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Title: Accelerated Dimension-Independent Adaptive Metropolis

This work describes improvements from algorithmic and architectural means to black-box Bayesian inference over high-dimensional parameter spaces. The well-known adaptive Metropolis (AM) algorithm [33] is extended herein to scale asymptotically uniformly with respect to the underlying parameter dimension for Gaussian targets, by respecting the variance of the target. The resulting algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional (with dimension d 1000) targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justi ed a posteriori). Asymptotically in dimension, this GPU implementation exhibits a factor of four improvement versus a competitive CPU-based Intel MKL parallel version alone. Strong scaling to concurrent chains is exhibited, through a combination of longer time per sample batch (weak scaling) and yet fewer necessary samples to convergence. The algorithm performance is illustrated on several Gaussian and non-Gaussian target examples, in which the dimension may be in excess of one thousand.
Authors:
 [1] ;  [2] ;  [3] ;  [4]
  1. King Abdullah University of Science and Technology (KAUST), Thuwal (Saudi Arabia)
  2. Columbia Univ., New York, NY (United States)
  3. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  4. King Abdullah University of Science and Technology (KAUST), Thuwal, Kingdom of Saudi Arabia
Publication Date:
Grant/Contract Number:
AC05-00OR22725
Type:
Accepted Manuscript
Journal Name:
SIAM Journal on Scientific Computing
Additional Journal Information:
Journal Volume: 38; Journal Issue: 5; Journal ID: ISSN 1064-8275
Publisher:
SIAM
Research Org:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org:
USDOE
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING; Markov chain Monte Carlo; big data; Bayesian inference; adaptive Metropolis; Metropolis-Hastings; BLAS; GPU acceleration; High performance computing
OSTI Identifier:
1346642

Chen, Yuxin, Keyes, David E., Law, Kody J., and Ltaief, Hatem. Accelerated Dimension-Independent Adaptive Metropolis. United States: N. p., Web. doi:10.1137/15M1026432.
Chen, Yuxin, Keyes, David E., Law, Kody J., & Ltaief, Hatem. Accelerated Dimension-Independent Adaptive Metropolis. United States. doi:10.1137/15M1026432.
Chen, Yuxin, Keyes, David E., Law, Kody J., and Ltaief, Hatem. 2016. "Accelerated Dimension-Independent Adaptive Metropolis". United States. doi:10.1137/15M1026432. https://www.osti.gov/servlets/purl/1346642.
@article{osti_1346642,
title = {Accelerated Dimension-Independent Adaptive Metropolis},
author = {Chen, Yuxin and Keyes, David E. and Law, Kody J. and Ltaief, Hatem},
abstractNote = {This work describes improvements from algorithmic and architectural means to black-box Bayesian inference over high-dimensional parameter spaces. The well-known adaptive Metropolis (AM) algorithm [33] is extended herein to scale asymptotically uniformly with respect to the underlying parameter dimension for Gaussian targets, by respecting the variance of the target. The resulting algorithm, referred to as the dimension-independent adaptive Metropolis (DIAM) algorithm, also shows improved performance with respect to adaptive Metropolis on non-Gaussian targets. This algorithm is further improved, and the possibility of probing high-dimensional (with dimension d 1000) targets is enabled, via GPU-accelerated numerical libraries and periodically synchronized concurrent chains (justi ed a posteriori). Asymptotically in dimension, this GPU implementation exhibits a factor of four improvement versus a competitive CPU-based Intel MKL parallel version alone. Strong scaling to concurrent chains is exhibited, through a combination of longer time per sample batch (weak scaling) and yet fewer necessary samples to convergence. The algorithm performance is illustrated on several Gaussian and non-Gaussian target examples, in which the dimension may be in excess of one thousand.},
doi = {10.1137/15M1026432},
journal = {SIAM Journal on Scientific Computing},
number = 5,
volume = 38,
place = {United States},
year = {2016},
month = {10}
}