Proceedings ArticleDOI
GPU Cluster for High Performance Computing
Zhe Fan,Feng Qiu,Arie E. Kaufman,Suzanne Yoakum-Stover +3 more
- pp 47-47
TLDR
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.read more
Citations
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Proceedings ArticleDOI
Mars: a MapReduce framework on graphics processors
TL;DR: Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface, and is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.
Book ChapterDOI
Accelerating large graph algorithms on the GPU using CUDA
Pawan Harish,P. J. Narayanan +1 more
TL;DR: This work presents a few fundamental algorithms - including breadth first search, single source shortest path, and all-pairs shortest path - using CUDA on large graphs using the G80 line of Nvidia GPUs.
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GPU-accelerated molecular modeling coming of age
TL;DR: The development of molecular modeling algorithms that leverage GPU computing, the advances already made and remaining issues to be resolved, and the continuing evolution of GPU technology that promises to become even more useful to molecular modeling are surveyed.
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CellSs: a programming model for the cell BE architecture
TL;DR: This work presents Cell superscalar (CellSs), which addresses the automatic exploitation of the functional parallelism of a sequential program through the different processing elements of the Cell BE architecture to improve the simplicity and flexibility of the programming model.
Proceedings ArticleDOI
Where is the data? Why you cannot debate CPU vs. GPU performance without the answer
Chris Gregg,Kim Hazelwood +1 more
TL;DR: A taxonomy for future CPU/GPU comparisons is suggested, and it is argued that this is not only germane for reporting performance, but is important to heterogeneous scheduling research in general.
References
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Journal ArticleDOI
The Lattice Boltzmann Equation for Fluid Dynamics and Beyond by Sauro Succi (Clarendon Press, Oxford, 2001) ISBN 0 19 850398 9
Journal ArticleDOI
Can programming be liberated from the von Neumann style?: a functional style and its algebra of programs
TL;DR: A new class of computing systems uses the functional programming style both in its programming language and in its state transition rules; these systems have semantics loosely coupled to states—only one state transition occurs per major computation.
BookDOI
Lattice-Gas Cellular Automata and Lattice Boltzmann Models
TL;DR: In this paper, the authors provide an introduction to lattice gas cellular automata (LGCA) and lattice Boltzmann models (LBM) for numerical solution of nonlinear partial differential equations.
Journal ArticleDOI
Brook for GPUs: stream computing on graphics hardware
Ian Buck,Tim Foley,Daniel Reiter Horn,Jeremy Sugerman,Kayvon Fatahalian,Mike Houston,Pat Hanrahan +6 more
TL;DR: This paper presents Brook for GPUs, a system for general-purpose computation on programmable graphics hardware that abstracts and virtualizes many aspects of graphics hardware, and presents an analysis of the effectiveness of the GPU as a compute engine compared to the CPU.
Proceedings ArticleDOI
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
TL;DR: This work implemented two basic, broadly useful, computational kernels: a sparse matrix conjugate gradient solver and a regular-grid multigrid solver for high-intensity numerical simulation of geometric flow and fluid simulation on the GPU.