Graphics processing unit (GPU) programming strategies and trends in GPU computing

Brodtkorb, André R.; Hagen, Trond Runar; Sætra, Martin Lilleeng

doi:10.1016/j.jpdc.2012.04.003

Cited by 228 publications

(128 citation statements)

References 7 publications

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“…More specifically, our simulation and empirical experiments were conducted using a commercial notebook with CPU Intel Core i7-3820QM and GPU NVIDIA Quadro K2000M, and MATLAB 2013b version, and show that the DeCo GPU version is faster than the parallel CPU version, up to 10 times when the weights include a learning mechanism and up to 5.5 times without it, when using an i7 CPU machine and the Parallel Computing Toolbox. These findings are similar to results in Brodtkorb et al (2013) when using a raw CUDA environment. In the comparison between GPU and non-parallel CPU implementations, the differences between GPU and CPU time increase up to almost 70 times when using a standard CPU processor, such as quad-core Xeon.…”

Section: Resultssupporting

confidence: 87%

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

et al. 2014

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This paper presents the MATLAB package DeCo (density combination) which is based on the paper by Billio, Casarin, Ravazzolo, and van Dijk (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel Sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing and for graphical process unit (GPU) parallel computing. For the GPU implementation we use the MATLAB parallel computing toolbox and show how to use general purpose GPU computing almost effortlessly. This GPU implementation provides a speed up of the execution time of up to seventy times on a standard CPU MATLAB implementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version through some simulation experiments and empirical applications.

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Section: Resultssupporting

confidence: 87%

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

et al. 2014

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show abstract

“…More specifically, our simulation and empirical experiments were conducted using a commercial notebook with CPU Intel Core i7-3820QM and GPU NVIDIA Quadro K2000M, and MATLAB 2013b version, and show that the DeCo GPU version is faster than the parallel CPU version, up to 10 times when the weights include a learning mechanism and up to 5.5 times without it, when using an i7 CPU machine and the parallel computing toolbox. These findings are similar to results in Brodtkorb et al (2013) when using a raw CUDA 6 Unreported results show that GPU is more than 36 times faster than sequential CPU implementation on Intel Xeon X3430 4core.…”

Section: Resultssupporting

confidence: 83%

Parallel Sequential Monte Carlo for Efficient Density Combination: TheDeCoMATLABToolbox

Casarin¹,

Grassi²,

Ravazzolo³

et al. 2015

J. Stat. Soft.

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This paper presents the MATLAB package DeCo (density combination) which is based on the paper by Billio, Casarin, Ravazzolo, and van Dijk (2013) where a constructive Bayesian approach is presented for combining predictive densities originating from different models or other sources of information. The combination weights are time-varying and may depend on past predictive forecasting performances and other learning mechanisms. The core algorithm is the function DeCo which applies banks of parallel sequential Monte Carlo algorithms to filter the time-varying combination weights. The DeCo procedure has been implemented both for standard CPU computing and for graphical process unit (GPU) parallel computing. For the GPU implementation we use the MATLAB parallel computing toolbox and show how to use general purpose GPU computing almost effortlessly. This GPU implementation provides a speed-up of the execution time of up to seventy times on a standard CPU MATLAB implementation on a multicore CPU. We show the use of the package and the computational gain of the GPU version through some simulation experiments and empirical applications.

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“…In the work below, this limiting factor is investigated along with the effects of these models on the GPGPU. Lastly Brodtkorb et al [31] perform a good review of current trends in GPGPU computing, and say "reporting a speedup of hundreds of times or more holds no scientific value without further explanation supported by detailed benchmarks and profiling results." [31].…”

Section: Related Workmentioning

confidence: 99%

An investigation of the efficient implementation of cellular automata on multi-core CPU and GPU hardware

Gibson

Keedwell

Savić

2015

Journal of Parallel and Distributed Computing

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-Cellular automata (CA) have proven to be excellent tools for the simulation of a wide variety of phenomena in the natural world. They are ideal candidates for acceleration with modern general purpose-graphical processing units (GPU/GPGPU) hardware that consists of large numbers of small, tightly-coupled processors. In this study the potential for speeding up CA execution using multi-core CPUs and GPUs is investigated and the scalability of doing so with respect to standard CA parameters such as lattice and neighbourhood sizes, numbers of states and generations is determined.Additionally the impact of 'Activity' (the number of 'alive' cells) within a given CA simulation is investigated in terms of both varying the random initial distribution levels of 'alive' cells, and via the use of novel state transition rules; Where a change in the dynamics of these rules (i.e. the number of states) allows for the investigation of the variable complexity within.3

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Graphics processing unit (GPU) programming strategies and trends in GPU computing

Cited by 228 publications

References 7 publications

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

Parallel Sequential Monte Carlo for Efficient Density Combination: The DeCo MATLAB Toolbox

Parallel Sequential Monte Carlo for Efficient Density Combination: TheDeCoMATLABToolbox

An investigation of the efficient implementation of cellular automata on multi-core CPU and GPU hardware

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