User-Friendly Parallel Computations with Econometric Examples

Creel, Michael

doi:10.1007/s10614-005-6868-2

Cited by 37 publications

(31 citation statements)

References 8 publications

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“…In this sense our paper contributes to the recent stream of the literature on the use of Central Processing Unit (CPU) and Graphics Processing Unit (GPU) parallel computing in econometrics (e.g., see Doornik et al (2002), Swann (2002), Creel (2005), Creel and Goffe (2008), Suchard et al (2010)). …”

Section: Introductionmentioning

confidence: 87%

Embarrassingly parallel sequential Markov-chain Monte Carlo for large sets of time series

Casarin

Craiu

Leisen

2016

Statistics and Its Interface

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Bayesian computation crucially relies on Markov chain Monte Carlo (MCMC) algorithms. In the case of massive data sets, running the Metropolis-Hastings sampler to draw from the posterior distribution becomes prohibitive due to the large number of likelihood terms that need to be calculated at each iteration. In order to perform Bayesian inference for a large set of time series, we consider an algorithm that combines "divide and conquer" ideas previously used to design MCMC algorithms for big data with a sequential MCMC strategy. The performance of the method is illustrated using a large set of financial data.

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Section: Introductionmentioning

confidence: 87%

Embarrassingly parallel sequential Markov-chain Monte Carlo for large sets of time series

Casarin

Craiu

Leisen

2016

Statistics and Its Interface

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“…Both run on different platforms and support parallel programming through PVM-based ProActive Interface for Scilab and MPI Toolbox (MPITB) for Octave. To decide between both we have prioritized the asynchronous message passing and data-parallel capabilities of MPI over the dynamic process spawning feature of PVM and consequently arrived at the ParallelKnoppix (PK) software featuring MPITB for Octave [5,7,8].…”

Section: Parallelknoppixmentioning

confidence: 99%

Cost-Effective HPC Clustering for Computer Vision Applications

Dietlmeier

Begley

Whelan

2008

2008 International Machine Vision and Image Processing Conference

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We will present a cost-effective and flexible realization of High Performance Computing (HPC) clustering and its potential in solving computationally intensive problems in computer vision. The featured software foundation to support the parallel programming is the GNU Parallel Knoppix package with Message Passing Interface (MPI) based Octave, Python and C interface capabilities. The implementation is especially of interest in applications where the main objective is to reuse the existing hardware infrastructure and to maintain the overall budget cost. We will present the benchmark results and compare and contrast the performances of Octave and MATLAB.

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“…An early attempt to use parallel computation for Monte Carlo simulation is Chong and Hendry (1986), while Swann (2002) develops a parallel implementation of maximum likelihood estimation. Creel and Goffe (2008) low diffusion of this technology in economics and econometrics, according to Creel (2005), is mainly due to two issues, which are the high cost of the hardware, e.g., a computing cluster, and the steep learning curve of dedicated programming languages such as CUDA (compute unified device architecture, see NVIDIA Corporation 2010), OpenCL (Khronos OpenCL Working Group 2009), Thrust (Hoberock and Bell 2011) and C++ AMP (C++ accelerated massive parallelism, see Gregory and Miller 2012). Table 1 compares different currently available GPGPU approaches.…”

Section: Introductionmentioning

confidence: 99%

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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User-Friendly Parallel Computations with Econometric Examples

Cited by 37 publications

References 8 publications

Embarrassingly parallel sequential Markov-chain Monte Carlo for large sets of time series

Embarrassingly parallel sequential Markov-chain Monte Carlo for large sets of time series

Cost-Effective HPC Clustering for Computer Vision Applications

Parallel Sequential Monte Carlo for Efficient Density Combination: TheDeCoMATLABToolbox

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