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Posts

Oct, 27

GPU acceleration of a production molecular docking code

Modeling the interactions of biological molecules, or docking , is critical to both understanding basic life processes and to designing new drugs. Here we describe the GPU-based acceleration of a recently developed, complex, production docking code. We show how the various functions can be mapped to the GPU and present numerous optimizations. We find which […]
Oct, 27

GPU accelerated molecular dynamics simulation of thermal conductivities

Molecular dynamics (MD) simulations have become a powerful tool for elucidating complex physical phenomena. However, MD method is very time-consuming. This paper presents a method to accelerate computation of MD simulation. The acceleration is achieved by take advantage of modern graphics processing units (GPU). As an example, the thermal conductivities of solid argon were calculated […]
Oct, 27

ACEMD: Accelerating Biomolecular Dynamics in the Microsecond Time Scale

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 […]
Oct, 27

Monte Carlo simulations on Graphics Processing Units

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 […]
Oct, 27

GPU Cluster for High Performance Computing

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 […]
Oct, 27

GPU Gems 3

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 […]
Oct, 27

Simulating spin models on GPU

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 […]
Oct, 27

Fast fluid dynamics simulation on the GPU

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 […]
Oct, 27

Getting Started with GPU Programming

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.
Oct, 27

GPU implementation of JPEG XR

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 […]
Oct, 27

Monte Carlo integration on 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 […]
Oct, 27

Computational advances in gravitational microlensing: a comparison of CPU, GPU, and parallel, large data codes

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 […]
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