high performance computing on graphics processing units: hgpu.org

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 […]

Turbulence simulations are computationally expensive and typically take weeks to complete. With the advent of GPGPU(General Purpose computing on GPUs), one can achieve significant speedup. Spectral method is one of the several known methods used in turbulence simulations. It is based on Fast Fourier Transforms(FFT) to an extent that it spends 70-80% time in calculating […]

We propose a four-tiered parallelization model for acceleration of the secure multiparty computation (SMC) on the CUDA based Graphic Processing Unit (GPU) cluster architecture. Specification layer is the top layer, which adopts the SFDL of Fairplay for specification of secure computations. The SHDL file generated by the SFDL compiler of Fairplay is used as inputs […]

The Hough transform is a commonly used algorithm to detect lines and other features in images. It is robust to noise and occlusion, but has a large computational cost. This paper introduces two new implementations of the Hough transform for lines on a GPU. One focuses on minimizing processing time, while the other has an […]

AES is a strong encryption system, so decryption-decompression of AES encrypted ZIP files requires very large computing power and techniques of reducing the password space. This makes implementations of techniques on common computing system not practical. In [1], we reduced the original very large password search space to a much smaller one which surely containing […]

CPU-GPU clusters have emerged as a dominant HPC platform, with the three of the four fastest supercomputers in the world falling in this category. The reasons for the popularity of these environments include their cost-effectiveness and energy efficiency. The need for exploiting both the CPU and GPU on each node of such platforms has created […]