A review of parallel processing for statistical computation

Adams, Niall M.; Kirby, Simon; Harris, P.; Clegg, D. B.

doi:10.1007/bf00161572

Cited by 13 publications

(10 citation statements)

References 37 publications

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“…The possibility of parallel processing has long been recognized by statisticians in carrying out computing intensive methods (Sylwestrowicz 1982;Schervish 1988;Adams et al 1996). For applications such as bootstrapping and kriging, algorithms can be "embarrassingly parallel" (Rossini et al 2003), since no inter-processor communication is needed.…”

Section: Introductionmentioning

confidence: 99%

Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

et al. 2007

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When MCMC methods for Bayesian spatiotemporal modeling are applied to large geostatistical problems, challenges arise as a consequence of memory requirements, computing costs, and convergence monitoring. This article describes the parallelization of a reparametrized and marginalized posterior sampling (RAMPS) algorithm, which is carefully designed to generate posterior samples efficiently. The algorithm is implemented using the Parallel Linear Algebra Package (PLAPACK). The scalability of the algorithm is investigated via simulation experiments that are implemented using a cluster with 25 processors. The usefulness of the method is illustrated with an application to sulfur dioxide concentration data from the Air Quality System database of the U.S. Environmental Protection Agency.

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Section: Introductionmentioning

confidence: 99%

Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

et al. 2007

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“…There have been earlier reviews in parallel statistical computing (e.g., Adams et al 1996), so we have aimed to review only the last decade. We have reviewed the use of parallel statistical computation, particularly in linear and nonlinear regression, density estimation, Markov-chain Monte Carlo methods, and methods involving SDEs.…”

Section: Conclusion and Discussionmentioning

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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“…PDP is architecturally and algorithmically more complex than meta-analysis, and cannot be easily implemented in distributed networks with independently operating instances of programs like SAS because a centralized algorithm must manage estimation convergence across all sites. 37 With this approach, regression parameter estimates are collected at every iteration and a new candidate vector of coefficients is generated for testing and correction at each site until estimations converge. These methods are not redundant, as GLORE and related parallel distributed methods can increase power if data are statistically homogeneous across all sites, 37 , 38 while meta-regression methods like OCEANS compute independently estimated models and are most appropriate if data are not identically distributed at all sites.…”

Section: Methods and Approachmentioning

confidence: 99%

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

Meeker

Jiang

Matheny

et al. 2015

Journal of the American Medical Informatics Association

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Background Centralized and federated models for sharing data in research networks currently exist. To build multivariate data analysis for centralized networks, transfer of patient-level data to a central computation resource is necessary. The authors implemented distributed multivariate models for federated networks in which patient-level data is kept at each site and data exchange policies are managed in a study-centric manner.Objective The objective was to implement infrastructure that supports the functionality of some existing research networks (e.g., cohort discovery, workflow management, and estimation of multivariate analytic models on centralized data) while adding additional important new features, such as algorithms for distributed iterative multivariate models, a graphical interface for multivariate model specification, synchronous and asynchronous response to network queries, investigator-initiated studies, and study-based control of staff, protocols, and data sharing policies.Materials and Methods Based on the requirements gathered from statisticians, administrators, and investigators from multiple institutions, the authors developed infrastructure and tools to support multisite comparative effectiveness studies using web services for multivariate statistical estimation in the SCANNER federated network.Results The authors implemented massively parallel (map-reduce) computation methods and a new policy management system to enable each study initiated by network participants to define the ways in which data may be processed, managed, queried, and shared. The authors illustrated the use of these systems among institutions with highly different policies and operating under different state laws.Discussion and Conclusion Federated research networks need not limit distributed query functionality to count queries, cohort discovery, or independently estimated analytic models. Multivariate analyses can be efficiently and securely conducted without patient-level data transport, allowing institutions with strict local data storage requirements to participate in sophisticated analyses based on federated research networks.

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A review of parallel processing for statistical computation

Cited by 13 publications

References 37 publications

Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

Parallelizing MCMC for Bayesian spatiotemporal geostatistical models

Parallel Statistical Computing for Statistical Inference

A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research

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