Accelerating 3D convolution using graphics hardware (case study)
VIS '99 Proceedings of the conference on Visualization '99: celebrating ten years
Physically-based visual simulation on graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Fast matrix multiplies using graphics hardware
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Using modern graphics architectures for general-purpose computing: a framework and analysis
Proceedings of the 35th annual ACM/IEEE international symposium on Microarchitecture
A multigrid solver for boundary value problems using programmable graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Proceedings of the ACM SIGGRAPH/EUROGRAPHICS conference on Graphics hardware
Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Sparse matrix solvers on the GPU: conjugate gradients and multigrid
ACM SIGGRAPH 2003 Papers
Nonlinear diffusion in graphics hardware
EGVISSYM'01 Proceedings of the 3rd Joint Eurographics - IEEE TCVG conference on Visualization
Discrete-event Execution Alternatives on General Purpose Graphical Processing Units (GPGPUs)
Proceedings of the 20th Workshop on Principles of Advanced and Distributed Simulation
GPU accelerated molecular dynamics simulation of thermal conductivities
Journal of Computational Physics
Data parallel execution challenges and runtime performance of agent simulations on GPUs
Proceedings of the 2008 Spring simulation multiconference
GPU accelerated Monte Carlo simulation of the 2D and 3D Ising model
Journal of Computational Physics
LBM based flow simulation using GPU computing processor
Computers & Mathematics with Applications
Better by a HAIR: hardware-amenable Internet routing
Computer Networks: The International Journal of Computer and Telecommunications Networking
Importance of explicit vectorization for CPU and GPU software performance
Journal of Computational Physics
Parallel implementation of the heisenberg model using Monte Carlo on GPGPU
ICCSA'11 Proceedings of the 2011 international conference on Computational science and its applications - Volume Part III
Scientific computing on commodity graphics hardware
CIS'04 Proceedings of the First international conference on Computational and Information Science
Performance potential for simulating spin models on GPU
Journal of Computational Physics
Use of GPU computing for uncertainty quantification in computational mechanics: A case study
Scientific Programming
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The latest graphics processing units (GPUs) are reported to reach up to 200 billion floating point operations per second (200Gflops (Spode's Abode, GeForce FX Preview (NV30), Spode, November (2002), Internet address (accessed on 10/2003): http://www.spodesabode.com/content/article/geforcefx)) and to have price performance of 0.1 cents per Mflop. These facts raise great interest in the plausibility of extending the GPUs' use to non-graphics applications, in particular numerical simulations on structured grids (lattice). In this paper we (1) review previous works on using GPUs for non-graphics applications, (2) implement probability-based simulations on the GPU, namely the Ising and percolation models, (3) implement vector operation benchmarks for the GPU, and finally (4) compare the CPU's and GPU's performance. Original contribution of this work is implementing Monte Carlo type simulations on the GPU. Such simulations have a wide area of applications. They are computationally intensive and, as we show in the paper, lend themselves naturally to implementation on GPUs, therefore allowing us to better use the GPU's computational power and speed up the computation. A general conclusion from the results obtained is that moving computations from the CPU to the GPU is feasible, yielding good time and price performance, for certain lattice computations. Preliminary results also show that it is feasible to use them in parallel.