Parallel Computing Techniques

Nakano, Junji

doi:10.1007/978-3-642-21551-3_9

Cited by 13 publications

(5 citation statements)

References 24 publications

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“…Temple Lang (1997) described a multithreaded application in S. PVM and MPI are directly available from R via the rpvm, Rmpi, and npRmpi packages. They can all be used in combination with the "snow" package, which facilitates implementation of embarrassingly parallel computations in R (Nakano 2004). There are now more than 20 packages for parallel statistical computing in R. Parallel statistical methods are also available in Matlab, through its Parallel Toolbox.…”

Section: Parallel Statistical Softwarementioning

confidence: 99%

Parallel Statistical Computing for Statistical Inference

Guo

2012

Journal of Statistical Theory and Practice

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Parallel statistical computing is an interesting and topical problem, driven by recent growth in the size of statistical data sets and the availability of network computing. This article reviews parallel statistical computing in regression analysis, nonparametric inference, and stochastic processes. In particular, we describe a range of methods including parallel multisplitting and the parallel QR method for least squares estimation in linear regression, parallel computing methods for nonlinear regression, the theoretical framework of the parallel bootstrap in nonparametric inference, preconditioner methods for Markov chains, and parallel Markov-chain Monte Carlo methods. We conclude that there is a need for further research in parallel statistical computing, and describe some of the important unsolved problems.

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Section: Parallel Statistical Softwarementioning

confidence: 99%

Parallel Statistical Computing for Statistical Inference

Guo

2012

Journal of Statistical Theory and Practice

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“…As shown above, this is not the case because in the implementation of a parallel algorithm there are some inherent non-parallelizable parts and communication costs between tasks (Nakano 2012). Amdahl's Law (Amdahl 1967) is often used in parallel computing to predict the theoretical maximum speedup when using multiple processors.…”

Section: Adjustments For Speeding Up the Algorithmmentioning

confidence: 99%

clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R

Scrucca¹,

Raftery²

2018

J. Stat. Soft.

106

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Finite mixture modeling provides a framework for cluster analysis based on parsimonious Gaussian mixture models. Variable or feature selection is of particular importance in situations where only a subset of the available variables provide clustering information. This enables the selection of a more parsimonious model, yielding more efficient estimates, a clearer interpretation and, often, improved clustering partitions. This paper describes the R package clustvarsel which performs subset selection for model-based clustering. An improved version of the Raftery and Dean (2006) methodology is implemented in the new release of the package to find the (locally) optimal subset of variables with group/cluster information in a dataset. Search over the solution space is performed using either a step-wise greedy search or a headlong algorithm. Adjustments for speeding up these algorithms are discussed, as well as a parallel implementation of the stepwise search. Usage of the package is presented through the discussion of several data examples.

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“…By using a machine with P cores/processors, we would like to obtain an increase in calculation speed of P times. However, this is typically not the case because in the implementation of a parallel algorithm there are some inherent non-parallelisable parts and communication costs between tasks (Nakano, 2012). The speedup achieved using P processors is computed as s P = q q q q q 0 2000 4000 6000 8000 1 2 4 8 16…”

Section: Simulation Studymentioning

confidence: 99%

On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution

Scrucca

2016

Preprint

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Genetic algorithms are stochastic iterative algorithms in which a population of individuals evolve by emulating the process of biological evolution and natural selection. The R package GA provides a collection of general purpose functions for optimisation using genetic algorithms. This paper describes some enhancements recently introduced in version 3 of the package. In particular, hybrid GAs have been implemented by including the option to perform local searches during the evolution. This allows to combine the power of genetic algorithms with the speed of a local optimiser. Another major improvement is the provision of facilities for parallel computing. Parallelisation has been implemented using both the master-slave approach and the islands evolution model. Several examples of usage are presented, with both real-world data examples and benchmark functions, showing that often high-quality solutions can be obtained more efficiently.

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Parallel Computing Techniques

Cited by 13 publications

References 24 publications

Parallel Statistical Computing for Statistical Inference

Parallel Statistical Computing for Statistical Inference

clustvarsel: A Package Implementing Variable Selection for Gaussian Model-Based Clustering in R

On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution

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