Using PLAPACK: parallel linear algebra package
Using PLAPACK: parallel linear algebra package
Parallel programming in OpenMP
Parallel programming in OpenMP
Supercomputing '96 Proceedings of the 1996 ACM/IEEE conference on Supercomputing
On MCMC sampling in hierarchical longitudinal models
Statistics and Computing
Information Theory, Inference & Learning Algorithms
Information Theory, Inference & Learning Algorithms
Parallel algorithms for Markov chain Monte Carlo methods in latent spatial Gaussian models
Statistics and Computing
Slice sampling for simulation based fitting of spatial data models
Statistics and Computing
Parallelizing MCMC for Bayesian spatiotemporal geostatistical models
Statistics and Computing
Parallel Computing Experiences with CUDA
IEEE Micro
Many-core algorithms for statistical phylogenetics
Bioinformatics
Accelerating Genome-Wide Association Studies Using CUDA Compatible Graphics Processing Units
IJCBS '09 Proceedings of the 2009 International Joint Conference on Bioinformatics, Systems Biology and Intelligent Computing
Massive Parallelization of Serial Inference Algorithms for a Complex Generalized Linear Model
ACM Transactions on Modeling and Computer Simulation (TOMACS) - Special Issue on Monte Carlo Methods in Statistics
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Slice sampling provides an easily implemented method for constructing a Markov chain Monte Carlo (MCMC) algorithm. However, slice sampling has two major drawbacks: (i) it requires repeated evaluation of likelihoods for each update, which can make it impractical when evaluations are expensive or as the number of evaluations grows (geometrically) with the dimension of the slice sampler, and (ii) since it can be challenging to construct multivariate updates, the updates are typically univariate, which often results in slow mixing samplers. We propose an approach to multivariate slice sampling that naturally lends itself to a parallel implementation. Our approach takes advantage of recent advances in computer architectures, for instance, the newest generation of graphics cards can execute roughly 30,000 threads simultaneously. We demonstrate that it is possible to construct a multivariate slice sampler that has good mixing properties and is efficient in terms of computing time. The contributions of this article are therefore twofold. We study approaches for constructing a multivariate slice sampler, and we show how parallel computing can be useful for making MCMC algorithms computationally efficient. We study various implementations of our algorithm in the context of real and simulated data.