Topic
GPU cluster
About: GPU cluster is a research topic. Over the lifetime, 797 publications have been published within this topic receiving 16849 citations.
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TL;DR: This paper develops a general purpose molecular dynamics code that runs entirely on a single GPU and shows that the GPU implementation provides a performance equivalent to that of fast 30 processor core distributed memory cluster.
1,514 citations
TL;DR: This work provides a validation and performance evaluation of the code and runs a microsecond-long trajectory for an all-atom molecular system in explicit TIP3P water on a single workstation computer equipped with just 3 GPUs, believing that microsecond time scale molecular dynamics on cost-effective hardware will have important methodological and scientific implications.
Abstract: 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 with over 23 000 atoms. We provide a validation and performance evaluation of the code and run a microsecond-long trajectory for an all-atom molecular system in explicit TIP3P water on a single workstation computer equipped with just 3 GPUs. We believe that microsecond time scale molecular dynamics on cost-effective hardware will have important methodological and scientific implications.
767 citations
TL;DR: Algorithm for efficient short range force calculation on hybrid high-performance machines, an approach for dynamic load balancing of work between CPU and accelerator cores, and the Geryon library that allows a single code to compile with both CUDA and OpenCL for use on a variety of accelerators are described.
557 citations
06 Nov 2004
TL;DR: A parallel flow simulation using the lattice Boltzmann model (LBM) on a GPU cluster and the dispersion of airborne contaminants in the Times Square area of New York City are simulated.
Abstract: 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 simulated the dispersion of airborne contaminants in the Times Square area of New York City. Using 30 GPU nodes, our simulation can compute a 480x400x80 LBM in 0.31 second/step, a speed which is 4.6 times faster than that of our CPU cluster implementation. Besides the LBM, we also discuss other potential applications of the GPU cluster, such as cellular automata, PDE solvers, and FEM.
485 citations
TL;DR: This paper presents an efficient implementation of the particle–particle particle-mesh method based on the work by Harvey and De Fabritiis, and provides a performance comparison of the same kernels compiled with both CUDA and OpenCL.
381 citations