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Posts

Feb, 11

The fast multipole method on parallel clusters, multicore processors, and graphics processing units

In this article, we discuss how the fast multipole method (FMM) can be implemented on modern parallel computers, ranging from computer clusters to multicore processors and graphics cards (GPU). The FMM is a somewhat difficult application for parallel computing because of its tree structure and the fact that it requires many complex operations which are […]
Feb, 11

Comparing CUDA and OpenGL implementations for a Jacobi iteration

The use of the GPU as a general purpose processor is becoming more popular and there are different approaches for this kind of programming. In this paper we present a comparison between different implementations of the OpenGL and CUDA approaches for solving our test case, a weighted Jacobi iteration with a structured matrix originating from […]
Feb, 11

Comparison of several parallel API for cloth modelling on modern GPUs

The paper compares three APIs for the implementation of cloth modelling on modern graphics processor units (GPU): OpenGL plus GLSL, NVIDIA CUDA and OpenCL. They are compared by programming features, platform and device portability, and performance for the purpose of dynamic cloth simulation. Results about performance are given and conclusions are drawn about use cases.
Feb, 11

GPU-based fast pencil beam algorithm for proton therapy

Performance of a treatment planning system is an essential factor in making sophisticated plans. The dose calculation is a major time-consuming process in planning operations. The standard algorithm for proton dose calculations is the pencil beam algorithm which produces relatively accurate results, but is time consuming. In order to shorten the computational time, we have […]
Feb, 11

Energy-efficient algorithms

Algorithmic solutions can help reduce energy consumption in computing environs. Energy conservation is a major concern today. Federal programs provide incentives to save energy and promote the use of renewable energy resources. Individuals, companies, and organizations seek energyefficient products as the energy cost to run equipment has grown to be a major factor.
Feb, 11

GPU-accelerated indirect boundary element method for voxel model analyses with fast multipole method

An indirect boundary element method (BEM) that uses the fast multipole method (FMM) was accelerated using graphics processing units (GPUs) to reduce the time required to calculate a three-dimensional electrostatic field. The BEM is designed to handle cubic voxel models and is specialized to consider square voxel walls as boundary surface elements. The FMM handles […]
Feb, 11

EigenCFA: accelerating flow analysis with GPUs

We describe, implement and benchmark EigenCFA, an algorithm for accelerating higher-order control-flow analysis (specifically, 0CFA) with a GPU. Ultimately, our program transformations, reductions and optimizations achieve a factor of 72 speedup over an optimized CPU implementation. We began our investigation with the view that GPUs accelerate high-arithmetic, data-parallel computations with a poor tolerance for branching. […]
Feb, 11

Fast computing of scattering maps of nanostructures using graphical processing units

Scattering maps from strained or disordered nano-structures around a Bragg reflection can either be computed quickly using approximations and a (Fast) Fourier transform, or using individual atomic positions. In this article we show that it is possible to compute up to 4.10^10 $reflections.atoms/s using a single graphic card, and we evaluate how this speed depends […]
Feb, 11

Accelerating the solution of families of shifted linear systems with CUDA

We describe the GPU implementation of shifted or multimass iterative solvers for sparse linear systems of the sort encountered in lattice gauge theory. We provide a generic tool that can be used by those without GPU programming experience to accelerate the simulation of a wide array of theories. We stress genericity, which is important to […]
Feb, 10

Interpretive OpenGL for computer graphics

OpenGL is the industry-leading, cross-platform graphics application programming interface (API), and the only major API with support for virtually all operating systems. Many languages, such as Fortran, Java, Tcl/Tk, and Python, have OpenGL bindings to take advantage of OpenGL visualization power. In this article, we present Ch OpenGL Toolkit, a truly platform-independent Ch binding to […]
Feb, 10

Interactive Computer Graphics: A Top-Down Approach Using OpenGL (5th Edition)

Interactive Computer Graphics fourth edition presents introductory computer graphics concepts using a proven top-down, programming-oriented approach and careful integration of OpenGL to teach core concepts. The fourth edition has been revised to more closely follow the OpenGL pipeline architecture and includes a new chapter on programmable hardware topics (vertex shaders). As with previous editions, readers […]
Feb, 10

OpenGL(R) Programming Guide: The Official Guide to Learning OpenGL(R), Version 2 (5th Edition)

The “OpenGL Programming Guide”, now in its third edition, is the definitive volume for programmers using this evolving graphics interface standard. Written by members of the OpenGL Architecture Review Board, this book offers understandable tutorials and lessons on getting up to speed and getting the most out of the latest version of OpenGL, version 1.2. […]
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Free GPU computing nodes at hgpu.org

Registered users can now run their OpenCL application at hgpu.org. We provide 1 minute of computer time per each run on two nodes with two AMD and one nVidia graphics processing units, correspondingly. There are no restrictions on the number of starts.

The platforms are

Node 1
  • GPU device 0: AMD/ATI Radeon HD 5870 2GB, 850MHz
  • GPU device 1: AMD/ATI Radeon HD 6970 2GB, 880MHz
  • CPU: AMD Phenom II X6 @ 2.8GHz 1055T
  • RAM: 12GB
  • OS: OpenSUSE 13.1
  • SDK: AMD APP SDK 2.9
Node 2
  • GPU device 0: AMD/ATI Radeon HD 7970 3GB, 1000MHz
  • GPU device 1: nVidia GeForce GTX 560 Ti 2GB, 822MHz
  • CPU: Intel Core i7-2600 @ 3.4GHz
  • RAM: 16GB
  • OS: OpenSUSE 12.2
  • SDK: nVidia CUDA Toolkit 6.0.1, AMD APP SDK 2.9

Completed OpenCL project should be uploaded via User dashboard (see instructions and example there), compilation and execution terminal output logs will be provided to the user.

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