based on the software platform CUDA (Compute Unified Device Architecture), a solver for unstructured sparse matrices with double precision complex data has been implemented and tested for several practical cases. Benchmark results […]

During the last years the computer networks have become an important part of our society. Networks have kept growing in size and complexity, making more complex its management and traffic monitoring and analysis processes, due to the huge amount of data and calculations involved. In the last decade, several researchers found effective to use graphics […]

Computer games, TV series, movies, simulators, and many other computer graphics applications use external scenes where a realistic looking terrain is a vital part of the viewing experience. Creating such terrains is a challenging task. In this paper we propose a method that generates realistic virtual terrains by simulation of hydraulic and thermal erosion on […]

The graphics processing unit (GPU) has emerged as a powerful and cost effective processor for high performance computing. GPUs are capable of an order of magnitude more floating point operations per second as compared to modern central processing units (CPUs), and thus provide a great deal of promise for computationally intensive statistical applications (Brodtkorb et […]

The goal of the project is to create a Multiprocessor system capable of rendering a 3D model into an MPEG-4 stream. This paper outlines the design, software architecture and hardware setup for the system. Preliminary success in the previous setup[1] helped us gain experience as well as motivation for this highly optimized and powerful second […]

The variable preconditioned (VP) Krylov subspace method on multi Graphics Processing Unit (GPU) is numerically investigated. Besides, the linear system obtained by finite element method with an edge element is adopted for the problem. The results of computations show that VP conjugate gradient method on multi GPU demonstrated significant achievement than that of CPU. Especially, […]

We present a framework for parallel programming. It consists of a distributed shared memory based simplified programming model, which leaves the application developer to focus mainly on task decomposition. This is a unified model for many-core processors (e.g., CPUs and GPUs), multiple processors on a system, as well as multiple systems. We also present a […]

Among the various techniques for solving hyperbolic partial differential equations with inhomogeneous, irregularly-shaped domains, a relatively new type of finite element method has grown in popularity because of its flexibility and scalability across many parallel cores. Discont inuous Galerkin (DG) methods have shown themselves to be an effective scheme for the simulation of wave-propagation problems […]

The method of Support Operator(SOM) is a numerical method based on finite difference method. We use SOM to simulate seismic wave propagation by solving the three dimension viscoelastic equations. The Support Operator Rupture Dynamics(SORD) has been proved to be highly scalable in large-scale multi-processors computing. This paper discusses accelerating SORD on multi-GPU system using NVIDIA […]

In this paper, we propose an efficient parallel implementation of the GMRES method for GPU clusters. This implementation requires us to parallelize the GMRES algorithm between the CPUs of the cluster. Hence, all parallel and intensive computations on local data are performed on GPUs and reduction operations to compute global results are carried out by […]