high performance computing on graphics processing units: hgpu.org

The high arithmetic performance and intrinsic parallelism of recent graphical processing units (GPUs) can offer a technological edge for molecular dynamics simulations. ACEMD is a production-class biomolecular dynamics (MD) engine supporting CHARMM and AMBER force fields. Designed specifically for GPUs it is able to achieve supercomputing scale performance of 40 ns/day for all-atom protein systems […]

Implementation of basic local Monte-Carlo algorithms on ATI Graphics Processing Units (GPU) is investigated. The Ising model and pure SU(2) gluodynamics simulations are realized with the Compute Abstraction Layer (CAL) of ATI Stream environment using the Metropolis and the heat-bath algorithms, respectively. We present an analysis of both CAL programming model and the efficiency of […]

Inspired by the attractive Flops/dollar ratio and the incredible growth in the speed of modern graphics processing units (GPUs), we propose to use a cluster of GPUs for high performance scientific computing. As an example application, we have developed a parallel flow simulation using the lattice Boltzmann model (LBM) on a GPU cluster and have […]

The GPU Gems series features a collection of the most essential algorithms required by Next-Generation 3D Engines.” -Martin Mittring, Lead Graphics Programmer, Crytek This third volume of the best-selling GPU Gems series provides a snapshot of today’s latest Graphics Processing Unit (GPU) programming techniques. The programmability of modern GPUs allows developers to not only distinguish […]

Over the last couple of years it has been realized that the vast computational power of graphics processing units (GPUs) could be harvested for purposes other than the video game industry. This power, which at least nominally exceeds that of current CPUs by large factors, results from the relative simplicity of the GPU architectures as […]

This chapter describes a method for fast, stable fluid simulation that runs entirely on the GPU. It introduces fluid dynamics and the associated mathematics, and it describes in detail the techniques to perform the simulation on the GPU. After reading this chapter, you should have a basic understanding of fluid dynamics and know how to […]

This tutorial describes a step-by-step procedure for programming a Macintosh Nvidia GPU. General scientific programmers with some C knowledge can get started in parallel processing application development with relative ease.

JPEG XR (formerly Microsoft Windows Media Photo and HD Photo) is the latest image coding standard. By integrating various advanced technologies such as integer hierarchical lapped transform, context adaptive Huffman coding, and high dynamic range coding, it achieves competitive performance to JPEG-2000, but with lower computational complexity and memory requirement. In this paper, the GPU […]

We use a graphics processing unit (GPU) for fast computations of Monte Carlo integrations. Two widely used Monte Carlo integration programs, VEGAS and BASES, are parallelized on GPU. By using $W^+$ plus multi-gluon production processes at LHC, we test integrated cross sections and execution time for programs in FORTRAN and C on CPU and those […]

To assess how future progress in gravitational microlensing computation at high optical depth will rely on both hardware and software solutions, we compare a direct inverse ray-shooting code implemented on a graphics processing unit (GPU) with both a widely-used hierarchical tree code on a single-core CPU, and a recent implementation of a parallel tree code […]

A Modern Graphics Processing unit (GPU) is able to perform massively parallel scientific computations at low cost. We extend our implementation of the checkerboard algorithm for the two-dimensional Ising model [T. Preis et al., Journal of Chemical Physics 228 (2009) 4468–4477] in order to overcome the memory limitations of a single GPU which enables us […]

GPUs have recently attracted the attention of many application developers as commodity data-parallel coprocessors. The newest generations of GPU architecture provide easier programmability and increased generality while maintaining the tremendous memory bandwidth and computational power of traditional GPUs. This opportunity should redirect efforts in GPGPU research from ad hoc porting of applications to establishing principles […]